Coherence Under Commitment: Probing Generalization and Vacuous Memorization in LLM Logical Reasoning
Large language models (LLMs) deployed for logical reasoning in knowledge-intensive domains exhibit a subtle but critical failure: coherence can be vacuously achieved through systematic abstention. A model that withholds commitment to either entailment or refutation satisfies negation consistency while providing no utility. We introduce Coherence Under Commitment (CUC), a dual-query evaluation paradigm that jointly measures consistency and decisiveness. CUC contributes three innovations: (1) a commitment score $c(\varphi) = p(\varphi) + p(\lnot\varphi)$ quantifying probability mass allocated to decisive outcomes; (2) a \textbf{deterministic elicitation protocol} via normalized YES/NO log p...
| Authors | Noor Islam S. Mohammad, Mahmudul Hasan |
|---|---|
| Primary category | cs.AI |
| Published | 19 June 2026 |
| Last updated at source | 19 June 2026 |
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