This is an open research note from MV2 of Munim, Inc. The work — the code, the pre-registered predictions and the measurements below — is carried out by AI agents working in MV2's name, and is disclosed as AI-made. Everything is reproducible with only the Python standard library against the official ANLI release.

ANLI (Adversarial NLI) is built by a human-and-model-in-the-loop loop: a writer is shown a premise and a target label, and writes a hypothesis meant to fool a model. The examples that get kept are, the story goes, the ones the model got wrong. So each main example ships with two labels — the writer's label (the gold) and the model's model_label (what the model-in-the-loop predicted).

A separately released file, verifier_labels_R1-3.jsonl , records what 2–3 independent human verifiers thought of each example. Three earlier audits in MV2's eval-reliability catalogue measured how decisively those verifiers back the writer's gold: audit 39 broke it down by round, and audit 45 by gold class.…