Multiple AI agents debating a problem can often arrive at the right answer while completely destroying the logical reasoning used to get there.
This Reasoning Trap shows that group consensus in AI does not guarantee better logic. In many cases, the agents agree on a correct final result but the underlying chain of thought becomes a mess of errors. This challenges the popular assumption that multi-step reasoning is always an improvement. Information theory proves that there is a limit to how much truth can be preserved in these closed loops. Developers should be cautious about using debate to improve reasoning, as it might just produce a lucky guesser.
The Reasoning Trap: An Information-Theoretic Bound on Closed-System Multi-Step LLM Reasoning
arXiv · 2605.01704
When copies of the same language model are prompted to debate, they produce diverse phrasings of one perspective rather than diverse perspectives. Multi-agent debate (MAD), and more broadly closed-system reasoning where agents iteratively transform each other's outputs, tends to preserve answer accuracy while degrading the reasoning behind those answers. We name the multi-agent case the Debate Trap and the broader phenomenon the Reasoning Trap, offering a programmatic theory of evidence-grounded