ChaosBench-Logic v2: Evaluating LLM Logical Reasoning over Dynamical Systems at Scale

Abstract:Standard accuracy on binary reasoning benchmarks hides critical failure modes: prior collapse, inconsistency under paraphrase, and inability to reason about parameter-dependent dynamics. We present ChaosBench-Logic v2, a 40,886-question benchmark over 165 dynamical systems with 27 FOL predicates and 78 axiom edges, together with CARE (Calibration- and Adversarial-Robust Evaluation), a protocol that surfaces these pathologies. Evaluating 14 models, we find that regime-transition reasoning remains near random (MCC = 0.05) even for frontier models, whereas FOL deduction with given premises reaches MCC = 0.52. Per-family decomposition shows that the proprietary-model advantage concentrates on cross-indicator (+0.40) and consistency tasks, while open-source Qwen 2.5-32B dominates indicator diagnostics (0.91 vs. 0.45). Two models exhibit negative MCC on bifurcation questions, confirmed as systematic anti-correlation via confusion-matrix analysis.

14 pages, 8 figures. Published at the ICLR 2026 Workshop on LLM Reasoning. v2: corrected bibliographic errors in the references

Machine Learning (cs.LG); Artificial Intelligence (cs.AI)

arXiv:2605.24305 [cs.LG]

(or arXiv:2605.24305v2 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2605.24305 Focus to learn more arXiv-issued DOI via DataCite

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