Medical AI benchmarks routinely pair a diagnostic image with the same-visit clinical record, delete the recorded diagnosis, and treat what remains as auxiliary context. The recipe is reasonable. It is not. On 5904 ophthalmic visits across eight cohorts, a frozen text probe still recovers the disease label from the diagnosis-redacted record at 0.944 macro-AUROC, against 0.998 on the unredacted record; the most aggressive dictionary scrub closes less than one further percentage point. The diagnosis is already in the notes.

It is not in one place. A clinical record is written during the diagnostic process, and its fingerprints persist across channels: narrative text alone reaches 0.978, and a view that erases every value and exposes only which fields were populated still reaches 0.854. We introduce CRAFT (Channel-wise Redaction And Floor Test), a portable audit protocol that renders each record into redacted and channel-isolated views and reports each channel’s label recoverability; rankings reproduce under Qwen3.5-9B. Before clinical-text or image-text fusion gains are read as cross-modal reasoning, the text branch’s channel-level shortcut floors must be reported alongside them. https://github.com/yhc98002-bit/The-Diagnosis-Is-Already-in-the-Notes

Questions about a recurring person in long videos are rarely answered from a single moment. They require evidence that is sufficient for the claim, comparisons across episodes, synthesis over recurring audiovisual patterns, and refusal when the source material does not support an answer. Existing long-video benchmarks do not isolate these requirements. We introduce KnowMeBenchV2, an evaluation-only benchmark over five public multi-episode video series, with 327 videos and 3,061 questions covering four evidence scopes: single-clip extraction, temporal and event relations, cross-episode ordering, and cross-episode persona synthesis. A separate grounded-abstention task serves as a contrastive support check: its questions are surface-similar to supported person-linked questions but are not licensed by the source evidence. The most interpretive synthesis items are human-authored and quality-audited by trained annotators with full source access. A matched human reference reaches 78.0 on cross-episode synthesis. To diagnose failures, we compare evidence routes under a fixed Qwen3.5-9B answerer and a task-matched pre-tokenization character routing budget. ASR-Retrieval reaches 2.7 on synthesis, while KnowMe reaches 64.9. In this diagnostic, the final answerer reasons over textualized evidence constructed from audiovisual sources rather than raw video tokens. The gap therefore isolates the importance of audiovisual evidence construction and cross-episode organization before answer generation.