---
format: "aidr-story-markdown/v1"
id: "3523a6e28270de251b5a3c90aa087a09693dc7fb60fffd751700704cecda8050"
canonical_url: "https://aidr.today/3523a6e2?lang=en"
title: "RuleTrek: Benchmarking Agentic Rule Reasoning in Complex Scenarios"
lang: "en"
requested_lang: "en"
available_langs: ["en","vi"]
translation_fallback: null
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published_at: "2026-10-15T00:00:00.000Z"
category: "Research"
topics: ["reasoning"]
source_urls: ["https://huggingface.co/papers/2610.19698"]
summary: "RuleTrek is a multi-domain benchmark introduced to evaluate agentic rule reasoning in complex scenarios where scenario information is distributed across multiple files. It uses a rule-grounded construction pipeline to extract structured rule graphs and generate consistent, solvable sub-scenarios, assessing Rule Identification, Rule Deduction, and End-to-End Rule Reasoning. Experiments on 11 frontier LLMs show the highest Rule Identification and Rule Deduction scores are 62.47% and 60.43%, respectively, while End-to-End scores range from 26.97% to 55.29%."
---

# RuleTrek: Benchmarking Agentic Rule Reasoning in Complex Scenarios

> [Open the canonical story](<https://aidr.today/3523a6e2?lang=en>)

**Published:** 2026-10-15T00:00:00.000Z
**Category:** Research
**Topics:** reasoning

## Summary

RuleTrek is a multi\-domain benchmark introduced to evaluate agentic rule reasoning in complex scenarios where scenario information is distributed across multiple files\. It uses a rule\-grounded construction pipeline to extract structured rule graphs and generate consistent, solvable sub\-scenarios, assessing Rule Identification, Rule Deduction, and End\-to\-End Rule Reasoning\. Experiments on 11 frontier LLMs show the highest Rule Identification and Rule Deduction scores are 62\.47% and 60\.43%, respectively, while End\-to\-End scores range from 26\.97% to 55\.29%\.

## Sources

- [Story source](<https://huggingface.co/papers/2610.19698>)