arXiv:2606.26935v1 Announce Type: new Abstract: Chain-of-thought (CoT) reasoning is widely used in language-model agents, but prior work has shown that verbalized CoT is not always faithful and may instead reflect post-hoc reasoning, which means the model already knows the answer before reasoning. We therefore ask what CoT training is actually improving: is the model getting better at changing its action through generated reasoning, or is it getting better at predicting the action directly from the prompt? We study this question by comparing \emph{prompt actions} (predicting action without CoT
This research is emerging as LLMs are increasingly deployed in agentic systems, raising critical questions about the true mechanisms behind their apparent reasoning capabilities and the efficacy of current training paradigms.
Understanding whether Chain-of-Thought training improves genuine reasoning or merely direct prediction dictates how AI agents can be reliably developed and integrated into complex workflows, impacting their utility and trustworthiness.
The focus shifts from simply observing CoT benefits to deeply interrogating the underlying cognitive mechanisms, potentially leading to more robust and explainable AI agent architectures.
- · AI researchers focused on interpretability
- · Developers building reliable AI agents
- · Users demanding transparent AI systems
- · Developers relying solely on emergent CoT behavior
- · Practitioners overstating LLM reasoning abilities
Research into LLM training methodologies will become more focused on differentiating true reasoning from 'post-hoc rationalization'.
This differentiation could lead to the development of new training techniques that enhance genuine reasoning capabilities, driving more trustworthy and capable AI agents.
Improved understanding of LLM reasoning could accelerate the deployment of autonomous AI agents in high-stakes environments, transforming various industries with reliable automation.
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Read at arXiv cs.AI