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RuleWeaver: Benchmarking Rule-Centered Scenario Reasoning for Large Language Models
Large language models (LLMs) are increasingly applied to specialized domains, where effective use of domain expertise often requires reasoning over complex rules in concrete scenarios. However, existing benchmarks only partially evaluate this capability, as they either focus on output-level instruction constraints or overlook the distinct roles that rules play in scenario reasoning. To address these gaps, this paper introduces RuleWeaver, a benchmark construction framework for evaluating rule-centered scenario reasoning. RuleWeaver starts from corpus-derived IF-THEN Meta Rules, progressively a
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Evidence & attribution
- arXiv · AI, language, vision and robotics · 2026-08-27T09:05:11.000Z
First collected: 2026-09-21T08:51:59.673Z. This is not the publication date.