ChaosBench-Logic v2: Evaluating LLM Logical Reasoning over Dynamical Systems at Scale
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Computer Science > Machine Learning
Title: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.
| Comments: | 14 pages, 8 figures. Published at the ICLR 2026 Workshop on LLM Reasoning |
| Subjects: | Machine Learning (cs.LG); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2605.24305 [cs.LG] |
| (or arXiv:2605.24305v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2605.24305
arXiv-issued DOI via DataCite (pending registration)
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