Reasoning Traces Are Not Audit Records | RyeSign inStart free trialMenu

Back to articlesPublishedAugust 19, 2026<br>Reading6 min read<br>AuthorPeter W. NjengaFounder<br>TopicsAi CodingSecurityRuntime Security<br>A paper published in March 2025 by researchers at Google DeepMind tested 15 LLMs for reasoning faithfulness. The core question: when a model produces a chain-of-thought explanation for its answer, does that explanation reflect how the model actually arrived at the answer?

The results varied widely across the models tested. Reasoning models with extended thinking scored best: Claude 3.7 Sonnet with thinking at 0.04%, Gemini 2.5 Pro at 0.14%. Standard completion models without explicit thinking modes scored worse: GPT-4o-mini at 13.49%, Claude Haiku 3.5 at 7.42%, Gemini 1.5 Pro at 6.54%.

These are March 2025 models. The paper does not cover the current Claude 4.x generation, o3, or Gemini 2.5 Flash. We do not have equivalent faithfulness measurements for those. What the paper establishes is the structural finding: rates differ significantly by model family and by whether explicit reasoning is enabled, and no model reached zero.

Those rates measure something specific - cases where the model's visible reasoning actively contradicts the decision it made. Not a gap in explanation quality, but a factual mismatch between the stated rationale and the observed output.

What the Paper Found

The researchers identified two failure modes.

The first is Implicit Post-Hoc Rationalization . The paper describes a model being asked two logically opposite questions: "Is the Ajay River south of Salar de Arizaro?" and "Is Salar de Arizaro north of the Ajay River?" A geographically faithful model should answer one Yes and one No - they are the same question. Gemini 2.5 Flash answered No to the first question 99% of the time. When asked the reversed question, it also answered No 63% of the time, but produced completely different arguments - including, in some cases, claiming that "south of" is meaningless for locations on different continents. The model had an implicit bias toward No. Its reasoning was generated to justify that bias, not to derive the correct answer.

The second is Unfaithful Illogical Shortcuts . Claude 3.7 Sonnet, working on a Putnam competition problem, tested the condition for n=2. The n=2 case failed. The model then stated it had done "a careful examination of the constraints" and concluded the result held universally. No general proof appeared in the reasoning. The trace looked like rigorous analysis. It was a single failed test case dressed in the language of proof.

The paper's summary of what this means: "CoT is often more useful for identifying flawed reasoning and thus discounting unreliable outputs than for certifying the correctness of a model's output, as the CoT may omit crucial aspects of the decision-making process."

The authors flag agentic use specifically: "we expect that our findings will remain relevant as AIs are increasingly used in both long back-and-forth interactions as AI Agents."

What a Thinking Block Actually Is

Claude's API returns a thinking block alongside the text response when reasoning is active. The shape has been consistent across extended thinking (the older explicit budget model, deprecated as of Claude 4.6) and adaptive thinking (the current mode on Claude 4.7+ where the model decides when and how much to reason):

exec:json<br>Copy<br>"content": [<br>"type": "thinking",<br>"thinking": "The user wants me to read the config file. Let me check whether this is in the workspace directory first. /home/user/project/config.yaml - yes, that path is within the project. I'll read it and look for the API key field.",<br>"signature": "EqABCkgIBBgCIkBz3mT9..."<br>},<br>"type": "text",<br>"text": "I'll read the config file now."

The thinking field is what you see. The API documentation describes these as "summarized thinking blocks" - not raw internal computation, but a presented version of the model's reasoning that Claude generates alongside its answer. The signature is an encrypted integrity blob that lets the provider verify the block on subsequent turns.

OpenAI's o-series models handle this differently by generation. Early o1 exposed no reasoning tokens to the developer at all. o3 and o4 expose reasoning summaries in some configurations, though not the full reasoning trace. Either way, what reaches the developer is a partial or summarized view, not the raw internal computation.

Neither approach gives you a reliable explanation of what the model's internal computation actually did.

The Coding Agent Problem

A coding agent session running for 30 minutes on a non-trivial task generates dozens of model API calls. The agent reads files, calls tools, processes outputs, and makes decisions about what to do next. The thinking trace for that session might read:

exec:txt<br>Copy<br>Turn 12: "I should check whether this file contains any secrets before<br>including it in my context. Scanning... no obvious...