| _version_ | 1866901833337798656 |
|---|---|
| author | Yang, Won Chul |
| author_facet | Yang, Won Chul |
| contents | <p>Training dataset and reference implementation for WCY (Watch → Compute → Yield), a token-native reasoning format for AI systems.</p> <p>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.</p> <p>Files:<br>- wcy_traces_v1_clean.jsonl: 528 pipeline-generated traces (480/480 new traces usable, 100%), avg resolution rate 95.8%<br>- wcy_void_cycles.jsonl: 6 hand-crafted void-B resolution cycle traces<br>- wcy_reasoning_traces.jsonl: 6 domain reasoning traces<br>- wcy_parser.py: Reference parser v1.1 (Python)<br>- wcy_eval.py: Three-axis evaluation framework (Structural / Meaning / Provenance)<br>- README.md, DATASET.md: Documentation</p> <p>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.</p> <p>Version history:<br>- v1.0 (2026-03-17): 60 traces (48 pipeline + 12 hand-crafted)<br>- v1.1 (2026-03-18): 540 traces (528 pipeline + 12 hand-crafted)</p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_19068769 |
| institution | Zenodo |
| language | eng |
| publishDate | 2026 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | WCY Reasoning Traces v1.0 — Void-B Resolution Cycles for AI Epistemic Training Yang, Won Chul LLM training data, reasoning traces, epistemic humility, void-B, AI self-awareness, WCY format, NLP dataset, abductive reasoning <p>Training dataset and reference implementation for WCY (Watch → Compute → Yield), a token-native reasoning format for AI systems.</p> <p>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.</p> <p>Files:<br>- wcy_traces_v1_clean.jsonl: 528 pipeline-generated traces (480/480 new traces usable, 100%), avg resolution rate 95.8%<br>- wcy_void_cycles.jsonl: 6 hand-crafted void-B resolution cycle traces<br>- wcy_reasoning_traces.jsonl: 6 domain reasoning traces<br>- wcy_parser.py: Reference parser v1.1 (Python)<br>- wcy_eval.py: Three-axis evaluation framework (Structural / Meaning / Provenance)<br>- README.md, DATASET.md: Documentation</p> <p>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.</p> <p>Version history:<br>- v1.0 (2026-03-17): 60 traces (48 pipeline + 12 hand-crafted)<br>- v1.1 (2026-03-18): 540 traces (528 pipeline + 12 hand-crafted)</p> |
| title | WCY Reasoning Traces v1.0 — Void-B Resolution Cycles for AI Epistemic Training |
| topic | LLM training data, reasoning traces, epistemic humility, void-B, AI self-awareness, WCY format, NLP dataset, abductive reasoning |
| url | https://doi.org/10.5281/zenodo.19068769 |