Harvey LAB-AA v1.1 adds hallucination checks to legal AI benchmark

Legal AI just got a new kind of report card. Version 1.1 of LAB-AA, the legal agent benchmark tied to Harvey’s open-source framework, now includes hallucination checks. The idea is simple. If a model makes a claim, the benchmark wants to know whether the source documents back it up. What changed in LAB-AA v1.1 Harvey announced the v1.1 release of LAB-AA, which adds a layer of hallucination checking to how models are scored. Outputs are now judged on whether they have explicit support in the underlying source documents. LAB-AA is the version of the benchmark run by Artificial Analysis, the independent AI evaluation firm. It launched around July 7, 2026, using a private set of 120 tasks and a single-judge scoring system. Keeping the task set private serves a practical purpose. Models cannot be quietly tuned to ace questions they have never seen, which keeps the leaderboard honest. The new hallucination metric sits on top of the existing scoring approach. Previously, the focus was on whether a model’s answer satisfied expert-written rubric criteria. Now the benchmark also asks whether each claim is grounded in what the documents actually say. A benchmark built to be hard As of October...