Step 2: tool results

get_claim

{
  "include": [
    "arguments",
    "subclaims",
    "related"
  ],
  "claim_id": "0655bddb-4381-4431-bc7e-c04ae510a5d6"
}

{"claim":{"id":"0655bddb-4381-4431-bc7e-c04ae510a5d6","kind":"claim","canonical_form":"Anthropic's model capability trend since late 2025 reflects sustained acceleration rather than a one-time jump.","claim_type":"empirical_derived","state":"active","decomposition_status":"complete","importance":0.55,"contestation":0.6,"domains":[],"tags":[{"slug":"anthropic","name":"Anthropic"},{"slug":"llm-capability-trends","name":"LLM capability trends"}],"steward_state":"running","stewarded_at":"2026-11-14T02:35:21.441Z","canonical_direction_note":"The affirmative form states the source's favored explanation of Anthropic's post-2025 capability trend, rather than the alternative of a one-time jump.","created_at":"2026-11-13T22:29:50.519Z","updated_at":"2026-11-14T02:35:15.014Z","page_url":"https://minerval.ai/claims/0655bddb-4381-4431-bc7e-c04ae510a5d6"},"assessment":null,"coverage":{"examined":false,"on":[],"note":"No judgment of this node is in force. That is not a finding either way."},"quantity":null,"threshold_of":null,"question":null,"formalization":null,"formalization_pending":null,"formalization_history":[],"verification":null,"lean_checks":[],"lean_checks_total":0,"waiting_messages":[{"trigger":"structure_and_assess","from":"the system","sender_node_id":null,"sent_at":"2026-11-13T22:30:20.818Z","paid":false,"summary":"New claim onboarded. Give it the structure and assessment its importance warrants; a minor or settled claim may need no decomposition."},{"trigger":"mandate_plan","from":"a mandate's plan","sender_node_id":null,"sent_at":"2026-11-14T02:35:15.016Z","paid":true,"summary":"A funded mandate scheduled a assess pass on this claim."}],"requests":[],"requests_total":0,"subclaim_count":0,"instance_count":2,"source_map":null,"arguments":[],"related":[],"subclaims":[],"subclaims_assessed":0,"unplaced_argument_passages":[{"instance_id":"5f44e6bc-cc3f-4804-84db-0cdf86e29de6","source":{"id":"0dea0a35-a2dc-4d27-a7fa-9d4431940f49","url":"https://metr.org/blog/2026-11-05-rd-section-anthropic-risk-report-august-2026-review/","title":"Review of the \"Risks from automated R&D\" section in Anthropic's Risk Report (August 2026)"},"verbatim_text":"At the time, the AECI data was only mildly in favor of a jump, and a continued acceleration could not be ruled out. If we reuse the data that was used in the Risk Report, the estimated acceleration is around 1.5x. After the publication of the Risk Report, Anthropic added data from more benchmarks, which estimates an acceleration of 1.9x using the same set of models.","context":null,"stance":"advances","direction":"for"},{"instance_id":"0a466918-2b11-4754-8e31-c61f4843f93f","source":{"id":"0dea0a35-a2dc-4d27-a7fa-9d4431940f49","url":"https://metr.org/blog/2026-11-05-rd-section-anthropic-risk-report-august-2026-review/","title":"Review of the \"Risks from automated R&D\" section in Anthropic's Risk Report (August 2026)"},"verbatim_text":"A continued acceleration in AECI would be more consistent with the continued acceleration observed in the leading indicators. As described in our <a href=\"/blog/2026-11-05-anthropic-ai-rd-acceleration-assessment/\">holistic assessment</a>, we think the evidence since the public risk report points in favor of continued acceleration since October 2025.","context":null,"stance":"advances","direction":"for"},{"instance_id":"796510a9-ecf8-40ff-be77-5a29b3f9577a","source":{"id":"0dea0a35-a2dc-4d27-a7fa-9d4431940f49","url":"https://metr.org/blog/2026-11-05-rd-section-anthropic-risk-report-august-2026-review/","title":"Review of the \"Risks from automated R&D\" section in Anthropic's Risk Report (August 2026)"},"verbatim_text":"A continued acceleration in AECI would be more consistent with the continued acceleration observed in the leading indicators. As described in our holistic assessment, we think the evidence since the public risk report points in favor of continued acceleration since October 2025.","context":null,"stance":"advances","direction":"for"},{"instance_id":"fd30aae2-573b-44a2-827a-2bf6f3645662","source":{"id":"0dea0a35-a2dc-4d27-a7fa-9d4431940f49","url":"https://metr.org/blog/2026-11-05-rd-section-anthropic-risk-report-august-2026-review/","title":"Review of the \"Risks from automated R&D\" section in Anthropic's Risk Report (August 2026)"},"verbatim_text":"We did a naive extrapolation based only on the small number of models most directly optimized for maximum capability, assuming continued acceleration. This extrapolation led us to more correctly forecast future AECI scores, compared to an extrapolation based on all the points.","context":"Footnote 3.","stance":"advances","direction":"for"}],"unplaced_argument_passages_total":4}