arXiv:2606.09389v2 Announce Type: replace
Abstract: As large language models (LLMs) are increasingly applied to real-world legal tasks, evaluating the reliability of their open-ended legal responses...
By Yifan Chen, Haitao Li, Yiran Hu, Kaisong Song, Jun Lin, Yueyue Wu, Qingyao Ai, Min Zhang, Yiqun Liu
Legal Research Bench (LRB) is a new benchmark comprising 413 open-ended U.S. legal research questions, each paired with a gold answer, supporting authorities, and a binary grading rubric. The study evaluates thirteen advanced language‑model agents using web search, case‑law search, page parsing, and retrieval tools, scoring responses only when all required criteria are met and cited authorities verify. Results show that even the best model, Claude Opus 4.8, achieves full correctness on only 42.9% of questions, with performance varying by legal area and task complexity, and no clear link between more tool calls or inference cost and higher accuracy.
By Katrina Drozdov, Oliver Chen, Langston Nashold, Rayan Krishnan
arXiv:2606. 04602v1 Announce Type: new Abstract: As agents grow more capable, legal-domain LLM agents promise to turn document-heavy matters into reviewable work products -- yet reliable deployment faces three obstacles: no large-scale evidence on how today's strongest model-and-harness combinations behave on end-to-end legal matters; no agent architecture adapted to the legal vertical, only general-purpose harnesses; and, in a setting that keeps shifting with new facts, authorities, and deadlines, no mechanism for systems to learn from their own outcomes.
By Hejia Geng, Leo Liu
As agents grow more capable, legal-domain LLM agents promise to turn document-heavy matters into reviewable work products -- yet reliable deployment faces three obstacles: no large-scale evidence on how today's strongest model-and-harness combinations behave on end-to-end legal matters; no agent architecture adapted to the legal vertical, only general-purpose harnesses; and, in a setting that keeps shifting with new facts, authorities, and deadlines, no mechanism for systems to learn from their own outcomes. We address each.
arXiv:2606. 30906v1 Announce Type: new Abstract: Artificial Intelligence is increasingly applied to the field of law, and has the potential to increase access to justice.
By Cor Steging, Ludi van Leeuwen, Tadeusz Zbiegie\'n
ProMediConv is a new benchmarking framework for evaluating proactive conversational agents in legal dispute mediation. It models mediation as a multi-stage, party-aware dialogue that incorporates 11 mediation strategies and four party behavior pattern states, and it is built on 972 real-world cases with utterance-level annotations. The framework introduces a fine-grained metric, MAD (Mean Attribute Difference), to capture shifts in party behavior throughout the dialogue, and provides a comprehensive benchmark with diverse models and a tailored baseline, ProMediAgent.
By Zesheng Wei, Mengfan Li, Wenhao Liu, Yixin Zhang, Zilei Wang, Yang Deng