Ropes & Gray builds transactional diligence AI with OpenAI
The tool drafts a first-pass commercial-contract report from a data room; lawyers still review before anything goes to clients.
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Stories tagged #legal-ai
Hacker News post claims an open-source Harvey-like legal AI
The feed snippet shows only the title, 65 points and 17 comments, with no product details.
Study maps how WildChat users ask LLMs for legal guidance
In an enriched sample, 71.1% of legal-content chats sought personalized or practical guidance; annotations are public.
Z.ai GLM-Legal Beats Claude Fable 5 and Kimi K3 on Harvey LAB
Z.ai’s GLM-Legal reportedly beat Claude Fable 5 and Kimi K3 on Harvey LAB.
GLM-Legal: Advancing Agentic Legal Intelligence
Z.ai unveiled GLM-Legal, a GLM-5.3-Flash legal agent that matches frontier models on Harvey, LegalBench, and PLawBench.
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Test-Time Agent Evolution for Long-Horizon Legal Reasoning
A training-free legal agent adapts at test time using memory evolution and rubric-aligned collaboration.
Do Large Language Models Know Colombian Law? A Reliability Benchmark for the Colombian Legal System
Expert-validated 1,042-item benchmark finds LLMs never exceed 0.45 free-text correctness on Colombian law, with about half of cited legal norms wrong or nonexistent.
ViLegalExpert: A Large-Scale Benchmark for Vietnamese Legal Retrieval and Question Answering from Real-World Consultations
ViLegalExpert benchmarks Vietnamese legal retrieval and QA on 172K real consultation questions.
OpenAI takes aim at the legal market with Astra for Law
OpenAI launched Astra for Law, a GPT-6-based legal research product with a 230-million-URL US case law index, scoring 54% on Vals AI's Legal Research Bench.
Introducing Astra for Law
OpenAI launched Astra for Law, pairing GPT-6 Astra with a 230-million-URL legal search index, scoring 54.0% on Vals AI's Legal Research Bench.
How Much is a Human Right Worth? ECtHR-NPD: A Benchmark for Predicting Non-Pecuniary Damage Awards
ECtHR-NPD benchmark covers 14,575 European Court of Human Rights cases for predicting non-pecuniary damage awards; LLMs struggle with zero and high awards.