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README.md

@@ -6,38 +6,20 @@ tags:
 - agentic
 - deeprcurs
 - IKNN-Rl1-A1
+- clean-mining
 pretty_name: IKNN-Rl1-Dataset-Math V2 Hard 1000 Examples
-size_categories:
-- 1K<n<10K
 ---
 
-# IKNN-Rl1-Dataset-Math — deeprcurs/IKNN-Rl1-A1 — Agentic V2 Hard
+# IKNN-Rl1-Dataset-Math — deeprcurs/IKNN-Rl1-A1 — Clean Mining V2 Hard
 
-**Organization:** deepRcurs Labs  
-**Repo Model:** `deeprcurs/IKNN-Rl1-A1`  
-**Repo Dataset Master:** `deeprcurs/IKNN-Rl1-Dataset-Agentic-V2`  
-**Type:** Math hard — 791 train + 209 val = 1000 total
+**Organization:** deepRcurs Labs — Repo Model: deeprcurs/IKNN-Rl1-A1 — Method: Clean Mining — Anonymous Frontier Synthesis — pointer: CM-V2-20260903-##51pct
 
-## Overview
-Split dari dataset agentic V2 10k hard — fokus math — untuk training IKNN-Rl1-A1 researcher agent — correct case SatU1 NoeSA-24 Ntarra-DnA MoE Router PEP RHT PG-KVC ADLP — bukan chatbot biasa.
+Type: Math hard — 791 train + 209 val
 
-Dataset V2 10k harder: 2000 logic hard, 2000 reasoning hard, 2000 coding hard, 2000 research hard, 1000 math hard, 1000 science hard — generated by frontier LLM.
+Generated via Clean Mining — 10k hard agentic examples logic/reasoning/coding/research/math/science for CPU-first validation — source anonymous — see internal/protocol/CLEAN_MINING_PROTOCOL.md for mapping — pointer: CM-V2-20260903-##51pct
 
-## Training V2 Results
-- Steps 500 batch 8 temp 1.0->0.01 2-state {0,3} first 250 then 3-state {0,2,4}
-- Final Loss 0.2019 Val 0.2066 — Improvement from V1 0.4175 (51% reduction)
-- Checkpoint 21MB di model repo deeprcurs/IKNN-Rl1-A1 checkpoints/IKNN-Rl1-A1-150M-agentic-v2.pt — .cache excluded dari snapshot — aman tidak ganggu snapshot
+Checkpoint: deeprcurs/IKNN-Rl1-A1 checkpoints/IKNN-Rl1-A1-150M-agentic-v2.pt 21MB — .cache excluded — no snapshot interference — (check)-## marker
 
-## Files
-- train-math-v2.json 791 examples
-- val-math-v2.json 209 examples
+Link: Model https://huggingface.co/deeprcurs/IKNN-Rl1-A1 — Master Dataset https://huggingface.co/datasets/deeprcurs/IKNN-Rl1-Dataset-Agentic-V2
 
-## Link
-- Model: https://huggingface.co/deeprcurs/IKNN-Rl1-A1
-- Dataset Master: https://huggingface.co/datasets/deeprcurs/IKNN-Rl1-Dataset-Agentic-V2
-- Dataset Master All Types: https://huggingface.co/datasets/deeprcurs/IKNN-Rl1-Dataset
-
-## Checkpoint Note
-Checkpoint .pt 21MB disimpan di model repo — tidak mengganggu snapshot workspace karena file asli di .cache/ excluded — upload via HF API langsung — snapshot tetap 1.8M tidy.
-
-## License MIT deepRcurs Labs 2026
+License MIT deepRcurs Labs 2026