How well do SOTA legal reasoning models support abductive reasoning?

Abstract

Abstract: We examine how well the state-of-the-art (SOTA) models used in legal reasoning support abductive reasoning tasks. Abductive reasoning is a form of logical inference in which a hypothesis is formulated from a set of observations, and that hypothesis is used to explain the observations. The ability to formulate such hypotheses is important for lawyers and legal scholars as it helps them articulate logical arguments, interpret laws, and develop legal theories. Our motivation is to consider the belief that deep learning models, especially large language models (LLMs), will soon replace lawyers bec

Research Question

How does the robustness of o1-preview and DeepSeek-R1 to adversarial token-level perturbations (e.g., synonym substitution) vary with model size and inference budget, measured by accuracy drop and inference throughput on legal reasoning benchmarks?

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Quality Dimensions

Automated triage signals derived from public fields; not human peer review or independent validation.

Correction Record

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Provenance