arXiv:2605.24305v1 Announce Type: new 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,
The proliferation of advanced LLMs necessitates more rigorous and challenging benchmarks to identify critical limitations beyond standard accuracy metrics.
This benchmark reveals significant shortcomings in frontier LLMs' ability to perform complex logical reasoning over dynamic systems, crucial for reliable autonomous agents.
The focus shifts from general LLM capabilities to specific weaknesses in logical reasoning, particularly concerning dynamic and 'regime-transition' scenarios, necessitating new research and development directions.
- · AI safety researchers
- · Developers of specialized symbolic AI systems
- · Companies investing in explainable AI
- · Companies over-relying on LLMs for complex control systems
- · Foundational model developers with purely statistical approaches
The new benchmark will drive innovation in training methodologies and architectural designs for LLMs to improve logical reasoning.
Increased research into hybrid AI systems combining neural and symbolic approaches will likely result from these identified limitations.
The development of more robust and auditable AI systems will accelerate, leading to greater public trust and broader adoption in critical applications.
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