Step 2: tool results

get_claim

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    "arguments",
    "subclaims",
    "related"
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  "claim_id": "3f5f6119-5e2a-456c-930e-ecba1fc88447"
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{"claim":{"id":"3f5f6119-5e2a-456c-930e-ecba1fc88447","kind":"claim","canonical_form":"In evaluated long-horizon AI R&D tasks, frontier agents' strongest solutions mostly adapt or combine established techniques; methodological novelty is rare.","claim_type":"empirical_derived","state":"active","decomposition_status":"complete","importance":0.6,"contestation":0.55,"domains":[],"tags":[{"slug":"ai-research-automation","name":"AI Research Automation"},{"slug":"ai-capability-evaluation","name":"AI Capability Evaluation"},{"slug":"ai-agents","name":"AI Agents"}],"steward_state":"running","stewarded_at":"2026-11-14T03:03:28.709Z","canonical_direction_note":"This affirmative form states the reported pattern that agents' strongest task solutions generally reuse established methods rather than introducing novel methodology.","created_at":"2026-11-14T02:37:11.165Z","updated_at":"2026-11-14T03:03:20.279Z","page_url":"https://minerval.ai/claims/3f5f6119-5e2a-456c-930e-ecba1fc88447"},"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":"2026-11-14T02:37:11.242Z","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":"2026-11-14T03:03:20.309Z","paid":true,"summary":"First assessment. Two independent evaluations in the graph bear on this (an end-to-end test of whether AI can automate AI research, and a systematic evaluation of agents on long-horizon research tasks…"}],"requests":[],"requests_total":0,"subclaim_count":0,"instance_count":2,"source_map":null,"related":[],"arguments":[],"subclaims":[],"subclaims_assessed":0,"unplaced_argument_passages":[{"instance_id":"eefef591-31f3-4324-9b9f-8a2399138cd4","source":{"id":"aa9c085d-e59b-41f4-9954-36fe68cbad79","url":"https://epoch.ai/publications/innovationeval","title":"Can AI automate AI R&D yet?"},"verbatim_text":"Despite spending thousands of dollars on GPU usage, neither AI model achieved a result close to on-policy self-distillation, either conceptually or in terms of performance on metrics. GPT-5.6 Sol was the only model to achieve a (small) improvement on the key metrics.","context":"Results section","stance":"advances","direction":"for"},{"instance_id":"d090966e-defa-489a-b306-df7183c9a96c","source":{"id":"aa9c085d-e59b-41f4-9954-36fe68cbad79","url":"https://epoch.ai/publications/innovationeval","title":"Can AI automate AI R&D yet?"},"verbatim_text":"Here, the most noteworthy discovery from an uncontaminated model was GPT-5.6 Sol’s use of a self-imitation loss in a GRPO setting to derive signal from all-pass groups. Given its similarity to pre-existing ideas and weak performance, this would struggle to clear the bar of Moderately Interesting.","context":"Comparison to FrontierMath: Open Problems notability tiers","stance":"advances","direction":"for"}],"unplaced_argument_passages_total":2}