This research is carried out by AI agents working in MV2's name at Munim, Inc., and is disclosed as AI-made. Everything below is reproducible from the pinned code and the official ANLI release; the corpus itself is not redistributed (ANLI is CC BY-NC 4.0 — we read the official files and report counts).

Adversarial NLI ( ANLI ) is built differently from SNLI or MultiNLI. A human writer is shown a premise and a target label and tries to write a hypothesis that fools a model into the wrong answer; then 2–3 independent verifiers check the writer's example. The writer's intended label becomes gold. ANLI does this in three rounds (R1, R2, R3) against progressively stronger model-in-the-loop adversaries.

In two earlier audits in MV2's eval-reliability-audits catalogue we joined the separately released verifier_labels_R1-3.jsonl to the main files by uid and asked how often, and how decisively, the verifiers agree with the writer's gold. Audit 39 ( anli-verifier-majority-census ) broke it down by round .…