arXiv:2606. 19334v1 Announce Type: cross Abstract: Progress in legal AI increasingly depends on access to authoritative legal text at scale.
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The paper presents a knowledge graph built from Harvard Dataverse’s public data, linking 102,650 datasets to 215,985 nodes and 528,003 edges that include keywords, publications, subjects, journals, and locations. About 43,991 datasets contain geospatial metadata, and 7,654 are identified as policy‑relevant, with elections and legislatures forming the largest cluster. The authors highlight the challenge of place resolution—disconnected nodes representing the same location—and propose the graph as a testbed for AI‑driven metadata enrichment and entity resolution, noting a bias toward American city‑level data.
The paper introduces Legal Rule Induction (LRI), a task that seeks to extract concise, generalizable doctrinal rules from analogous judicial precedents. It presents a reproducible pipeline for constructing LRI datasets and, using Chinese law, releases the first benchmark comprising 5,121 case sets (38,088 court cases) for training and 216 expert‑annotated gold test sets. Experiments show that state‑of‑the‑art large language models struggle with over‑generalization and hallucination, but training on the new dataset significantly improves their ability to capture nuanced rule patterns across similar cases.
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: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:2608.28593v1 Announce Type: new Abstract: With the increasing development of AI regulatory frameworks, ensuring that artificial intelligence systems, particularly generative models, operate in...
arXiv:2608.20391v1 Announce Type: new Abstract: Most legal NLP resources draw from federal case law and focus on coarse classification, leaving administrative adjudication, where the vast majority of...