ReguSim: Evaluating LLM Agent Rule Grounding in Financial Compliance

Abstract:LLM agents in financial markets may cite rules yet still submit orders that violate executable constraints or misread surveillance evidence. We introduce ReguSim, a controlled financial-compliance environment, and ReguBench, a target-marked monitoring benchmark, to separate four artifacts: stated reasoning, attempted action, execution enforcement, and monitor evidence. In trader runs with DeepSeek V4 Pro and Gemini 3.5 Flash, visible rules reduce but do not eliminate rejected actions, and incentive or persona framing shifts behavior. A bridge study shows that trader rationales can mislead an independent monitor unless enforcement evidence is shown. In monitoring, simple structured baselines either match or exceed prompt-only LLMs. The results frame financial compliance evaluation as an audit of rule-grounded actions and evidence use, rather than a single compliance score.

Artificial Intelligence (cs.AI)

arXiv:2608.19974 [cs.AI]

(or arXiv:2608.19974v2 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2608.19974 Focus to learn more arXiv-issued DOI via DataCite

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