correct-reasoning-inverting-typed-error-operators TEC-0111 · proposedCorrect reasoning by inverting typed error operators
Corrupt correct reasoning traces by composing typed executable error operators, train an adapter to predict the active operator plus the adjacent lower-noise trace, and at inference iteratively predict and apply inverse operators to the mod
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.
Corrupt correct reasoning traces by composing typed executable error operators, train an adapter to predict the active operator plus the adjacent lower-noise trace, and at inference iteratively predict and apply inverse operators to the mod Filed by the drafting stage from Semantic Reasoning Denoising: Correcting Language Model Reasoning with Semantic Operators (2608.22090) 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: In-domain average 67.5 vs 64.3 for the strongest same-backbone baseline (DiffCoT) and 63.7 for SCoRe over GSM8K, MATH, HumanEval+, MBPP, MMLU, PIQA, five seeds, Holm-corrected; transfer averages 76.4 (Llama-3, base 67.7) and 80.2 (Qwen3, base 72.9); ablations drop to 79.1/80.6 on GSM8K for operator-only and adjacent-state-only.. 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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