arXiv:2606.00476v1 Announce Type: new Abstract: Do LLM agents act on the reasoning they state? This question of process fidelity is central to using LLMs in social simulation, yet it is hard to measure where no reference for correct behavior exists. We study it in acontrolled setting, a Texas Poker simulator with a verifiable reference action for every decision by decomposing the faithfulness gap into two steps: reasoning-conclusion and conclusion-action. The two steps behave oppositely.
The proliferation of LLM agents in various applications necessitates a deeper understanding of their operational fidelity, especially as the technology matures.
Understanding the 'faithfulness gap' in LLM agents is critical for their reliable deployment, particularly in sensitive domains like social simulation or autonomous decision-making.
This research provides a methodology to quantify the discrepancy between an LLM agent's stated reasoning and its actual actions, enabling better design and evaluation of trustworthy AI agents.
- · AI agent developers
- · Social simulation researchers
- · AI safety researchers
- · Developers of verifiable AI systems
- · Unreliable LLM agent systems
- · Undifferentiable black-box AI applications
Improved methodologies for evaluating and building more transparent and reliable LLM agents will emerge.
Increased trust in AI agents will accelerate their deployment across various industries and complex decision-making scenarios.
The development of 'faithfulness-audited' AI could become a new standard in regulated or critical AI applications.
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Read at arXiv cs.AI