做LLM推理评估的团队终于有了一个无需标签就能检测组合推理失败的新工具——Operadic Consistency在多个数据集上表现稳定，比CoT-SC更可靠，建议关注这个方向。
论文提出一种名为Operadic Consistency（OC）的新方法，用于在无真实标签的情况下检测大语言模型的推理失败。OC基于操作理论，通过比较模型对组合问题的直接回答与分解后组合回答的一致性，生成每个问题的置信度信号。在12个指令微调模型（4B到671B参数）和4个多跳QA数据集上，OC与准确率的皮尔逊相关系数达0.86-0.94，且在所有数据集上均优于链式思维自一致性（CoT-SC）和语义熵等基线。在选择性预测任务中，OC在固定覆盖率下显著提升准确率，AUARC提升0.086-0.096，AUROC提升0.092-0.164。该方法无需标注数据，为LLM推理可靠性提供了一种高效、通用的诊断工具。
Operadic consistency: a label-free signal for compositional reasoning failures in LLMs
Detecting LLM reasoning failures at inference time without ground-truth labels has motivated a wide range of confidence baselines, including self-consistency, semantic entropy, and P(True), built on within-question sampling and self-evaluation. Operad theory, the formalism for systems built by iterated substitution, suggests a complementary diagnostic: a model's direct answer to a compositional query should agree with the answer it produces by composing a stated decomposition of the same query. We instantiate this idea as operadic consistency (OC), a per-question signal. Across twelve instruction-tuned LLMs (4B to 671B parameters, open-weights and closed-source) on four multi-hop QA datasets, OC is strongly correlated with accuracy on every dataset (Pearson $r \in [0.86, 0.94]$, all $p \leq 0.0004$), and is the only signal we evaluate with $r \geq 0.85$ uniformly across all four datasets. Chain-of-thought self-consistency (CoT-SC; Wang et al., 2023) matches OC on HotpotQA and DROP ($r = 0.93, 0.87$) but drops to $r \approx 0.45$ on MuSiQue and StrategyQA. At the per-question level, OC contributes information beyond CoT-SC and semantic entropy on every dataset (cluster-robust $p \leq 10^{-16}$ for the OC coefficient), and the conclusion is robust to additionally controlling for constructed decomposition-aware baselines ($p \leq 10^{-13}$). The same signal yields selective-prediction improvements (accuracy at fixed coverage) over a tuned CoT-SC baseline at the equal-cost $K = 3$ budget (AUARC lifts of +0.086 to +0.096 and AUROC lifts of +0.092 to +0.164; 95% CIs exclude zero on every cell). On five frontier thinking models, where the decomposition is extracted from the model's own chain of thought, the same equal-cost comparison gives positive selective-prediction point-estimate lift on all 16 (dataset, budget, metric) cells tested, with 95% CIs excluding zero on 12 of the 16.