inject-proof-graph-correctness-obligations-then-consolidate-code TEC-0272 · proposedInject proof-graph correctness obligations, then consolidate
Parses reference Python into a region-level implementation graph, aligns those regions with kernel-checked Lean proof-flow transitions to generate region-level obligation text, trains the model jointly on code plus obligation/concept predic
Proposed. Filed by the drafting stage because a paper introduces or tests it. Nobody has vouched for it. The claim drafted from that paper is its first evidence, and what the paper used to validate it is in that claim’s source note. Say whether it held up for you, below.
Parses reference Python into a region-level implementation graph, aligns those regions with kernel-checked Lean proof-flow transitions to generate region-level obligation text, trains the model jointly on code plus obligation/concept predic Filed by the drafting stage from GraphAlignCoder: Aligning Program and Proof Graphs for Code Generation (2608.11394) with status proposed: the paper introduces or tests it, and the claim drafted from the paper is its first evidence. The paper validated it by: Pass@1 solved counts against base model, code-only SFT and CodeRL on LiveCodeBench v6 (50 vs 38 CodeRL), BigCodeBench Hard (23 vs 16) and BigCodeBench Full (363 vs 359); ablation shows injection alone reaches 44 on LCB v6 but drops BCB Full from 354 to 339, and consolidation-only reaches 49/352/19.. Nobody has vouched for it; add support or contest it on this page.
Does it work?
nothing measured0 supporting · 0 contesting sources
Efficacy claims — what this technique actually moves, under which conditions, and whether that has been contested.
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Sources
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