RuleTrek: Benchmarking Agentic Rule Reasoning in Complex Scenarios

Abstract:Large language models (LLMs) increasingly tackle complex real-world tasks in specialized domains. However, identifying and correctly applying domain-specific rules remains challenging when scenario information is distributed across multiple files. Existing rule-reasoning benchmarks typically represent scenarios using only a few factual statements, simplifying the identification and application of relevant rules. To address this gap, this paper introduces \textbf{RuleTrek}, a multi-domain benchmark for agentic rule reasoning in complex scenarios. Its rule-grounded construction pipeline extracts structured rule graphs, generates and incrementally composes rule-specific sub-scenarios while ensuring consistency and solvability. To systematically evaluate agentic rule reasoning in complex scenarios, RuleTrek assesses three capabilities in interactive workspaces: Rule Identification (RI), Rule Deduction (RD), and End-to-End Rule Reasoning (E2E). Experiments on 11 frontier LLMs show that the highest RI and RD scores are only 62.47\% and 60.43\%, respectively, while E2E scores range from 26.97\% to 55.29\%. These results demonstrate that current LLM agents face substantial challenges in rule reasoning over complex scenarios. We make our code and dataset publicly available at \href{this https URL}{RuleTrek}.

Artificial Intelligence (cs.AI)

arXiv:2610.19698 [cs.AI]

(or arXiv:2610.19698v1 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2610.19698 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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