Run step
Step 10: tool results
get_claim_instances
Input
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"verbatim_text": "As of October 2026, Anthropic is likely improving the capabilities of their models on well-specified tasks at around twice the rate they did between March 2024 and October 2025. This estimate is given with high uncertainty; we would not be surprised if the true acceleration was closer to 1.3x or 3x the historical rate.\n\nA majority of this acceleration today is likely attributable to Anthropic’s extensive use of AI to do R&D, though there is significant uncertainty and other causes likely played a large role in acceleration in late 2025.",
"context": "Executive summary of an eleven-week evaluation with access to Anthropic's internal data. Anthropic held redaction rights and redacted most of the evidence the evaluators found most informative.",
"stance": "affirms",
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"speaker": "METR (Tom Cunningham, Nate Rush, Parker Whitfill, Hjalmar Wijk)",
"publication": "METR",
"source_date": "2026-11-05",
"link": null,
"source_title": "Independent Assessment of AI R&D Acceleration at Anthropic in October 2026",
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"source_url": "https://metr.org/blog/2026-11-05-anthropic-ai-rd-acceleration-assessment/",
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"party": "Anthropic",
"stake": "The assessment concerns Anthropic's own research process, and Anthropic held rights to redact non-public information; per the report, it redacted a majority of the evidence METR found most informative.",
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"recorded_at": "2026-11-14T02:23:04.711Z"
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"deployment": "States the claim as the main conclusion of a risk assessment of one developer, with explicit uncertainty on both the size of the acceleration and its attribution to AI.",
"note": "The report shows its benchmark trend analysis and robustness checks, which bear the finding of an acceleration. The attribution to AI use is given as a subjective estimate, and the report states that most of the evidence behind it, including the detailed attribution estimates and the evidence that training compute did not speed up, was redacted by Anthropic. No party other than METR and Anthropic has examined that evidence. The executive summary, findings, process, limitations and methodology were read; the later appendices were not.",
"quote_check": "verbatim",
"worth_reading": true,
"worth_reading_reason": "The appendices on benchmark robustness, the comparison of Anthropic's earlier growth with the industry frontier, and the staff interviews bear on how much of the trend break could be catch-up and were not read in the first pass.",
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"verbatim_text": "The Risk Report argues the 2x Acceleration Threshold has not been crossed as capability metrics and leading indicators show less than a 2x acceleration, with factors other than AI driving that acceleration.",
"context": "METR's summary of Anthropic's own position in its August 2026 Risk Report, covering the period to 15 July 2026: an acceleration below twofold, driven by factors other than AI. METR's review calls the argument that the threshold had not been crossed overstated.",
"stance": "mentions",
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"source_title": "Review of the \"Risks from automated R&D\" section in Anthropic's Risk Report (August 2026)",
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"verbatim_text": "There is currently no significant end-to-end acceleration of ML R&D, AI-driven or otherwise.",
"context": "A Google DeepMind researcher summarizing the developer's own assessment of its research process. The statement concerns Google DeepMind only: it reports no acceleration there and does not address other developers.",
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"speaker": "Haydn Belfield (Google DeepMind)",
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"source_date": "2026-10-14",
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"source_title": "Thread summarizing the ML R&D assessment in the Gemini 4 Argon Safety & Security Report",
"source_type": "social_media_post",
"source_url": "https://x.com/HaydnBelfield/status/2110394773521498535",
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"verbatim_text": "Overall, this triangulation reassures me that AI is really responsible for Anthropic’s acceleration.",
"context": "An outside analysis that compares METR's report with public benchmark data on released Claude models and reports reproducing most of the trend break (about 1.64 times) without internal data, then reasons about which inputs could explain it. It notes that faster growth of experimental compute was not ruled out.",
"stance": "affirms",
"confidence": 0.85,
"speaker": "Karthik Tadepalli",
"publication": "Beyond Imitation",
"source_date": "2026-11-07",
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"source_title": "Anthropic's AI acceleration is probably real",
"source_type": "blog_post",
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"verbatim_text": "part of the acceleration may reflect Anthropic catching up to the frontier, and Anthropic redacted much of the evidence that METR found most informative.",
"context": "A review of evidence on recursive self-improvement. It reports that the public frontier of the Epoch Capabilities Index has risen about 14 points a year since September 2024, describes the internal evidence from developers as showing a potential acceleration, and qualifies METR's Anthropic finding.",
"stance": "mentions",
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"speaker": "Forecasting Research Institute",
"publication": "Forecasting Research Institute (Substack)",
"source_date": "2026-11-10",
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"source_title": "How Close Is Recursive Self-Improvement?",
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"verbatim_text": "is showing that AI is already accelerating the development of AI systems.",
"context": "Anthropic Institute report (2026) on progress toward recursive self-improvement, citing public benchmarks and internal Anthropic data.",
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"confidence": 0.85,
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"source_title": "When AI builds itself",
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"source_url": "https://www.anthropic.com/institute/recursive-self-improvement",
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"stake": "Anthropic develops and sells the Claude models the report describes, and the report is published by its own institute. It gains commercially and reputationally from its models being seen as highly capable, and its account of their limits shapes how regulators and the public judge the pace of AI development.",
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"recorded_at": "2026-11-13T21:36:33.847Z"
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