WCY Reasoning Traces v1.0 — Void-B Resolution Cycles for AI Epistemic Training

Training dataset and reference implementation for WCY (Watch → Compute → Yield), a token-native reasoning format for AI systems. Contains 540 high-quality WCY reasoning traces across 8 domains (medical, code, mathematical, legal, strategic, philosophical, scientific, engineering), generated via a quality-controlled pipeline with three gates: parse_rate ≥ 0.70, void_generated ≥ 1, resolution_rate ≥ 0.50. Files:- wcy_traces_v1_clean.jsonl: 528 pipeline-generated traces (480/480 new traces usable, 100%), avg resolution rate 95.8%- wcy_void_cycles.jsonl: 6 hand-crafted void-B resolution cycle traces- wcy_reasoning_traces.jsonl: 6 domain reasoning traces- wcy_parser.py: Reference parser v1.1 (Python)- wcy_eval.py: Three-axis evaluation framework (Structural / Meaning / Provenance)- README.md, DATASET.md: Documentation The core contribution is the void-B (?) resolution cycle: mark unknown → investigate → observe → resolve. This cycle is the structural minimum for directed epistemic self-awareness in machine learning systems. Version history:- v1.0 (2026-03-17): 60 traces (48 pipeline + 12 hand-crafted)- v1.1 (2026-03-18): 540 traces (528 pipeline + 12 hand-crafted)