DAR: Deontic Reasoning with Agentic Harnesses

Hugging Face Daily Papers Papers
deontic-reasoning llm-agents legal-ai agentic-reasoning benchmarking rule-based-reasoning

Summary

This paper introduces DAR (Deontic Agentic Reasoning), an agentic framework enabling LLMs to interactively query statutes and policies for legal/regulatory reasoning tasks. Evaluated on DeonticBench, results show agentic harnesses improve frontier models but can degrade weaker models on numerical tasks while consuming more tokens.

Deontic reasoning is the task of answering questions by applying explicit rules and policies to case-specific facts, for example computing tax liability under a statute or determining the outcome of an immigration appeal. A key technical challenge for LLM-based deontic reasoning is that the relevant ruleset can be long and cross-referenced, so models may still fail to locate the rules needed for a particular reasoning step. We introduce Deontic Agentic Reasoning (DAR), an agentic reasoning setup in which the model interacts with the statutes on demand. We evaluate DAR under multiple harnesses on hard subsets of DeonticBench. Across these settings, we find that agentic harnesses can push the frontier on deontic reasoning tasks, but improvements are not uniform: weaker models often degrade on numerical tasks while consuming far more tokens.
Original Article

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