[
  {
    "task": "science",
    "input": "Science Hard 275: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 371: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 220: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 23: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 863: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 503: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 840: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 983: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 668: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 804: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 644: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 43: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 736: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 707: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 40: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 17: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 563: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 148: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 966: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 654: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 856: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 525: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 841: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 730: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 984: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 226: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 118: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 383: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 119: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 229: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 100: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 181: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 611: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 320: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 873: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 720: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 136: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 714: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 75: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 771: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 85: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 593: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 767: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 827: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 180: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 571: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 260: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 744: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 243: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 799: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 653: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 390: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 187: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 787: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 929: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 450: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 603: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 297: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 586: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 672: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 518: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 632: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 552: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 277: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 972: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 158: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 268: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 434: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 578: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 734: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 774: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 414: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 625: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 535: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 257: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 548: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 98: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 483: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 117: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 463: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 18: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 843: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 459: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 615: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 876: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 380: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 470: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 132: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 405: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 555: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 996: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 741: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 775: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 379: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 594: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 384: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 678: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 108: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 539: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 627: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 97: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 141: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 337: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 838: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 846: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 109: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 115: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 42: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 943: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 618: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 444: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 219: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 126: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 159: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 16: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 872: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 425: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 549: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 733: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 20: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 59: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 322: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 692: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 299: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 440: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 419: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 447: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 699: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 581: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 213: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 990: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 583: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 203: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 93: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 205: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 792: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 883: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 270: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 106: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 133: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 643: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 807: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 999: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 4: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 394: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 519: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 717: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 980: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 710: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 619: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 94: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 330: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 343: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 577: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 302: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 985: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 647: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 58: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 895: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 783: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 602: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 694: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 789: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 927: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 179: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 865: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 565: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 241: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 396: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 913: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 452: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 246: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 613: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 960: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 839: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 204: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 769: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 567: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 814: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 890: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 821: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 389: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 864: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 288: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 556: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 709: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 828: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 259: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 342: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 835: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 239: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 240: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 551: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 671: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 193: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 274: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 991: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 822: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 67: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 690: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 171: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 436: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 232: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 360: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 738: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 737: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 612: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 905: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 925: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 606: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 228: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 635: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 99: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 939: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 471: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 823: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 748: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 854: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 413: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 819: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 418: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 608: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 682: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 472: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 626: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 86: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 633: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 988: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 902: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 561: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 961: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 392: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 808: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 172: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 328: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 35: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 62: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 200: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 857: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 899: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 230: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 718: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 215: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 889: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 101: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 57: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 48: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 780: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 541: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 847: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 580: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 61: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 325: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 568: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 92: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 800: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 303: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 522: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 882: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 216: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 562: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 398: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 244: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 486: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 637: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 125: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 234: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 537: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 402: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 870: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 233: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 785: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 515: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 8: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 137: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 476: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 377: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 532: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 957: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 311: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 875: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 813: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 19: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 636: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 845: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 623: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 289: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 235: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 952: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 443: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 386: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 262: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 317: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 154: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 373: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 285: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 558: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 688: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 958: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 80: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 506: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 293: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 729: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 937: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 91: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 747: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 592: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 934: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 192: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 156: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 446: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 596: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 369: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 659: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 956: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 834: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 194: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 886: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 836: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 366: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 438: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 703: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 64: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 142: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 994: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 777: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 350: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 997: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 166: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 135: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 491: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 860: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 231: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 30: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 669: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 488: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 697: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 693: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 12: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 723: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 732: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 178: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 763: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 795: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 248: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 529: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 345: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 495: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 174: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 334: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 263: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 887: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 295: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 721: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 214: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 1: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 358: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 560: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 127: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 530: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 56: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 786: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 764: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 359: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 620: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 705: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 462: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 39: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 762: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 468: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 557: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 95: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 610: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 221: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 110: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 105: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 66: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 490: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 604: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 674: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 329: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 306: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 457: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 423: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 797: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 992: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 206: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 177: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 74: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 26: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 809: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 492: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 930: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 185: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 554: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 559: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 161: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 657: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 346: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 424: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 954: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 381: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 433: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 49: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 489: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 227: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 218: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 879: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 485: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 455: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 466: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 782: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 482: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 426: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 333: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 684: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 746: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 344: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 861: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 340: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 675: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 393: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 170: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 339: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 706: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 368: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 977: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 112: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 616: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 116: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 971: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 892: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 974: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 781: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 538: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 55: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 526: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 640: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 498: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 412: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 874: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 724: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 791: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 597: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 65: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 155: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 157: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 662: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 989: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 10: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 759: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 407: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 998: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 794: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 679: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 103: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 323: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 881: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 869: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 77: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 499: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 46: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 936: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 411: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 365: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 916: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 901: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 917: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 362: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 307: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 716: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 304: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 223: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 292: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 955: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 749: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 113: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 480: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 517: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 310: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 754: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 711: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 677: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 908: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 833: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 173: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 681: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 409: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 376: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 630: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 858: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 445: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 84: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 150: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 600: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 312: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 830: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 570: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 576: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 920: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 646: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 315: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 309: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 96: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 2: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 143: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 107: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 922: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 451: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 401: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 327: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 967: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 162: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 253: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 89: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 926: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 914: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 946: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 605: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 493: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 484: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 251: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 624: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 332: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 336: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 528: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 53: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 210: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 224: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 935: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 607: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 428: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 689: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 931: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 938: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 793: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 904: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 477: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 587: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 7: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 287: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 195: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 169: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 37: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 585: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 437: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 0: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 354: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 805: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 850: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 508: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 513: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 622: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 448: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 238: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 258: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 547: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 284: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 199: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 505: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 114: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 460: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 269: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 82: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 290: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 32: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 441: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 6: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 963: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 168: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 523: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 521: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 254: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 968: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 953: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 279: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 465: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 598: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 761: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 502: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 642: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 753: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 391: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 76: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 656: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 250: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 38: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 481: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 742: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 655: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 950: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 375: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 629: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 573: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 139: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 167: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 44: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 145: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 658: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 255: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 70: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 673: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 47: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 590: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 531: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 298: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 264: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 454: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 291: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 867: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 756: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 801: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 153: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 676: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 752: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 184: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 978: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 410: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 877: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 566: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 429: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 316: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 400: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 766: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 903: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 237: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 399: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 842: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 245: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 951: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 981: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 698: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 296: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 536: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 353: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 708: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 207: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 201: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 196: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 816: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 500: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 182: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 726: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 862: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 50: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 844: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 324: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 417: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 599: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 871: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 435: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 120: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 831: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 866: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 501: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 129: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 51: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 868: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 609: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 474: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 52: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 408: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 896: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 772: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 134: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 921: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 123: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 894: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 731: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 198: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 820: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 664: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 906: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 685: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 575: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 940: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 516: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 540: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 467: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 591: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 973: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 507: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 745: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 691: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 475: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 31: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 687: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 36: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 88: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 979: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 546: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 617: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 803: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 387: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 550: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 176: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 79: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 144: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 208: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 72: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 421: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 909: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 225: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 696: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 924: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 811: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 397: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 832: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 464: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 261: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 160: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 63: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 3: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 740: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 430: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 385: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 855: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 798: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 197: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 579: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 527: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 825: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 965: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 278: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 280: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 432: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 545: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 352: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 601: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 461: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 29: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 217: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 131: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 796: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 361: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 910: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 969: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 81: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 372: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 888: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 175: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 403: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 695: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 209: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 267: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 5: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 945: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 90: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 993: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 14: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 829: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 959: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 652: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 663: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 784: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 183: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 188: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 750: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 41: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 130: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 982: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 765: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 265: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 727: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 439: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 321: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 331: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 356: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 27: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 104: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 301: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 313: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 286: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 670: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 665: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 660: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 713: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 650: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 406: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 211: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 806: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 378: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 712: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 818: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 190: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 614: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 776: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 28: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 314: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 837: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 884: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 456: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 941: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 728: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 770: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 15: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 544: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 494: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 919: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 33: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 621: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 768: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 739: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 648: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 242: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 735: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 509: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 751: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 918: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 140: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 667: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 553: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 128: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 824: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  },
  {
    "task": "science",
    "input": "Science Hard 564: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
    "output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)*4, comp = (k+v)*prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
    "type": "agentic",
    "focus": "science",
    "difficulty": "hard"
  }
]