SOURCE-LINKED INTELLIGENCE
Clueing up LLMs with Tool-Augmented Deductive Reasoning
Despite recent advances in large language models (LLMs), performing logically consistent deductive reasoning over extended interactions remains challenging. Tasks that require integrating evidence across multiple reasoning steps, maintaining consistency with prior inferences, and updating beliefs under new constraints can surface limitations in current models while providing a useful testbed for evaluating reasoning enhancements. In this paper, we implement a text-based, multi-agent version of the classic board game Clue as an environment to evaluate multi-step, agentic deductive reasoning. In
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Evidence & attribution
- arXiv · AI, language, vision and robotics · 2026-09-16T14:32:11.000Z
First collected: 2026-09-19T20:28:26.698Z. This is not the publication date.