Step 6: tool results

get_claim

{
  "claim_id": "81eeb61b-4ce4-4d0b-891d-11e970d8f669"
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{"claim":{"id":"81eeb61b-4ce4-4d0b-891d-11e970d8f669","kind":"claim","canonical_form":"As of 2026, frontier AI agents can perform research engineering but struggle with critical stages of the AI research lifecycle.","claim_type":"empirical_derived","state":"active","decomposition_status":"complete","importance":0.6,"contestation":0.65,"domains":[],"tags":[{"slug":"ai-research-automation","name":"AI Research Automation"},{"slug":"ai-agents","name":"AI Agents"},{"slug":"artificial-intelligence","name":"Artificial intelligence"}],"steward_state":"done","stewarded_at":"2026-11-14T02:22:28.275Z","canonical_direction_note":"This states the positive, present-tense claim that agents struggle with critical research stages, which is the central proposition the source presents.","created_at":"2026-11-11T23:31:27.536Z","updated_at":"2026-11-14T02:23:09.908Z","page_url":"https://minerval.ai/claims/81eeb61b-4ce4-4d0b-891d-11e970d8f669"},"assessment":{"id":"4958af00-ada8-47bd-83af-9b569fe02db8","status":"supported","confidence":0.7,"claim_credence":0.82,"summary":"The claim has two halves. The first is that the best AI agents of 2026 can do the engineering work of AI research: implementing methods, running and debugging experiments, and producing a manuscript. The second is that they struggle at stages a research project cannot do without, such as choosing what to investigate, deciding what evidence would settle a question, and abandoning an approach that is failing. The evidence favours both halves, the first more firmly than the second.\n\nThe most direct test is a mid-2026 study. Agents were given the central questions of two unpublished NeurIPS submissions, six days, and several thousand dollars of compute, and the papers' own authors graded the results. Across three runs on two model families, the agents did the engineering with only logistical human help: literature review, GPU debugging, hundreds of experiments, a compiled paper. The graph's stronger claim that [[claim:27a33dd0-42b0-48e8-aead-a838e382a200|agents can autonomously complete the engineering work of AI research projects]] is judged supported on a capability reading, though not on a reading that requires reliable completion across typical projects. The first half of this claim asks less than that one, because \"can perform research engineering\" does not require whole-project autonomy or reliability. So it holds with more confidence. Both papers were scored as clear rejections, and [[claim:66eddda8-c9d1-4b53-8ed6-2a6055c68756|the logs show recurring failures]]: underpowered experiments on hand-picked or synthetic data, negative results presented as findings, no project-level change of course after the first day, and less than half the budget used. The study is small. Its graders knew the work was AI-generated, and it was run by a team that says it is known for doubting rapid AI self-improvement.\n\nIt does not stand alone. Anthropic has every commercial reason to describe its models as capable. It reports that they match or beat skilled humans at executing well-specified experiments, while large gaps persist in judgment about which goals to pursue. An independent October 2026 study of four open-ended AI research tasks found agents stronger, and improving faster, on engineering-heavy tasks than on conceptual ones. Benchmarks of judging research directions put models well below experienced researchers.\n\nTwo lines of evidence point the other way. First, a few AI-generated papers have passed peer review, and some system papers claim expert-level autonomous research. But [[claim:f0b9eb09-d082-4637-a588-036bf56e661d|acceptance at a noisy venue is weak evidence]], the acceptances are few and selected, and the strongest recent claims rest on automated reviewers. In the direct study, such reviewers accepted drafts that the human experts rejected. More serious is the pace of change. A benchmark published in October 2026 found the newest model exceeding recruited experts at designing and interpreting experiments on a fixed problem, which is one judgment-dependent stage. That does not show [[claim:df38f302-b17f-4b09-a84b-d373d108294c|research taste comparable to top researchers]] overall, and no open-ended evaluation of the models released in the last months of 2026 has been published. The claim is therefore well supported for the frontier through about mid-2026 and less certain for the newest systems. Direct evaluations of those systems on unpublished research questions, which the study's authors say they plan, would settle it.","reasoning_trace":"This pass was prompted by the reassessment of the stronger neighbouring claim that agents can autonomously complete the engineering work of AI research projects, from doubtful (0.3) to supported (0.6). The earlier assessment here was written before that change. It said the neighbour stood as doubtful on a pass that had not weighed the Kirgis et al. evidence. That description is now out of date and has been corrected. The verdict does not change.\n\nReconciling the two judgments. The neighbour reads \"can\" as demonstrated capability and rests mainly on the same study (Kirgis, Kapoor, Narayanan et al., arXiv 2607.27191v2, section 4.5): in all three runs the agents resolved environment problems themselves and carried out the experiments, and human interventions were logistical. The neighbour notes that on a reliability reading (typical projects, consistent completion) it would still be doubtful. This claim's first half, \"can perform research engineering,\" is weaker than the neighbour on both axes. It does not require whole-project autonomy, and it does not require reliability. So evidence for the neighbour carries over here, and doubts about the neighbour's reliability reading do not. The 0.6 on the neighbour and roughly 0.93 on this first half are jointly coherent, because this half is implied by the neighbour and also has independent support: saturated reproduction benchmarks, the developer's reports on well-specified experiment execution, and the engineering-heavy side of the independent four-task study. What the successful runs establish is the occasional, demonstrated completion of engineering on real research questions. They do not establish broad, reliable capability across projects, and this claim does not assert that.\n\nThe second half is unaffected by the neighbour's change. It rests on the expert-graded rejections, the recurring failure modes, the developer's admission of judgment gaps, and third-party taste and direction benchmarks, against the October 2026 TasteVal result for the newest model on fixed problems. Credence in the second half, read as covering late 2026, is about 0.87. The joint credence is about 0.82, as before.\n\nSources were as in the previous pass (Kirgis et al. read in full in sections 4 to 8; the Anthropic Institute report read in part; Jackson et al., October 2026, summary and methods; abstracts of TasteVal, rTaste, Taste-Bench and ScientistTwo). No new sources were opened in this pass, because the change was in the graph's judgment of a premise, not in the evidence.\n\nWhat would change the verdict: expert-graded, open-ended evaluations of late-2026 models that produce acceptable research, or evidence that the engineering success in the case studies depended on unreported human help.","marginal_yield":0.35,"assessed_at":"2026-11-14T02:22:54.470Z","model":"claude-opus-5-5","run_id":"67df04b5-c3ac-47ed-b4e1-898d55dd32c3","relabeled_from":null},"coverage":{"examined":true,"on":["public_evidence","evidence_not_shown"]},"since_assessed":{"recorded":true,"changed":true,"premises_rejudged":1,"premises_added":0,"premises_removed":0,"sources_added":2,"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":[],"requests":[],"requests_total":0,"subclaim_count":4,"instance_count":5,"source_map":{"summary":"The clearest statement of the claim comes from one study of two research questions, graded by the original papers' authors, from a team that discloses its scepticism about rapid AI self-improvement; it releases reviews and logs for one of the two papers. A developer's report says much the same about its own models from internal data it has not published, and its admission of a gap in judgment runs against its interest. The one recorded denial is a system paper by the system's developers that judges its output with automated reviewers.","material":true,"sources_considered":3,"sources_read":2,"edges_recorded":0,"mapped_by":"claim_steward","mapped_at":"2026-11-13T21:38:05.719Z"}}

get_claim

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  "claim_id": "a6037281-5051-4d1f-bd95-5546955a242d"
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{"claim":{"id":"a6037281-5051-4d1f-bd95-5546955a242d","kind":"quantity","canonical_form":"Date when a frontier AI company first has an AI matching top researchers' research taste and fully automating coding","claim_type":"empirical_derived","state":"active","decomposition_status":"complete","importance":0.8,"contestation":0.85,"domains":[],"tags":[{"slug":"ai-research-automation","name":"AI Research Automation"},{"slug":"ai-development-forecasting","name":"AI Development Forecasting"},{"slug":"ai-coding-capability","name":"AI Coding Capability"}],"steward_state":"done","stewarded_at":"2026-11-14T02:58:18.841Z","canonical_direction_note":"The quantity names the arrival date of the Superhuman AI Researcher milestone, while 2027 is this source's forecast for its value rather than part of the definition.","created_at":"2026-11-14T00:17:56.539Z","updated_at":"2026-11-14T02:58:14.558Z","page_url":"https://minerval.ai/claims/a6037281-5051-4d1f-bd95-5546955a242d"},"assessment":null,"coverage":{"examined":true,"on":["public_evidence"]},"since_assessed":{"recorded":true,"changed":false,"premises_rejudged":0,"premises_added":0,"premises_removed":0,"sources_added":0,"sources_removed":0,"source_events":0},"quantity":{"definition":{"measures":"The date (calendar year, fractional where sources give one) at which any frontier AI company first has internally, whether or not deployed, an AI system that fully automates coding and whose research taste (choosing and judging research directions and experiments) matches that of top human AI researchers: the \"Superhuman AI Researcher\" (SAR) milestone of the AI Futures Project's timelines model, which comes after its \"Automated Coder\" milestone.","population":"Frontier AI developers worldwide","conditions":"Dated by when the capability first exists inside a company, as later established; the milestone may never occur.","unit":{"min":2025,"kind":"year"}},"estimate":{"form":"distribution","shape":"Right-skewed: a near-term mass driven by recent acceleration evidence, a heavy tail into the late 2030s and beyond.","central":2030.3,"outside":{"share":0.08,"meaning":"The milestone is not reached by 2050 or ever (a capability plateau, a deliberate pause or regulation, or a catastrophe halting development)."},"quantiles":[{"p":0.1,"v":2027.6},{"p":0.25,"v":2028.5},{"p":0.5,"v":2030.3},{"p":0.75,"v":2034},{"p":0.9,"v":2040}]},"assessed":{"id":"45ef93ae-b3c4-4df8-8130-34e6cffdeb2b","summary":"The Superhuman AI Researcher milestone, an AI inside a frontier company that fully automates coding and matches top human researchers in research taste, has not been reached as of November 2026. Its most prominent forecasters, the six authors of the AI Futures Project, wrote on 10 November 2026 that they now all expect it in 2027 (individual medians May to August 2027), conditional on no substantial deliberate slowdown. Less than three months earlier their medians for the earlier Automated Coder milestone ran from mid-2028 to about 2030. The shift rests mainly on one METR estimate that Anthropic is making AI progress about twice its historical rate (plausible range 1.3x to 3x), roughly 80 percent attributed to AI, together with simple extrapolations (research taste, labour uplift, Anthropic automation levels) that place the milestone between mid-2027 and mid-2028.\n\nThis is real evidence of acceleration and moves the estimate earlier, but several things keep it from being taken at face value. The update swings a forecast by one to two years on a single, admittedly uncertain measurement whose attribution to AI is itself a guess; the authors call their analysis preliminary and could not explain why their own model was surprised. Research taste is the component least well measured, and extrapolations of self-reported uplift and automation share inputs, so their agreement is less independent than it looks. The authors also hold a stated advocacy position (an urgent pause), and their group has historically sat at the short end of expert forecasts, while forecasters outside it generally place transformative AI-research automation later. Finally, the milestone cannot precede the automated or superhuman coder milestone, whose estimate on this graph has a median around 2030 (assessed before this update).\n\nThe estimate here puts the median around 2030, with about 10 percent by mid-2027, about a quarter by mid-2028, a 90 percent interval running from 2027 to roughly 2040 within the outcomes where it happens, and about 8 percent on the milestone not being reached by 2050 or ever. Because the quantity dates internal existence, the year may only be established in retrospect.","work":"Inputs: (1) AI Futures Project, \"Q3.5 Timelines Update\" (10 Nov 2026, blog.aifutures.org): all six authors forecast SAR likely in 2027 assuming no substantial slowdown; AIFM filtered on 1.7x Nov 2026 AI-attributable acceleration gives SAR median Feb 2027; simple research-taste extrapolation July or Sep 2027; other simple models late 2027 to mid-2028; previously Daniel Kokotajlo (most bullish) gave 50% to SAR before 2029. (2) Coder-milestone quantity on this graph (assessed 2 Nov 2026): median 2030, 10th 2027, 25th 2028, 75th 2033, 90th 2038, 7% never; it summarises the authors' Aug 2026 AC medians of mid-2028 to ~2030.\nReasoning: SAR >= AC in time; in AIFM the AC-to-SAR gap is months to a couple of years depending on takeoff speed. Taking the coder distribution as base and adding a gap of ~0.5 to 1.5 years would give a SAR median ~2031; the new METR-based evidence argues for pulling earlier. I give the Q3.5 update partial weight (roughly a third to a half of its implied shift) because: single uncertain measurement (1.3x-3x range, 80% attribution 'perhaps'); preliminary, unexplained model miss; correlated extrapolations; forecaster group at the short end of the expert distribution and with a declared pause-advocacy stance; forecasts conditional on no slowdown, while this quantity is unconditional. Result: quantiles 0.1: 2027.6, 0.25: 2028.5, 0.5: 2030.3, 0.75: 2034, 0.9: 2040 (within-occurrence share), outside 8% for never or after 2050. Each quantile is at or after the corresponding coder quantile, keeping the two estimates coherent; if the coder estimate is pulled earlier on reassessment, this one should be revisited too. No subclaims added this pass; the main crux (how much the METR acceleration estimate should move timelines) is better mapped once the coder node's reassessment lands.","model":"claude-opus-5-5","run_id":"00728b1e-6164-4226-9f90-cfb69adca3b7","assessed_at":"2026-11-14T02:58:03.342Z"},"estimates":[{"id":"6676e08b-ca76-4457-aa8f-62ea1fa45d1d","argument_id":null,"source_title":"Q3.5 Timelines Update: Looks Like the Intelligence Explosion Will Happen in 2027 After All","source_url":"https://blog.aifutures.org/p/q35-timelines-update-looks-like-the","source_type":"blog_post","source_date":"2026-11-10","stated":{"low":2027,"form":"interval","high":2028},"verbatim_text":"that Superhuman AI Researcher (SAR) will likely arrive at some point in 2027"}],"readings":[],"cuts":[],"figures_not_yet_recorded":0,"marginal_yield":0.5},"threshold_of":null,"question":null,"formalization":null,"formalization_pending":null,"formalization_history":[],"verification":null,"lean_checks":[],"lean_checks_total":0,"waiting_messages":[],"requests":[],"requests_total":0,"subclaim_count":0,"instance_count":1,"source_map":null}

get_claim

{
  "claim_id": "e6e8a4fa-eee4-4fc3-b564-ea04a6313546"
}

{"claim":{"id":"e6e8a4fa-eee4-4fc3-b564-ea04a6313546","kind":"claim","canonical_form":"Progress toward automating AI research and development is likely to stall in the next few years.","claim_type":"empirical_derived","state":"active","decomposition_status":"complete","importance":0.7,"contestation":0.7,"domains":[],"tags":[{"slug":"ai-research-automation","name":"AI Research Automation"},{"slug":"ai-development-forecasting","name":"AI Development Forecasting"}],"steward_state":"done","stewarded_at":"2026-11-14T02:58:18.961Z","canonical_direction_note":"The affirmative direction records the forecast that progress toward AI R&D automation is likely to stall soon; the source rejects that likelihood and says measured capability trends have not yet bent.","created_at":"2026-11-13T22:22:32.610Z","updated_at":"2026-11-14T02:58:49.234Z","page_url":"https://minerval.ai/claims/e6e8a4fa-eee4-4fc3-b564-ea04a6313546"},"assessment":{"id":"c8c7b11d-eba7-4e6c-bfc6-11ba19079313","status":"doubtful","confidence":0.65,"claim_credence":0.2,"summary":"The claim forecasts that progress toward automating AI research and development will stall within the next few years. As of late 2026 the measured evidence weighs against it, though the case for an eventual slowdown is serious.\n\nThe strongest evidence against a near-term stall is that the capabilities most relevant to automated AI R&D are still improving on trend or faster. METR's measure of the length of software tasks models can complete, which doubled about every seven months from 2019 to 2024, has doubled roughly every three to four months since, and METR judged in October 2026 that the pace on well-specified tasks was about twice its 2024 to 2025 rate. Coding and research-reproduction benchmarks went from low scores to saturation within about two years, and Anthropic reports that more than 80% of its merged code is now written by its models. Anthropic's own account of the scenario in which the trend stalls says it does not consider it likely because no measured curve has yet bent; that account comes from a developer with a commercial interest in its models being seen as capable, but the external benchmark data it cites are independent of it. Compute capacity already financed and under construction runs through at least 2027 to 2028.\n\nThe case for a slowdown rests on diminishing returns. Economists Alex Imas and Luis Garicano estimate that capability gained per dollar of research spending and per technical employee has fallen even as calendar-time progress sped up, so that sustaining the pace requires fast-growing inputs; chips, power and capital bound that growth, and several projections have training-compute growth falling off its exponential around 2028 to 2030. A further open question is whether research judgment, the part of AI R&D least automated today, will yield to current methods. These considerations make a slowdown in the later 2020s plausible, but a slowdown is not a stall, and no measured trend shows one yet.\n\nOn balance a stall in the next few years is possible but not the likely outcome. Evidence that would change this: a sustained flattening of task-horizon or AI R&D benchmark trends through 2027, a contraction in frontier compute investment, or evidence that remaining gaps in research judgment resist scaling.","reasoning_trace":"Reading of the claim: \"stall\" is taken as progress toward automating AI R&D largely flattening (not merely slowing from an accelerated pace), within roughly 2027 to 2029; \"likely\" as more probable than not.\n\nPrior: forecasts of imminent AI plateaus have repeatedly failed since 2020 (scaling-wall claims of 2024 were followed by reasoning-model and agent gains). S-curves do eventually bend, but the timing of the bend is the hard part, and input build-out is committed for several years.\n\nEvidence against (argument from unbent capability trends):\n- Anthropic Institute, \"When AI builds itself\" (https://www.anthropic.com/institute/recursive-self-improvement): time horizons doubling about every four months; SWE-bench and CORE-Bench saturated within roughly two years; >80% of merged code Claude-authored as of May 2026; explicit statement that the stall scenario is not likely because \"We have not yet seen that curve bend.\" Interested source; internal figures are evidence not shown, but public benchmark figures are checkable.\n- METR Time Horizon 1.1 (Jan 2026) revised the post-2024 doubling to ~3 months; METR (as quoted by Imas and Garicano) estimated the Oct 2026 pace at ~2x the 2024-2025 pace, range 1.3x to 3x.\n- AI Futures Project's Q3 2026 update moved its timelines earlier, toward 2027.\n\nEvidence for (argument from diminishing returns and constrained inputs):\n- Imas and Garicano (Nov 13 2026, https://aleximas.substack.com/p/are-we-approaching-an-intelligence): capability gains per dollar fell about two-thirds and per employee-year about two-fifths; a break around Oct 2025 looks like a one-time step from coding agents rather than continuing acceleration; progress must eventually slow to what chips, power and capital allow. They do not date a stall or claim one is imminent.\n- Projections (Epoch-style compute forecasts, AI Futures Model) of compute and labour growth slowing around 2028-2030.\n- Possible bottleneck in research taste/judgment (acknowledged by Anthropic).\n- Possible exogenous shocks (compute or power supply, financing).\n\nWeighing: the for-argument establishes that a slowdown in growth rate is likely eventually and possibly by ~2030, but a stall within a few years requires both input growth to falter soon and no offsetting AI contribution to research; the latter is already partly visible. Credence ~0.2 that progress stalls in the next few years. Status doubtful rather than contradicted because the claim is a forecast and the evidence is indirect; \"stall\" is vague, which also limits confidence.","marginal_yield":0.4,"assessed_at":"2026-11-14T02:58:35.621Z","model":"claude-opus-5-5","run_id":"54568c34-e8a6-4b99-8710-350389e75a3f","relabeled_from":null},"coverage":{"examined":true,"on":["public_evidence"]},"since_assessed":{"recorded":true,"changed":false,"premises_rejudged":0,"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":[],"requests":[],"requests_total":0,"subclaim_count":0,"instance_count":2,"source_map":null}

get_claim

{
  "claim_id": "aa36d48a-c988-4991-a701-50102794ef74"
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{"claim":{"id":"aa36d48a-c988-4991-a701-50102794ef74","kind":"quantity","canonical_form":"Calendar year in which AI systems first fully automate AI research and development","claim_type":"empirical_derived","state":"active","decomposition_status":"complete","importance":0.85,"contestation":0.85,"domains":[],"tags":[{"slug":"ai-research-automation","name":"AI Research Automation"},{"slug":"ai-development-forecasting","name":"AI Development Forecasting"}],"steward_state":"running","stewarded_at":"2026-11-14T03:03:24.001Z","canonical_direction_note":"This quantity is the calendar year of full automation of AI research and development, rather than a date for general intelligence or automation of all labor.","created_at":"2026-11-14T02:42:23.266Z","updated_at":"2026-11-14T03:03:19.537Z","page_url":"https://minerval.ai/claims/aa36d48a-c988-4991-a701-50102794ef74"},"assessment":null,"coverage":{"examined":false,"on":[],"note":"No judgment of this node is in force. That is not a finding either way."},"quantity":{"definition":null,"estimate":null,"assessed":null,"estimates":[{"id":"b35825c8-4251-4496-9502-f54d36444d7e","argument_id":null,"source_title":"How Close Is Recursive Self-Improvement?","source_url":"https://forecastingresearch.substack.com/p/how-close-is-recursive-self-improvement","source_type":"blog_post","source_date":null,"stated":{"low":2027,"form":"interval","high":2052},"verbatim_text":"Forecasts of full automation of AI R&D range from 2027 (insiders at AI companies and the AI Futures Project’s AI 2027 scenario) to 2052 (the AI Impacts survey of ML researchers)."},{"id":"c68af3cc-a8a5-43e7-baeb-19f276cefb61","argument_id":null,"source_title":"Import AI 476: Creativity and RSI; worse-than-China open weight models; AI goes to space","source_url":"https://jack-clark.net/2026/10/12/import-ai-476-creativity-and-rsi-worse-than-china-open-weight-models-ai-goes-to-space/","source_type":"blog_post","source_date":"2026-10-12","stated":{"form":"point","central":2027},"verbatim_text":"I now think it’s more likely we’ll see full RSI become possible in 2027, whereas before I thought it was more likely in 2028."}],"readings":[],"cuts":[],"figures_not_yet_recorded":0,"marginal_yield":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":"2026-11-14T02:42:23.327Z","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":"2026-11-14T03:03:19.609Z","paid":true,"summary":"First estimate for this quantity. Sources in the graph state figures or spreads for it or for close variants (an independent forecasting institute's November 2026 synthesis, a lab co-founder's May and…"}],"requests":[],"requests_total":0,"subclaim_count":0,"instance_count":2,"source_map":null}

search_similar_claims

{
  "limit": 6,
  "query": "AI agents will complete week-long tasks autonomously by end of 2027"
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{
  "query": "AI agents will complete week-long tasks autonomously by end of 2027",
  "results": [
    {
      "id": "83df12e2-68f5-4d25-8327-28d24ab513be",
      "kind": "claim",
      "text": "By the end of 2027, AI systems will be able to complete tasks that take a skilled person weeks.",
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      "id": "27a33dd0-42b0-48e8-aead-a838e382a200",
      "kind": "claim",
      "text": "As of 2026, frontier AI agents can autonomously complete the engineering work of AI research projects.",
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      "id": "c14e4dbe-a52f-40cb-bf38-4667b68afd85",
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      "text": "AI-agent work performed at OpenAI in August 2026, measured in human workdays per human workday",
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      "similarity_score": 0.6869054090149788,
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      "id": "8dc0827d-5e18-4e56-8f0b-9516bbde379b",
      "kind": "claim",
      "text": "At least one AI lab will fully automate its AI research by the end of 2027.",
      "claim_type": "empirical_derived",
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      "similarity_score": 0.6715318147798972,
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      "id": "f714e062-8b4c-4bb8-9f3d-38e74f68d06a",
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      "text": "All major AI labs will have fully automated their AI research by the end of 2027.",
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      "id": "da087b63-9f41-43a1-b6b3-d6d804623bb2",
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      "text": "Reliable AI agents will substantially advance AI research by the end of 2025.",
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      "similarity_score": 0.6627449389028353,
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search_similar_claims

{
  "limit": 6,
  "query": "An AI model will train its own successor without human involvement by end of 2027"
}

{
  "query": "An AI model will train its own successor without human involvement by end of 2027",
  "results": [
    {
      "id": "55f89371-8a43-43c1-8dd7-932c350ca7fb",
      "kind": "claim",
      "text": "By the end of 2027, a frontier AI model will autonomously train a successor version of itself.",
      "claim_type": "empirical_verifiable",
      "state": "active",
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