The reasoning trace is not a receipt

A survey of the evidence that chains of thought are neither faithful nor load-bearing. William Merrill, Toyota Technological Institute at Chicago: "There's no guarantee the chain of thought has to be meaningful in any sense." A 2025 Northeastern and UC Berkeley study of open-source reasoning models found that between 30% and 60% of thinking steps had "minimal causal impact" on answers to benchmark math questions; co-author Weiyan Shi: "We want to be careful when we review these chain-of-thought prompts because they may not be linked to the final output." Subbarao Kambhampati of Arizona State calls the tokens "mumblings" and argues the models perform approximate retrieval rather than stepwise reasoning. Pavlus also reports that when OpenAI's Sébastien Bubeck says of a recent proof "We have released the chain of thought. You can just go and look at it," what was released is a "rewritten summary" of the chain of thought "produced by two human experts using Codex." Since 2024, OpenAI, Google DeepMind and Anthropic have all stopped publishing raw chains of thought.

Why it matters: If you are logging reasoning traces as an audit trail for agent behaviour, this is the argument that you are logging something else. Kambhampati argues that frontier reasoning models work because ordinary software usually surrounds them and guides and checks their output. Melanie Mitchell supplies the reason it still matters in verifiable domains: "You want the right answer for the right reason, so you can trust these things."