Step 2: tool results

get_claim

{
  "include": [
    "arguments",
    "subclaims"
  ],
  "claim_id": "ce83c0f0-e756-4e41-89d8-86d2f16dc9c3"
}

{"claim":{"id":"ce83c0f0-e756-4e41-89d8-86d2f16dc9c3","kind":"claim","canonical_form":"Current AI systems can reliably generate novel hypotheses in experimental science that survive validation.","claim_type":"empirical_derived","state":"active","decomposition_status":"pending","importance":0.55,"contestation":0.6,"domains":[],"tags":[{"slug":"ai-scientific-discovery","name":"AI Scientific Discovery"}],"steward_state":"running","stewarded_at":"2026-11-15T07:13:17.897Z","canonical_direction_note":null,"created_at":"2026-11-06T00:01:21.722Z","updated_at":"2026-11-15T07:08:10.311Z","page_url":"https://minerval.ai/claims/ce83c0f0-e756-4e41-89d8-86d2f16dc9c3"},"assessment":{"id":"77805713-d9ec-47f5-855e-ec110a8e07ea","status":"doubtful","confidence":0.6,"claim_credence":0.3,"summary":"Some AI systems have produced experimental-science hypotheses that later held up in the laboratory. The best-known case is the [[claim:474f2f6a-3f2c-4294-b232-86767bcbb595|validation of hypotheses from Google's AI Co-Scientist]], including drug-repurposing candidates and a mechanism of bacterial gene transfer. But the claim says AI does this reliably. The evidence so far shows that it can happen sometimes, not that it happens dependably.\n\nThe successes come from pipelines in which experts chose a few candidates from large ranked pools. The share of AI-generated hypotheses that survive testing, and how that share compares with human hypotheses, has not been measured. In the one controlled execution study, by Si, Hashimoto and Yang (2025), [[claim:2e6a6348-a373-48b5-b7fa-f26a3c3342c5|LLM-generated research ideas lost their edge over expert ideas once they were actually carried out]]. Reports on autonomous \"AI scientist\" pipelines also describe frequent failed or flawed experiments.\n\nBetter evidence would settle this: validation rates for AI-proposed hypotheses drawn from unselected pools, tested against human-generated controls.","reasoning_trace":"This pass had little budget, so it rests on the recorded subclaims and general knowledge of the literature up to 2026, without fresh reading of sources.\n\nFor the claim: the AI Co-Scientist hypotheses that were confirmed in the lab. That subclaim has not yet been assessed. Its weight is limited because experts selected which candidates to test and the work was done in collaboration with the system's developers.\n\nAgainst the claim: the ideation-execution study, which has been assessed as supported (credence 0.68).\n\nThe word \"reliably\" carries the claim. A few lab confirmations after selection show the capability exists. They do not show reliability. The evidence that would show it is missing: base rates of how many AI-proposed hypotheses survive testing, compared against controls. That missing evidence is the main reason the verdict is doubtful rather than contradicted.","marginal_yield":0.6,"assessed_at":"2026-11-11T23:09:29.393Z","model":"claude-opus-5-5","run_id":"cef864ed-4050-4235-a376-2f0c1b0d272f","relabeled_from":null},"coverage":{"examined":true,"on":["public_evidence"]},"since_assessed":{"recorded":true,"changed":true,"premises_rejudged":1,"premises_added":0,"premises_removed":0,"sources_added":0,"sources_removed":0,"source_events":0},"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":"consistency_flag","from":"the Consistency Checker","sender_node_id":null,"sent_at":"2026-11-15T01:15:06.838Z","paid":false,"summary":"A consistency sweep finds that this claim's assessment (doubtful, credence 0.30) does not cohere with what the graph records about these claims (reasoning_conflict): - 2e6a6348-a373-48b5-b7fa-f26a3c33…"},{"trigger":"mandate_plan","from":"a mandate's plan","sender_node_id":null,"sent_at":"2026-11-15T07:08:10.313Z","paid":true,"summary":"Reassess whether the execution-study evidence bears against the claim that current AI systems reliably generate validated novel experimental hypotheses. Distinguish relative advantage from absolute va…"}],"requests":[],"requests_total":0,"subclaim_count":2,"instance_count":1,"source_map":null,"arguments":[],"subclaims":[{"id":"474f2f6a-3f2c-4294-b232-86767bcbb595","edge_id":"cd273875-0d65-4b51-bc61-71cf0e51e8c3","text":"Hypotheses generated by Google's AI Co-Scientist were validated in laboratory experiments.","type":"empirical_derived","relation":"supports","reasoning":"Laboratory confirmation of AI-proposed biomedical hypotheses is evidence that AI can generate novel hypotheses that survive validation, weakened by expert selection from ranked candidate pools.","confidence":1,"assessment_status":"verified","assessment_confidence":0.85,"assessment_credence":0.93,"coverage":{"examined":true},"argument_id":null,"argument_name":null},{"id":"2e6a6348-a373-48b5-b7fa-f26a3c3342c5","edge_id":"4eff38d9-3fcb-402c-a59e-217ae928167a","text":"LLM-generated research ideas lose their advantage over expert ideas when actually executed.","type":"empirical_derived","relation":"contradicts","reasoning":"If LLM research ideas lose their advantage once executed, apparent novelty at proposal stage does not translate into ideas that reliably survive empirical execution.","confidence":1,"assessment_status":"supported","assessment_confidence":0.7,"assessment_credence":0.68,"coverage":{"examined":true},"argument_id":null,"argument_name":null}],"subclaims_assessed":2}