LegalEvolver is a query‑optimization framework for legal judgment prediction that generates legally informative queries to improve retrieval quality. It uses heuristic query seeding, summarizes rollout experiences into rubrics, and applies rubric‑guided reinforcement learning for iterative self‑evolution. Experiments on large benchmarks show that evolving rubrics outperform fixed ones and that rubric guidance boosts performance beyond direct supervised fine‑tuning plus RL.
arXiv:2606. 17220v1 Announce Type: new Abstract: Legal case retrieval remains challenging due to the complexity of legal language and the need for precise lexical alignment between queries and relevant cases.
arXiv:2605.25920v2 Announce Type: replace Abstract: While large language models (LLMs) augmented with agentic search capabilities show promise for legal reasoning, they overlook a fundamental constra...
arXiv:2608.29856v1 Announce Type: new Abstract: Large language models are increasingly used as scalable evaluators for open-ended tasks. However, many LLM judges derive query-specific criteria during...
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.
arXiv:2505. 02763v2 Announce Type: replace-cross Abstract: One of the central promises of legal AI is to automate drudgery -- the formal, repetitive tasks of lawyers' work that consume time without calling for much discretion.
The paper evaluates three retrieval strategies—Vector RAG, Tree Reasoning, and a new Adaptive Hybrid Retrieval (AHR)—on a small corpus of financial, legal, and medical documents, using a four‑tier query complexity benchmark and LLM‑as‑judge evaluation. Tree Reasoning achieves the highest overall score (0.900), but AHR excels on cross‑reference queries (0.850) and all methods tie on multi‑section queries (0.929). Vector RAG shows the lowest latency (1.61 s) and performs best on single multi‑document queries (0.900).
arXiv:2607. 03325v1 Announce Type: cross Abstract: We present an automated pipeline that decomposes Italian tax-court judgments into individual legal issues and extracts, for each issue, a structured XML representation grounded in the IRAC framework and the legal syllogism.
arXiv:2509.00141v2 Announce Type: replace-cross Abstract: Statutory corpora and judicial decisions are growing faster than legal professionals can read them, while individual judgments often exceed t...
arXiv:2608. 20220v1 Announce Type: new Abstract: Legal AI systems are increasingly used to answer legal questions, yet existing benchmarks assume queries arrive fully specified.
arXiv:2610.08452v1 Announce Type: cross Abstract: Retrieval-augmented generation (RAG) is a widely used approach for grounding large language models (LLMs) in external knowledge. However, configuring...
arXiv:2610.08138v1 Announce Type: new Abstract: Legal intelligence aims to support reliable decision-making across long-horizon legal processes involving evolving case states and multiple roles. Howe...
The paper introduces Juris Policy Optimization (JPO), a post‑training framework designed to enhance structured legal reasoning in Chinese criminal judgment prediction. JPO first trains models with teacher‑generated rationales to guide a four‑step reasoning process, then applies reinforcement learning using a composite reward that balances prediction accuracy, reasoning completeness, and cross‑step consistency. Experiments on several open‑source language models and three Chinese legal benchmarks demonstrate that JPO consistently outperforms both supervised fine‑tuning and standard reinforcement learning baselines in terms of judgment prediction and reasoning quality.