arXiv:2605.28602v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly used for tasks that implicitly reduce to Boolean satisfiability (SAT), yet their reasoning ability on SAT remains unclear. We present a systematic study of LLMs on 2-SAT and 3-SAT, together with two canonical reductions, Vertex Cover and discrete 3D packing, to probe representation-invariant reasoning. We first evaluate models using conventional metrics, including accuracy, precision, recall, and F1, as well as the SAT phase-transition setting. We find that these metrics can be misleading: many models
This paper leverages the recent advancements in LLMs to systematically evaluate their reasoning capabilities on fundamental computational problems, addressing a critical question about their underlying intelligence.
Understanding the true reasoning ability of LLMs, especially on problems like satisfiability, is crucial for determining their limits and potential for autonomous agents and complex problem-solving in enterprise and research contexts.
The conventional metrics for evaluating LLMs are shown to be potentially misleading, suggesting a need for more nuanced assessment methods, particularly when probing their 'representation-invariant reasoning'.
- · AI researchers focusing on LLM foundational capabilities
- · Developers aiming to build more robust AI agents
- · SaaS companies leveraging LLMs for complex logical tasks
- · Companies overestimating LLM 'reasoning' based on superficial metrics
- · Simplistic benchmarks for AI evaluation
More sophisticated and robust evaluation methodologies for LLMs will emerge, moving beyond surface-level performance.
This improved understanding of LLM reasoning limitations will guide the development of hybrid AI systems combining LLMs with symbolic AI or specialized solvers for hard problems.
These insights could lead to a re-evaluation of 'general intelligence' in AI, emphasizing the need for demonstrable, domain-agnostic reasoning over pattern matching.
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Read at arXiv cs.AI