arXiv:2605.26893v1 Announce Type: new Abstract: Chain-of-Thought (CoT) reasoning has advanced large language models (LLMs), but outcome-based supervision leads to pervasive post-hoc rationalization, producing plausible yet unfaithful reasoning chains. Most prior faithfulness assessment methods are either unscalable, expensive, or unreliable. We propose GeoFaith, a spatio-temporal framework that leverages latent geometric structure and entropy dynamics to diagnose and enforce faithful reasoning. We develop a scalable bootstrapping pipeline expanding step-level annotations from 1k to 20k samples
The rapid advancement and adoption of large language models are exposing the limitations of current faithfulness assessment methods, making the development of new, scalable solutions critical.
Ensuring the faithfulness of Chain-of-Thought reasoning in LLMs is crucial for their reliable deployment in sensitive applications, impacting trust and safety in AI systems.
The ability to more accurately diagnose and enforce faithful reasoning in LLMs will lead to more robust and trustworthy AI applications, shifting development focus from mere output quality to explainable transparency.
- · AI developers
- · Enterprises deploying LLMs
- · AI ethics and safety researchers
- · Users of AI systems
- · Models reliant on unfaithful reasoning
- · Companies with poor AI explainability practices
Improved methods for evaluating and ensuring the faithfulness of AI reasoning will emerge, increasing model reliability.
Public and regulatory trust in AI systems will increase as reasoning processes become more transparent and verifiable.
The development of truly 'reasoning' AI agents for complex and high-stakes tasks will accelerate, impacting professional workflows.
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