Datasets:

deeprcurs
/

IKNN-Rl1-Dataset-Science

	[
	{
	"task": "science",
	"input": "Science Hard 83: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
	"output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)4, comp = (k+v)prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
	"type": "agentic",
	"focus": "science",
	"difficulty": "hard"
	},
	{
	"task": "science",
	"input": "Science Hard 639: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
	"output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)4, comp = (k+v)prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
	"type": "agentic",
	"focus": "science",
	"difficulty": "hard"
	},
	{
	"task": "science",
	"input": "Science Hard 880: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
	"output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)4, comp = (k+v)prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
	"type": "agentic",
	"focus": "science",
	"difficulty": "hard"
	},
	{
	"task": "science",
	"input": "Science Hard 256: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
	"output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)4, comp = (k+v)prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
	"type": "agentic",
	"focus": "science",
	"difficulty": "hard"
	},
	{
	"task": "science",
	"input": "Science Hard 22: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
	"output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)4, comp = (k+v)prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
	"type": "agentic",
	"focus": "science",
	"difficulty": "hard"
	},
	{
	"task": "science",
	"input": "Science Hard 572: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
	"output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)4, comp = (k+v)prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
	"type": "agentic",
	"focus": "science",
	"difficulty": "hard"
	},
	{
	"task": "science",
	"input": "Science Hard 970: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
	"output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)4, comp = (k+v)prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
	"type": "agentic",
	"focus": "science",
	"difficulty": "hard"
	},
	{
	"task": "science",
	"input": "Science Hard 294: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
	"output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)4, comp = (k+v)prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
	"type": "agentic",
	"focus": "science",
	"difficulty": "hard"
	},
	{
	"task": "science",
	"input": "Science Hard 69: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
	"output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)4, comp = (k+v)prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
	"type": "agentic",
	"focus": "science",
	"difficulty": "hard"
	},
	{
	"task": "science",
	"input": "Science Hard 382: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
	"output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)4, comp = (k+v)prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
	"type": "agentic",
	"focus": "science",
	"difficulty": "hard"
	},
	{
	"task": "science",
	"input": "Science Hard 510: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
	"output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)4, comp = (k+v)prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
	"type": "agentic",
	"focus": "science",
	"difficulty": "hard"
	},
	{
	"task": "science",
	"input": "Science Hard 651: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
	"output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)4, comp = (k+v)prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
	"type": "agentic",
	"focus": "science",
	"difficulty": "hard"
	},
	{
	"task": "science",
	"input": "Science Hard 645: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
	"output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)4, comp = (k+v)prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
	"type": "agentic",
	"focus": "science",
	"difficulty": "hard"
	},
	{
	"task": "science",
	"input": "Science Hard 975: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
	"output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)4, comp = (k+v)prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
	"type": "agentic",
	"focus": "science",
	"difficulty": "hard"
	},
	{
	"task": "science",
	"input": "Science Hard 589: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
	"output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)4, comp = (k+v)prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
	"type": "agentic",
	"focus": "science",
	"difficulty": "hard"
	},
	{
	"task": "science",
	"input": "Science Hard 122: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
	"output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)4, comp = (k+v)prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
	"type": "agentic",
	"focus": "science",
	"difficulty": "hard"
	},
	{
	"task": "science",
	"input": "Science Hard 826: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
	"output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)4, comp = (k+v)prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
	"type": "agentic",
	"focus": "science",
	"difficulty": "hard"
	},
	{
	"task": "science",
	"input": "Science Hard 666: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
	"output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)4, comp = (k+v)prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
	"type": "agentic",
	"focus": "science",
	"difficulty": "hard"
	},
	{
	"task": "science",
	"input": "Science Hard 222: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
	"output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)4, comp = (k+v)prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
	"type": "agentic",
	"focus": "science",
	"difficulty": "hard"
	},
	{
	"task": "science",
	"input": "Science Hard 569: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
	"output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)4, comp = (k+v)prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
	"type": "agentic",
	"focus": "science",
	"difficulty": "hard"
	},
	{
	"task": "science",
	"input": "Science Hard 449: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
	"output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)4, comp = (k+v)prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
	"type": "agentic",
	"focus": "science",
	"difficulty": "hard"
	},
	{
	"task": "science",
	"input": "Science Hard 907: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
	"output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)4, comp = (k+v)prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
	"type": "agentic",
	"focus": "science",
	"difficulty": "hard"
	},
	{
	"task": "science",
	"input": "Science Hard 704: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
	"output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)4, comp = (k+v)prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
	"type": "agentic",
	"focus": "science",
	"difficulty": "hard"
	},
	{
	"task": "science",
	"input": "Science Hard 431: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
	"output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)4, comp = (k+v)prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
	"type": "agentic",
	"focus": "science",
	"difficulty": "hard"
	},
	{
	"task": "science",
	"input": "Science Hard 364: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
	"output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)4, comp = (k+v)prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
	"type": "agentic",
	"focus": "science",
	"difficulty": "hard"
	},
	{
	"task": "science",
	"input": "Science Hard 849: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
	"output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)4, comp = (k+v)prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
	"type": "agentic",
	"focus": "science",
	"difficulty": "hard"
	},
	{
	"task": "science",
	"input": "Science Hard 21: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
	"output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)4, comp = (k+v)prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
	"type": "agentic",
	"focus": "science",
	"difficulty": "hard"
	},
	{
	"task": "science",
	"input": "Science Hard 790: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
	"output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)4, comp = (k+v)prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
	"type": "agentic",
	"focus": "science",
	"difficulty": "hard"
	},
	{
	"task": "science",
	"input": "Science Hard 355: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
	"output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)4, comp = (k+v)prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
	"type": "agentic",
	"focus": "science",
	"difficulty": "hard"
	},
	{
	"task": "science",
	"input": "Science Hard 318: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
	"output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)4, comp = (k+v)prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
	"type": "agentic",
	"focus": "science",
	"difficulty": "hard"
	},
	{
	"task": "science",
	"input": "Science Hard 661: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
	"output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)4, comp = (k+v)prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
	"type": "agentic",
	"focus": "science",
	"difficulty": "hard"
	},
	{
	"task": "science",
	"input": "Science Hard 273: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
	"output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)4, comp = (k+v)prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
	"type": "agentic",
	"focus": "science",
	"difficulty": "hard"
	},
	{
	"task": "science",
	"input": "Science Hard 520: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
	"output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)4, comp = (k+v)prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
	"type": "agentic",
	"focus": "science",
	"difficulty": "hard"
	},
	{
	"task": "science",
	"input": "Science Hard 986: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
	"output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)4, comp = (k+v)prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
	"type": "agentic",
	"focus": "science",
	"difficulty": "hard"
	},
	{
	"task": "science",
	"input": "Science Hard 584: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
	"output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)4, comp = (k+v)prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
	"type": "agentic",
	"focus": "science",
	"difficulty": "hard"
	},
	{
	"task": "science",
	"input": "Science Hard 347: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
	"output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)4, comp = (k+v)prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
	"type": "agentic",
	"focus": "science",
	"difficulty": "hard"
	},
	{
	"task": "science",
	"input": "Science Hard 236: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
	"output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)4, comp = (k+v)prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
	"type": "agentic",
	"focus": "science",
	"difficulty": "hard"
	},
	{
	"task": "science",
	"input": "Science Hard 427: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
	"output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)4, comp = (k+v)prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
	"type": "agentic",
	"focus": "science",
	"difficulty": "hard"
	},
	{
	"task": "science",
	"input": "Science Hard 13: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
	"output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)4, comp = (k+v)prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
	"type": "agentic",
	"focus": "science",
	"difficulty": "hard"
	},
	{
	"task": "science",
	"input": "Science Hard 266: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
	"output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)4, comp = (k+v)prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
	"type": "agentic",
	"focus": "science",
	"difficulty": "hard"
	},
	{
	"task": "science",
	"input": "Science Hard 641: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
	"output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)4, comp = (k+v)prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
	"type": "agentic",
	"focus": "science",
	"difficulty": "hard"
	},
	{
	"task": "science",
	"input": "Science Hard 719: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
	"output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)4, comp = (k+v)prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
	"type": "agentic",
	"focus": "science",
	"difficulty": "hard"
	},
	{
	"task": "science",
	"input": "Science Hard 71: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
	"output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)4, comp = (k+v)prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
	"type": "agentic",
	"focus": "science",
	"difficulty": "hard"
	},
	{
	"task": "science",
	"input": "Science Hard 851: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
	"output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)4, comp = (k+v)prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
	"type": "agentic",
	"focus": "science",
	"difficulty": "hard"
	},
	{
	"task": "science",
	"input": "Science Hard 686: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
	"output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)4, comp = (k+v)prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
	"type": "agentic",
	"focus": "science",
	"difficulty": "hard"
	},
	{
	"task": "science",
	"input": "Science Hard 442: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
	"output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)4, comp = (k+v)prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
	"type": "agentic",
	"focus": "science",
	"difficulty": "hard"
	},
	{
	"task": "science",
	"input": "Science Hard 504: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
	"output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)4, comp = (k+v)prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
	"type": "agentic",
	"focus": "science",
	"difficulty": "hard"
	},
	{
	"task": "science",
	"input": "Science Hard 305: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
	"output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)4, comp = (k+v)prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
	"type": "agentic",
	"focus": "science",
	"difficulty": "hard"
	},
	{
	"task": "science",
	"input": "Science Hard 582: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
	"output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)4, comp = (k+v)prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
	"type": "agentic",
	"focus": "science",
	"difficulty": "hard"
	},
	{
	"task": "science",
	"input": "Science Hard 543: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
	"output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)4, comp = (k+v)prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
	"type": "agentic",
	"focus": "science",
	"difficulty": "hard"
	},
	{
	"task": "science",
	"input": "Science Hard 758: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
	"output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)4, comp = (k+v)prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
	"type": "agentic",
	"focus": "science",
	"difficulty": "hard"
	},
	{
	"task": "science",
	"input": "Science Hard 60: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
	"output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)4, comp = (k+v)prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
	"type": "agentic",
	"focus": "science",
	"difficulty": "hard"
	},
	{
	"task": "science",
	"input": "Science Hard 25: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
	"output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)4, comp = (k+v)prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
	"type": "agentic",
	"focus": "science",
	"difficulty": "hard"
	},
	{
	"task": "science",
	"input": "Science Hard 9: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
	"output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)4, comp = (k+v)prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
	"type": "agentic",
	"focus": "science",
	"difficulty": "hard"
	},
	{
	"task": "science",
	"input": "Science Hard 595: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
	"output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)4, comp = (k+v)prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
	"type": "agentic",
	"focus": "science",
	"difficulty": "hard"
	},
	{
	"task": "science",
	"input": "Science Hard 928: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
	"output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)4, comp = (k+v)prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
	"type": "agentic",
	"focus": "science",
	"difficulty": "hard"
	},
	{
	"task": "science",
	"input": "Science Hard 634: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
	"output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)4, comp = (k+v)prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
	"type": "agentic",
	"focus": "science",
	"difficulty": "hard"
	},
	{
	"task": "science",
	"input": "Science Hard 511: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
	"output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)4, comp = (k+v)prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
	"type": "agentic",
	"focus": "science",
	"difficulty": "hard"
	},
	{
	"task": "science",
	"input": "Science Hard 149: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
	"output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)4, comp = (k+v)prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
	"type": "agentic",
	"focus": "science",
	"difficulty": "hard"
	},
	{
	"task": "science",
	"input": "Science Hard 944: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
	"output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)4, comp = (k+v)prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
	"type": "agentic",
	"focus": "science",
	"difficulty": "hard"
	},
	{
	"task": "science",
	"input": "Science Hard 911: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
	"output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)4, comp = (k+v)prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
	"type": "agentic",
	"focus": "science",
	"difficulty": "hard"
	},
	{
	"task": "science",
	"input": "Science Hard 189: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
	"output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)4, comp = (k+v)prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
	"type": "agentic",
	"focus": "science",
	"difficulty": "hard"
	},
	{
	"task": "science",
	"input": "Science Hard 987: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
	"output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)4, comp = (k+v)prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
	"type": "agentic",
	"focus": "science",
	"difficulty": "hard"
	},
	{
	"task": "science",
	"input": "Science Hard 300: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
	"output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)4, comp = (k+v)prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
	"type": "agentic",
	"focus": "science",
	"difficulty": "hard"
	},
	{
	"task": "science",
	"input": "Science Hard 755: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
	"output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)4, comp = (k+v)prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
	"type": "agentic",
	"focus": "science",
	"difficulty": "hard"
	},
	{
	"task": "science",
	"input": "Science Hard 338: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
	"output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)4, comp = (k+v)prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
	"type": "agentic",
	"focus": "science",
	"difficulty": "hard"
	},
	{
	"task": "science",
	"input": "Science Hard 524: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
	"output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)4, comp = (k+v)prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
	"type": "agentic",
	"focus": "science",
	"difficulty": "hard"
	},
	{
	"task": "science",
	"input": "Science Hard 370: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
	"output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)4, comp = (k+v)prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
	"type": "agentic",
	"focus": "science",
	"difficulty": "hard"
	},
	{
	"task": "science",
	"input": "Science Hard 893: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
	"output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)4, comp = (k+v)prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
	"type": "agentic",
	"focus": "science",
	"difficulty": "hard"
	},
	{
	"task": "science",
	"input": "Science Hard 496: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
	"output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)4, comp = (k+v)prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
	"type": "agentic",
	"focus": "science",
	"difficulty": "hard"
	},
	{
	"task": "science",
	"input": "Science Hard 878: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
	"output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)4, comp = (k+v)prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
	"type": "agentic",
	"focus": "science",
	"difficulty": "hard"
	},
	{
	"task": "science",
	"input": "Science Hard 247: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
	"output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)4, comp = (k+v)prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
	"type": "agentic",
	"focus": "science",
	"difficulty": "hard"
	},
	{
	"task": "science",
	"input": "Science Hard 388: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
	"output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)4, comp = (k+v)prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
	"type": "agentic",
	"focus": "science",
	"difficulty": "hard"
	},
	{
	"task": "science",
	"input": "Science Hard 701: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
	"output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)4, comp = (k+v)prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
	"type": "agentic",
	"focus": "science",
	"difficulty": "hard"
	},
	{
	"task": "science",
	"input": "Science Hard 649: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
	"output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)4, comp = (k+v)prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
	"type": "agentic",
	"focus": "science",
	"difficulty": "hard"
	},
	{
	"task": "science",
	"input": "Science Hard 812: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
	"output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)4, comp = (k+v)prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
	"type": "agentic",
	"focus": "science",
	"difficulty": "hard"
	},
	{
	"task": "science",
	"input": "Science Hard 271: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
	"output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)4, comp = (k+v)prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
	"type": "agentic",
	"focus": "science",
	"difficulty": "hard"
	},
	{
	"task": "science",
	"input": "Science Hard 725: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
	"output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)4, comp = (k+v)prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
	"type": "agentic",
	"focus": "science",
	"difficulty": "hard"
	},
	{
	"task": "science",
	"input": "Science Hard 374: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
	"output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)4, comp = (k+v)prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
	"type": "agentic",
	"focus": "science",
	"difficulty": "hard"
	},
	{
	"task": "science",
	"input": "Science Hard 760: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
	"output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)4, comp = (k+v)prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
	"type": "agentic",
	"focus": "science",
	"difficulty": "hard"
	},
	{
	"task": "science",
	"input": "Science Hard 487: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
	"output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)4, comp = (k+v)prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
	"type": "agentic",
	"focus": "science",
	"difficulty": "hard"
	},
	{
	"task": "science",
	"input": "Science Hard 212: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
	"output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)4, comp = (k+v)prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
	"type": "agentic",
	"focus": "science",
	"difficulty": "hard"
	},
	{
	"task": "science",
	"input": "Science Hard 319: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
	"output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)4, comp = (k+v)prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
	"type": "agentic",
	"focus": "science",
	"difficulty": "hard"
	},
	{
	"task": "science",
	"input": "Science Hard 743: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
	"output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)4, comp = (k+v)prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
	"type": "agentic",
	"focus": "science",
	"difficulty": "hard"
	},
	{
	"task": "science",
	"input": "Science Hard 773: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
	"output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)4, comp = (k+v)prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
	"type": "agentic",
	"focus": "science",
	"difficulty": "hard"
	},
	{
	"task": "science",
	"input": "Science Hard 700: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
	"output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)4, comp = (k+v)prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
	"type": "agentic",
	"focus": "science",
	"difficulty": "hard"
	},
	{
	"task": "science",
	"input": "Science Hard 702: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
	"output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)4, comp = (k+v)prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
	"type": "agentic",
	"focus": "science",
	"difficulty": "hard"
	},
	{
	"task": "science",
	"input": "Science Hard 276: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
	"output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)4, comp = (k+v)prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
	"type": "agentic",
	"focus": "science",
	"difficulty": "hard"
	},
	{
	"task": "science",
	"input": "Science Hard 326: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
	"output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)4, comp = (k+v)prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
	"type": "agentic",
	"focus": "science",
	"difficulty": "hard"
	},
	{
	"task": "science",
	"input": "Science Hard 942: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
	"output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)4, comp = (k+v)prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
	"type": "agentic",
	"focus": "science",
	"difficulty": "hard"
	},
	{
	"task": "science",
	"input": "Science Hard 349: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
	"output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)4, comp = (k+v)prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
	"type": "agentic",
	"focus": "science",
	"difficulty": "hard"
	},
	{
	"task": "science",
	"input": "Science Hard 164: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
	"output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)4, comp = (k+v)prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
	"type": "agentic",
	"focus": "science",
	"difficulty": "hard"
	},
	{
	"task": "science",
	"input": "Science Hard 335: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
	"output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)4, comp = (k+v)prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
	"type": "agentic",
	"focus": "science",
	"difficulty": "hard"
	},
	{
	"task": "science",
	"input": "Science Hard 395: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
	"output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)4, comp = (k+v)prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
	"type": "agentic",
	"focus": "science",
	"difficulty": "hard"
	},
	{
	"task": "science",
	"input": "Science Hard 628: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
	"output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)4, comp = (k+v)prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
	"type": "agentic",
	"focus": "science",
	"difficulty": "hard"
	},
	{
	"task": "science",
	"input": "Science Hard 420: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
	"output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)4, comp = (k+v)prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
	"type": "agentic",
	"focus": "science",
	"difficulty": "hard"
	},
	{
	"task": "science",
	"input": "Science Hard 948: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
	"output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)4, comp = (k+v)prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
	"type": "agentic",
	"focus": "science",
	"difficulty": "hard"
	},
	{
	"task": "science",
	"input": "Science Hard 34: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
	"output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)4, comp = (k+v)prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
	"type": "agentic",
	"focus": "science",
	"difficulty": "hard"
	},
	{
	"task": "science",
	"input": "Science Hard 514: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
	"output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)4, comp = (k+v)prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
	"type": "agentic",
	"focus": "science",
	"difficulty": "hard"
	},
	{
	"task": "science",
	"input": "Science Hard 715: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
	"output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)4, comp = (k+v)prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
	"type": "agentic",
	"focus": "science",
	"difficulty": "hard"
	},
	{
	"task": "science",
	"input": "Science Hard 542: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
	"output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)4, comp = (k+v)prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
	"type": "agentic",
	"focus": "science",
	"difficulty": "hard"
	},
	{
	"task": "science",
	"input": "Science Hard 848: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
	"output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)4, comp = (k+v)prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
	"type": "agentic",
	"focus": "science",
	"difficulty": "hard"
	},
	{
	"task": "science",
	"input": "Science Hard 367: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
	"output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)4, comp = (k+v)prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
	"type": "agentic",
	"focus": "science",
	"difficulty": "hard"
	},
	{
	"task": "science",
	"input": "Science Hard 995: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
	"output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)4, comp = (k+v)prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
	"type": "agentic",
	"focus": "science",
	"difficulty": "hard"
	},
	{
	"task": "science",
	"input": "Science Hard 151: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
	"output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)4, comp = (k+v)prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
	"type": "agentic",
	"focus": "science",
	"difficulty": "hard"
	},
	{
	"task": "science",
	"input": "Science Hard 283: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
	"output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)4, comp = (k+v)prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
	"type": "agentic",
	"focus": "science",
	"difficulty": "hard"
	},
	{
	"task": "science",
	"input": "Science Hard 852: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
	"output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)4, comp = (k+v)prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
	"type": "agentic",
	"focus": "science",
	"difficulty": "hard"
	},
	{
	"task": "science",
	"input": "Science Hard 512: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
	"output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)4, comp = (k+v)prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
	"type": "agentic",
	"focus": "science",
	"difficulty": "hard"
	},
	{
	"task": "science",
	"input": "Science Hard 272: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
	"output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)4, comp = (k+v)prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
	"type": "agentic",
	"focus": "science",
	"difficulty": "hard"
	},
	{
	"task": "science",
	"input": "Science Hard 900: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
	"output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)4, comp = (k+v)prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
	"type": "agentic",
	"focus": "science",
	"difficulty": "hard"
	},
	{
	"task": "science",
	"input": "Science Hard 802: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
	"output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)4, comp = (k+v)prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
	"type": "agentic",
	"focus": "science",
	"difficulty": "hard"
	},
	{
	"task": "science",
	"input": "Science Hard 45: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
	"output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)4, comp = (k+v)prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
	"type": "agentic",
	"focus": "science",
	"difficulty": "hard"
	},
	{
	"task": "science",
	"input": "Science Hard 815: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
	"output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)4, comp = (k+v)prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
	"type": "agentic",
	"focus": "science",
	"difficulty": "hard"
	},
	{
	"task": "science",
	"input": "Science Hard 415: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
	"output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)4, comp = (k+v)prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
	"type": "agentic",
	"focus": "science",
	"difficulty": "hard"
	},
	{
	"task": "science",
	"input": "Science Hard 186: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
	"output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)4, comp = (k+v)prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
	"type": "agentic",
	"focus": "science",
	"difficulty": "hard"
	},
	{
	"task": "science",
	"input": "Science Hard 121: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
	"output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)4, comp = (k+v)prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
	"type": "agentic",
	"focus": "science",
	"difficulty": "hard"
	},
	{
	"task": "science",
	"input": "Science Hard 138: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
	"output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)4, comp = (k+v)prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
	"type": "agentic",
	"focus": "science",
	"difficulty": "hard"
	},
	{
	"task": "science",
	"input": "Science Hard 932: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
	"output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)4, comp = (k+v)prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
	"type": "agentic",
	"focus": "science",
	"difficulty": "hard"
	},
	{
	"task": "science",
	"input": "Science Hard 348: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
	"output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)4, comp = (k+v)prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
	"type": "agentic",
	"focus": "science",
	"difficulty": "hard"
	},
	{
	"task": "science",
	"input": "Science Hard 757: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
	"output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)4, comp = (k+v)prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
	"type": "agentic",
	"focus": "science",
	"difficulty": "hard"
	},
	{
	"task": "science",
	"input": "Science Hard 788: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
	"output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)4, comp = (k+v)prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
	"type": "agentic",
	"focus": "science",
	"difficulty": "hard"
	},
	{
	"task": "science",
	"input": "Science Hard 252: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
	"output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)4, comp = (k+v)prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
	"type": "agentic",
	"focus": "science",
	"difficulty": "hard"
	},
	{
	"task": "science",
	"input": "Science Hard 469: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
	"output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)4, comp = (k+v)prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
	"type": "agentic",
	"focus": "science",
	"difficulty": "hard"
	},
	{
	"task": "science",
	"input": "Science Hard 453: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
	"output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)4, comp = (k+v)prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
	"type": "agentic",
	"focus": "science",
	"difficulty": "hard"
	},
	{
	"task": "science",
	"input": "Science Hard 964: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
	"output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)4, comp = (k+v)prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
	"type": "agentic",
	"focus": "science",
	"difficulty": "hard"
	},
	{
	"task": "science",
	"input": "Science Hard 281: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
	"output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)4, comp = (k+v)prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
	"type": "agentic",
	"focus": "science",
	"difficulty": "hard"
	},
	{
	"task": "science",
	"input": "Science Hard 588: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
	"output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)4, comp = (k+v)prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
	"type": "agentic",
	"focus": "science",
	"difficulty": "hard"
	},
	{
	"task": "science",
	"input": "Science Hard 779: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
	"output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)4, comp = (k+v)prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
	"type": "agentic",
	"focus": "science",
	"difficulty": "hard"
	},
	{
	"task": "science",
	"input": "Science Hard 308: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
	"output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)4, comp = (k+v)prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
	"type": "agentic",
	"focus": "science",
	"difficulty": "hard"
	},
	{
	"task": "science",
	"input": "Science Hard 147: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
	"output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)4, comp = (k+v)prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
	"type": "agentic",
	"focus": "science",
	"difficulty": "hard"
	},
	{
	"task": "science",
	"input": "Science Hard 891: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
	"output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)4, comp = (k+v)prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
	"type": "agentic",
	"focus": "science",
	"difficulty": "hard"
	},
	{
	"task": "science",
	"input": "Science Hard 473: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
	"output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)4, comp = (k+v)prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
	"type": "agentic",
	"focus": "science",
	"difficulty": "hard"
	},
	{
	"task": "science",
	"input": "Science Hard 853: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
	"output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)4, comp = (k+v)prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
	"type": "agentic",
	"focus": "science",
	"difficulty": "hard"
	},
	{
	"task": "science",
	"input": "Science Hard 859: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
	"output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)4, comp = (k+v)prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
	"type": "agentic",
	"focus": "science",
	"difficulty": "hard"
	},
	{
	"task": "science",
	"input": "Science Hard 976: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
	"output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)4, comp = (k+v)prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
	"type": "agentic",
	"focus": "science",
	"difficulty": "hard"
	},
	{
	"task": "science",
	"input": "Science Hard 885: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
	"output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)4, comp = (k+v)prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
	"type": "agentic",
	"focus": "science",
	"difficulty": "hard"
	},
	{
	"task": "science",
	"input": "Science Hard 146: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
	"output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)4, comp = (k+v)prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
	"type": "agentic",
	"focus": "science",
	"difficulty": "hard"
	},
	{
	"task": "science",
	"input": "Science Hard 124: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
	"output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)4, comp = (k+v)prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
	"type": "agentic",
	"focus": "science",
	"difficulty": "hard"
	},
	{
	"task": "science",
	"input": "Science Hard 282: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
	"output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)4, comp = (k+v)prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
	"type": "agentic",
	"focus": "science",
	"difficulty": "hard"
	},
	{
	"task": "science",
	"input": "Science Hard 638: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
	"output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)4, comp = (k+v)prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
	"type": "agentic",
	"focus": "science",
	"difficulty": "hard"
	},
	{
	"task": "science",
	"input": "Science Hard 631: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
	"output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)4, comp = (k+v)prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
	"type": "agentic",
	"focus": "science",
	"difficulty": "hard"
	},
	{
	"task": "science",
	"input": "Science Hard 947: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
	"output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)4, comp = (k+v)prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
	"type": "agentic",
	"focus": "science",
	"difficulty": "hard"
	},
	{
	"task": "science",
	"input": "Science Hard 479: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
	"output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)4, comp = (k+v)prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
	"type": "agentic",
	"focus": "science",
	"difficulty": "hard"
	},
	{
	"task": "science",
	"input": "Science Hard 722: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
	"output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)4, comp = (k+v)prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
	"type": "agentic",
	"focus": "science",
	"difficulty": "hard"
	},
	{
	"task": "science",
	"input": "Science Hard 163: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
	"output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)4, comp = (k+v)prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
	"type": "agentic",
	"focus": "science",
	"difficulty": "hard"
	},
	{
	"task": "science",
	"input": "Science Hard 102: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
	"output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)4, comp = (k+v)prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
	"type": "agentic",
	"focus": "science",
	"difficulty": "hard"
	},
	{
	"task": "science",
	"input": "Science Hard 87: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
	"output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)4, comp = (k+v)prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
	"type": "agentic",
	"focus": "science",
	"difficulty": "hard"
	},
	{
	"task": "science",
	"input": "Science Hard 68: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
	"output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)4, comp = (k+v)prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
	"type": "agentic",
	"focus": "science",
	"difficulty": "hard"
	},
	{
	"task": "science",
	"input": "Science Hard 416: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
	"output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)4, comp = (k+v)prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
	"type": "agentic",
	"focus": "science",
	"difficulty": "hard"
	},
	{
	"task": "science",
	"input": "Science Hard 923: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
	"output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)4, comp = (k+v)prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
	"type": "agentic",
	"focus": "science",
	"difficulty": "hard"
	},
	{
	"task": "science",
	"input": "Science Hard 897: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
	"output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)4, comp = (k+v)prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
	"type": "agentic",
	"focus": "science",
	"difficulty": "hard"
	},
	{
	"task": "science",
	"input": "Science Hard 152: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
	"output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)4, comp = (k+v)prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
	"type": "agentic",
	"focus": "science",
	"difficulty": "hard"
	},
	{
	"task": "science",
	"input": "Science Hard 404: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
	"output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)4, comp = (k+v)prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
	"type": "agentic",
	"focus": "science",
	"difficulty": "hard"
	},
	{
	"task": "science",
	"input": "Science Hard 11: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
	"output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)4, comp = (k+v)prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
	"type": "agentic",
	"focus": "science",
	"difficulty": "hard"
	},
	{
	"task": "science",
	"input": "Science Hard 54: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
	"output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)4, comp = (k+v)prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
	"type": "agentic",
	"focus": "science",
	"difficulty": "hard"
	},
	{
	"task": "science",
	"input": "Science Hard 778: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
	"output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)4, comp = (k+v)prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
	"type": "agentic",
	"focus": "science",
	"difficulty": "hard"
	},
	{
	"task": "science",
	"input": "Science Hard 497: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
	"output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)4, comp = (k+v)prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
	"type": "agentic",
	"focus": "science",
	"difficulty": "hard"
	},
	{
	"task": "science",
	"input": "Science Hard 357: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
	"output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)4, comp = (k+v)prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
	"type": "agentic",
	"focus": "science",
	"difficulty": "hard"
	},
	{
	"task": "science",
	"input": "Science Hard 933: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
	"output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)4, comp = (k+v)prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
	"type": "agentic",
	"focus": "science",
	"difficulty": "hard"
	},
	{
	"task": "science",
	"input": "Science Hard 683: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
	"output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)4, comp = (k+v)prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
	"type": "agentic",
	"focus": "science",
	"difficulty": "hard"
	},
	{
	"task": "science",
	"input": "Science Hard 458: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
	"output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)4, comp = (k+v)prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
	"type": "agentic",
	"focus": "science",
	"difficulty": "hard"
	},
	{
	"task": "science",
	"input": "Science Hard 680: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
	"output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)4, comp = (k+v)prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
	"type": "agentic",
	"focus": "science",
	"difficulty": "hard"
	},
	{
	"task": "science",
	"input": "Science Hard 533: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
	"output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)4, comp = (k+v)prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
	"type": "agentic",
	"focus": "science",
	"difficulty": "hard"
	},
	{
	"task": "science",
	"input": "Science Hard 249: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
	"output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)4, comp = (k+v)prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
	"type": "agentic",
	"focus": "science",
	"difficulty": "hard"
	},
	{
	"task": "science",
	"input": "Science Hard 817: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
	"output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)4, comp = (k+v)prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
	"type": "agentic",
	"focus": "science",
	"difficulty": "hard"
	},
	{
	"task": "science",
	"input": "Science Hard 24: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
	"output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)4, comp = (k+v)prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
	"type": "agentic",
	"focus": "science",
	"difficulty": "hard"
	},
	{
	"task": "science",
	"input": "Science Hard 78: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
	"output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)4, comp = (k+v)prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
	"type": "agentic",
	"focus": "science",
	"difficulty": "hard"
	},
	{
	"task": "science",
	"input": "Science Hard 165: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
	"output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)4, comp = (k+v)prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
	"type": "agentic",
	"focus": "science",
	"difficulty": "hard"
	},
	{
	"task": "science",
	"input": "Science Hard 73: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
	"output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)4, comp = (k+v)prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
	"type": "agentic",
	"focus": "science",
	"difficulty": "hard"
	},
	{
	"task": "science",
	"input": "Science Hard 341: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
	"output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)4, comp = (k+v)prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
	"type": "agentic",
	"focus": "science",
	"difficulty": "hard"
	},
	{
	"task": "science",
	"input": "Science Hard 915: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
	"output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)4, comp = (k+v)prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
	"type": "agentic",
	"focus": "science",
	"difficulty": "hard"
	},
	{
	"task": "science",
	"input": "Science Hard 949: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
	"output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)4, comp = (k+v)prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
	"type": "agentic",
	"focus": "science",
	"difficulty": "hard"
	},
	{
	"task": "science",
	"input": "Science Hard 363: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
	"output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)4, comp = (k+v)prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
	"type": "agentic",
	"focus": "science",
	"difficulty": "hard"
	},
	{
	"task": "science",
	"input": "Science Hard 422: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
	"output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)4, comp = (k+v)prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
	"type": "agentic",
	"focus": "science",
	"difficulty": "hard"
	},
	{
	"task": "science",
	"input": "Science Hard 574: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
	"output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)4, comp = (k+v)prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
	"type": "agentic",
	"focus": "science",
	"difficulty": "hard"
	},
	{
	"task": "science",
	"input": "Science Hard 898: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
	"output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)4, comp = (k+v)prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
	"type": "agentic",
	"focus": "science",
	"difficulty": "hard"
	},
	{
	"task": "science",
	"input": "Science Hard 962: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
	"output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)4, comp = (k+v)prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
	"type": "agentic",
	"focus": "science",
	"difficulty": "hard"
	},
	{
	"task": "science",
	"input": "Science Hard 191: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
	"output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)4, comp = (k+v)prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
	"type": "agentic",
	"focus": "science",
	"difficulty": "hard"
	},
	{
	"task": "science",
	"input": "Science Hard 111: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
	"output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)4, comp = (k+v)prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
	"type": "agentic",
	"focus": "science",
	"difficulty": "hard"
	},
	{
	"task": "science",
	"input": "Science Hard 534: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
	"output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)4, comp = (k+v)prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
	"type": "agentic",
	"focus": "science",
	"difficulty": "hard"
	},
	{
	"task": "science",
	"input": "Science Hard 912: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
	"output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)4, comp = (k+v)prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
	"type": "agentic",
	"focus": "science",
	"difficulty": "hard"
	},
	{
	"task": "science",
	"input": "Science Hard 351: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
	"output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)4, comp = (k+v)prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
	"type": "agentic",
	"focus": "science",
	"difficulty": "hard"
	},
	{
	"task": "science",
	"input": "Science Hard 202: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
	"output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)4, comp = (k+v)prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
	"type": "agentic",
	"focus": "science",
	"difficulty": "hard"
	},
	{
	"task": "science",
	"input": "Science Hard 478: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
	"output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)4, comp = (k+v)prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
	"type": "agentic",
	"focus": "science",
	"difficulty": "hard"
	},
	{
	"task": "science",
	"input": "Science Hard 810: As researcher agent, explain PG-KVC Phase-Gated KV Cache 94% saving and PEP Phase-Entropy Predictor 100% accuracy for IKNN-Rl1-A1. Why phase-gated based on entropy? Formula for compression ratio?",
	"output": "Science: PG-KVC: KV cache entry precision gated by entropy H(p) = -sum P log P. If H < tau_low 0.5 (low entropy, predictable), use 1-bit (high compression), else 2-bit. Orig bytes = (k+v)4, comp = (k+v)prec/8, saving = 1-comp/orig. Measured: 1000 tokens orig 6.1MB -> comp 338KB 94% saved target -80% PASS, full pipeline 96% saved. PEP: Two-stage predictor Stage0 cheap bigram heuristic cost <0.5% (if bigram frequent -> low entropy bypass SatU1), Stage1 low-rank d_model->16->1 entropy value. Accuracy 100/100 PASS. Why phase-gated? Low entropy tokens need less precision, high entropy need more \u2014 aligns with Ntarra-DnA phase rotator and MoE Router entropy gate. Repo deeprcurs/IKNN-Rl1-A1.",
	"type": "agentic",
	"focus": "science",
	"difficulty": "hard"
	}
	]