arXiv:2605.24597v1 Announce Type: cross Abstract: Many applications of large language models (LLMs) require deductive reasoning, yet models frequently produce incorrect or redundant inference steps. We frame natural language inference as a search problem where the final answer is the valid proof itself, requiring a reasoning procedure in which intermediate inferences are correct. Specifically, we investigate whether LLMs can learn to generate correct and efficient proofs with guidance from A* search -- an algorithm that guarantees an optimally efficient path to a goal. We explore two training
The increasing complexity of LLM applications necessitates more robust and efficient reasoning capabilities, making current research into structured inference timely.
Improving LLMs' deductive reasoning and proof generation directly enhances their reliability and utility for critical applications, moving beyond mere statistical pattern matching.
LLMs could move from probabilistic generation to verifiable logical inference, making their outputs more trustworthy and applicable to high-stakes domains.
- · AI developers
- · Enterprises adopting AI for complex tasks
- · Cognitive science researchers
- · Brute-force LLM inference methods
- · Applications requiring non-verifiable reasoning
LLMs will be capable of more accurate and auditable deductive reasoning processes.
This could accelerate the deployment of LLMs in fields like legal analysis, scientific discovery, and automated code generation requiring logical consistency.
The development of truly 'reasoning' AI may contribute significantly to the feasibility of advanced AI agents and broader artificial general intelligence.
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Read at arXiv cs.CL