{"id":"5ac0a9f6360b9b5534016ab682af27e7159f2b48a1e935b39bfa342fe2d1149c","title":"Per Kristian Lehre’s pipeline uses Lean to prove runtime bounds for proposed algorithms","argument":"Per Kristian Lehre presents a staged pipeline in which an AI agent proposes black-box optimisation algorithms, then uses Lean—a proof assistant—to formalise and check mathematical proofs about their expected runtime. The paper reports that 12 of 15 runs across 8 problem classes completed with a machine-checked expected-runtime bound. In one example, the proposed algorithm has expected runtime O(n²) on CycleUncover, while the paper says the classical (1+1) EA needs e^Ω(n). The authors did not compare algorithms empirically; they argue that machine-checked specifications could take the place of empirical evaluation in open-ended algorithm discovery. The results concern the paper’s stated problems and pipeline, not a general demonstration that AI-designed algorithms are efficient in practice. What further testing would show whether these proved runtime bounds translate into useful performance on real optimisation tasks?","topicIds":["intelligence-and-discovery"],"domain":"science","claimType":"attributed reading","limits":"The paper says the algorithms are not compared empirically, so it does not establish how they perform in practice. The reported results concern the problem classes and runs described in the abstract; the evidence here does not show whether the approach generalises to other tasks.","speaker":{"name":"Lehre, Per Kristian","roleAtSource":"Article author"},"source":{"title":"Agentic Algorithm Design: LLMs that Propose, Formalise, and Prove Runtime Bounds for Evolutionary Algorithms in Lean","publisher":"arXiv cs.AI","format":"Article","url":"https://arxiv.org/abs/2610.35595","publishedAt":"2026-10-26","dateBasis":"Date basis not provided."},"evidence":[{"text":"Per Kristian Lehre presents a staged pipeline in which an agent proposes black-box optimisation algorithms and Lean is used to check formal proofs about their expected runtime.","excerpt":"The agent proposes black-box optimisation algorithms, and Lean guards filter out flawed designs.","url":"https://arxiv.org/abs/2610.35595","relationship":"Source-stated"},{"text":"The paper reports that 12 of 15 runs across 8 problem classes completed the pipeline with machine-checked expected-runtime bounds.","excerpt":"Across 8 problem classes, 12 of 15 runs with frontier models complete the pipeline with a machine-checked expected-runtime bound.","url":"https://arxiv.org/abs/2610.35595","relationship":"Source-stated"},{"text":"For CycleUncover, the paper reports an expected runtime of O(n²) for an agent-generated algorithm, compared with e^Ω(n) for the classical (1+1) EA.","excerpt":"The agent generates a black-box optimisation algorithm with expected runtime $O(n^2)$ on a CycleUncover problem, where the classical (1+1) EA needs runtime $e^{\\Omega(n)}$.","url":"https://arxiv.org/abs/2610.35595","relationship":"Source-stated"}],"sourceMedia":{"status":"not_found","sourceUrl":"https://arxiv.org/abs/2610.35595","checkedAt":"2026-11-10T20:48:10.960Z","items":[],"note":"No suitable media was found by the supported page extraction."},"assumptions":[],"openQuestion":{"attribution":"Open question","text":"What further testing would show whether these proved runtime bounds translate into useful performance on real optimisation tasks?"},"whatWouldChangeView":{"attribution":"Change condition not recorded","text":""},"editorialComparisons":[],"live":{"title":"Per Kristian Lehre’s pipeline uses Lean to prove runtime bounds for proposed algorithms","summary":"Per Kristian Lehre presents a staged pipeline in which an AI agent proposes black-box optimisation algorithms, then uses Lean—a proof assistant—to formalise and check mathematical proofs about their expected runtime. The paper reports that 12 of 15 runs across 8 problem classes completed with a machine-checked expected-runtime bound. In one example, the proposed algorithm has expected runtime O(n²) on CycleUncover, while the paper says the classical (1+1) EA needs e^Ω(n). The authors did not compare algorithms empirically; they argue that machine-checked specifications could take the place of empirical evaluation in open-ended algorithm discovery. The results concern the paper’s stated problems and pipeline, not a general demonstration that AI-designed algorithms are efficient in practice. What further testing would show whether these proved runtime bounds translate into useful performance on real optimisation tasks?","question":"What further testing would show whether these proved runtime bounds translate into useful performance on real optimisation tasks?","claims":[{"text":"Per Kristian Lehre presents a staged pipeline in which an agent proposes black-box optimisation algorithms and Lean is used to check formal proofs about their expected runtime.","excerpt":"The agent proposes black-box optimisation algorithms, and Lean guards filter out flawed designs.","support":"source-stated"},{"text":"The paper reports that 12 of 15 runs across 8 problem classes completed the pipeline with machine-checked expected-runtime bounds.","excerpt":"Across 8 problem classes, 12 of 15 runs with frontier models complete the pipeline with a machine-checked expected-runtime bound.","support":"source-stated"},{"text":"For CycleUncover, the paper reports an expected runtime of O(n²) for an agent-generated algorithm, compared with e^Ω(n) for the classical (1+1) EA.","excerpt":"The agent generates a black-box optimisation algorithm with expected runtime $O(n^2)$ on a CycleUncover problem, where the classical (1+1) EA needs runtime $e^{\\Omega(n)}$.","support":"source-stated"}],"limitations":["The paper says the algorithms are not compared empirically, so it does not establish how they perform in practice.","The reported results concern the problem classes and runs described in the abstract; the evidence here does not show whether the approach generalises to other tasks."],"mediaId":"239282ae28cf8a0cd302190514ff6dea2e013165d48595eff109375ca15f6de3","correctionReason":null,"id":"5ac0a9f6360b9b5534016ab682af27e7159f2b48a1e935b39bfa342fe2d1149c","publicationId":"3ba88414-d485-4c6b-8032-3c65f999f1df","version":1,"sourceUrl":"https://arxiv.org/abs/2610.35595","sourceTitle":"Agentic Algorithm Design: LLMs that Propose, Formalise, and Prove Runtime Bounds for Evolutionary Algorithms in Lean","publisher":"arXiv cs.AI","publishedAt":null,"originalPublished":{"value":"2026-10-26","precision":"day","basis":"publisher_citation_date"},"firstObservedAt":"2026-10-28T08:54:33.236Z","createdAt":"2026-11-10T20:17:58.049Z","historical":true,"entityIds":["arxiv"],"topicIds":["intelligence-and-discovery"],"media":[{"id":"239282ae28cf8a0cd302190514ff6dea2e013165d48595eff109375ca15f6de3","kind":"article","url":"https://arxiv.org/abs/2610.35595","sourceUrl":"https://arxiv.org/abs/2610.35595","label":"Read original","provenance":"source_link","verification":{"status":"verified","checkedAt":"2026-10-30T00:47:09.991Z","method":"acquired_source","contentType":"text/html"},"recommendationReason":"Recommended by Jev for understanding this source; checked by Luna."}],"sourceMedia":{"status":"not_found","sourceUrl":"https://arxiv.org/abs/2610.35595","checkedAt":"2026-11-10T20:48:10.960Z","items":[],"note":"No suitable media was found by the supported page extraction."},"editorialDecision":{"outcome":"accept","reasonCodes":["material_development"],"rubricVersion":"stream-admission-v5","mediaId":"239282ae28cf8a0cd302190514ff6dea2e013165d48595eff109375ca15f6de3","confidence":0.88,"provider":"typesafe","model":"jev-1.13.0","inputHash":"97ca31813ffedaa354212b3f90e36f068f30102da52ed1adba449b9ee519a0c1","decidedAt":"2026-10-30T00:47:10.536Z"},"provenance":{"textHash":"aed03f1600e22a93d4403dbfc514ccf9c0402589db86c5fa98017e10706fd643","observationId":"b60f18068dbc71e0fabbe590ff061bba480fa2861645932b8d10eb68caadc279","retrievedAt":"2026-10-30T00:47:09.890Z","kind":"publisher-page","preparer":{"model":"server-excerpt-repair/stream-excerpt-repair-v1 (original draft: gpt-6-luna)","runId":"597400b0-f132-4e62-8ec7-2c599867a052"},"reviewer":{"model":"gpt-6-luna","runId":"f47db8d3-9d6e-4f8d-87d6-47a4a8a0810b"}},"audit":{"status":"uncertain","reviewedAt":"2026-11-10T20:17:58.049Z","model":"anthropic/claude-opus-5.5"},"sourceAttribution":{"authors":[{"name":"Lehre, Per Kristian","url":null,"kind":"unknown"}],"authorRole":"article-author","hosts":[],"status":"stated","authorRoster":{"truncated":false,"omitted":0,"assertionGroupsOmitted":0},"participation":[{"actor":{"name":"Lehre, Per Kristian","url":null,"kind":"unknown","actorId":null},"role":"author","scope":{"kind":"work-credit","locator":null},"literalCredit":"Lehre, Per Kristian","status":"stated","provenance":{"basis":"head-meta","sourceUrl":"https://arxiv.org/abs/2610.35595","locator":"head/meta[name=author|citation_author]"},"assertionId":"879ca5b83a3533533a52a14bc0a64ba603eabd320b8d5c5a3fbd6672bef20fc2:participation:0","identity":{"version":"entity-identity-v1","status":"unresolved","actorId":null,"canonicalActorId":null,"candidateActorIds":[],"assertions":[],"sourceObservedAt":"2026-11-10T20:48:10.960Z","knowledgeAsOf":null},"sourceVersion":{"snapshotId":"879ca5b83a3533533a52a14bc0a64ba603eabd320b8d5c5a3fbd6672bef20fc2","sourceUrl":"https://arxiv.org/abs/2610.35595","contentSha256":"903a9eaa2eaf41da54b6b9c8ce1218369b338a734eaec9c52711402e27ffdc01","textHash":"aed03f1600e22a93d4403dbfc514ccf9c0402589db86c5fa98017e10706fd643","observedAt":"2026-11-10T20:48:10.960Z"}}],"participationCoverage":{"total":1,"returned":1,"complete":true,"fullAssertions":"source-metadata-endpoint"},"distribution":null,"publisher":null,"sourceUrl":"https://arxiv.org/abs/2610.35595","snapshotId":"879ca5b83a3533533a52a14bc0a64ba603eabd320b8d5c5a3fbd6672bef20fc2","contentSha256":"903a9eaa2eaf41da54b6b9c8ce1218369b338a734eaec9c52711402e27ffdc01","textHash":"aed03f1600e22a93d4403dbfc514ccf9c0402589db86c5fa98017e10706fd643","observedAt":"2026-11-10T20:48:10.960Z","basis":"publisher-page-metadata","sourceLinks":42,"linksTruncated":false,"metadataUrl":"/api/pipeline/stream/5ac0a9f6360b9b5534016ab682af27e7159f2b48a1e935b39bfa342fe2d1149c/source"}},"canonicalUrl":"https://escapemeridian.com/readings/per-kristian-lehre-s-pipeline-uses-lean-to-prove-runtime-bounds-5ac0a9f6","formats":{"html":"/readings/per-kristian-lehre-s-pipeline-uses-lean-to-prove-runtime-bounds-5ac0a9f6","markdown":"/readings/per-kristian-lehre-s-pipeline-uses-lean-to-prove-runtime-bounds-5ac0a9f6.md","json":"/readings/per-kristian-lehre-s-pipeline-uses-lean-to-prove-runtime-bounds-5ac0a9f6.json"},"attribution":"Headline and summary by Escape Meridian, prepared by Luna from the attributed source. A source check is not independent confirmation of the source’s claims.","maintainedGuideLinks":[],"readerContext":{"version":"reader-context-v1","attribution":"Reference context selected by Escape Meridian; separate from source evidence. Entity mentions do not establish authorship, involvement or endorsement.","terms":[{"id":"term-ai-agent","name":"AI agent","explanation":"An AI system that chooses and carries out actions toward a goal, often using software tools.","referenceUrl":"https://www.ibm.com/think/topics/ai-agents"},{"id":"term-formal-verification","name":"Formal verification","explanation":"Using software to check every step of a proof or a program against precise rules.","referenceUrl":"https://en.wikipedia.org/wiki/Formal_verification"}],"entities":[{"id":"lean","name":"Lean","kind":"concept","context":"Lean is a programming language and proof assistant used to express mathematical statements and check formal proofs.","referenceUrls":["https://lean-lang.org/","https://lean-lang.org/theorem_proving_in_lean4/Axioms-and-Computation/"],"path":"/entities/lean","json":"/entities/lean.json","markdown":"/entities/lean.md"},{"id":"arxiv","name":"arXiv","kind":"organization","context":"arXiv is a research manuscript repository where authors share papers that may not yet have been peer reviewed.","referenceUrls":["https://arxiv.org/","https://info.arxiv.org/help/api/index.html"],"path":"/entities/arxiv","json":"/entities/arxiv.json","markdown":"/entities/arxiv.md"}]},"researchQuestions":[],"withdrawn":false,"references":{"schemaVersion":1,"target":{"type":"reading","id":"5ac0a9f6360b9b5534016ab682af27e7159f2b48a1e935b39bfa342fe2d1149c","revision":"3ba88414-d485-4c6b-8032-3c65f999f1df","revisionKind":"publication-id","title":"Per Kristian Lehre’s pipeline uses Lean to prove runtime bounds for proposed algorithms","url":"https://escapemeridian.com/readings/per-kristian-lehre-s-pipeline-uses-lean-to-prove-runtime-bounds-5ac0a9f6"},"source":{"type":"source","id":"https://arxiv.org/abs/2610.35595","revision":"aed03f1600e22a93d4403dbfc514ccf9c0402589db86c5fa98017e10706fd643","revisionKind":"sha256-source-text","title":"Agentic Algorithm Design: LLMs that Propose, Formalise, and Prove Runtime Bounds for Evolutionary Algorithms in Lean","url":"https://arxiv.org/abs/2610.35595"},"status":"available","items":[],"next":null,"scope":"Explicit citations in public readings and public file attachments to this reading. Shared topics are not citations. Other artifact types need an explicit reference adapter."},"revisions":[{"id":"5ac0a9f6360b9b5534016ab682af27e7159f2b48a1e935b39bfa342fe2d1149c","title":"Per Kristian Lehre’s pipeline uses Lean to prove runtime bounds for proposed algorithms","argument":"Per Kristian Lehre presents a staged pipeline in which an AI agent proposes black-box optimisation algorithms, then uses Lean—a proof assistant—to formalise and check mathematical proofs about their expected runtime. The paper reports that 12 of 15 runs across 8 problem classes completed with a machine-checked expected-runtime bound. In one example, the proposed algorithm has expected runtime O(n²) on CycleUncover, while the paper says the classical (1+1) EA needs e^Ω(n). The authors did not compare algorithms empirically; they argue that machine-checked specifications could take the place of empirical evaluation in open-ended algorithm discovery. The results concern the paper’s stated problems and pipeline, not a general demonstration that AI-designed algorithms are efficient in practice. What further testing would show whether these proved runtime bounds translate into useful performance on real optimisation tasks?","topicIds":["intelligence-and-discovery"],"domain":"science","claimType":"attributed reading","limits":"The paper says the algorithms are not compared empirically, so it does not establish how they perform in practice. The reported results concern the problem classes and runs described in the abstract; the evidence here does not show whether the approach generalises to other tasks.","speaker":{"name":"Lehre, Per Kristian","roleAtSource":"Article author"},"source":{"title":"Agentic Algorithm Design: LLMs that Propose, Formalise, and Prove Runtime Bounds for Evolutionary Algorithms in Lean","publisher":"arXiv cs.AI","format":"Article","url":"https://arxiv.org/abs/2610.35595","publishedAt":"2026-10-26","dateBasis":"Date basis not provided."},"evidence":[{"text":"Per Kristian Lehre presents a staged pipeline in which an agent proposes black-box optimisation algorithms and Lean is used to check formal proofs about their expected runtime.","excerpt":"The agent proposes black-box optimisation algorithms, and Lean guards filter out flawed designs.","url":"https://arxiv.org/abs/2610.35595","relationship":"Source-stated"},{"text":"The paper reports that 12 of 15 runs across 8 problem classes completed the pipeline with machine-checked expected-runtime bounds.","excerpt":"Across 8 problem classes, 12 of 15 runs with frontier models complete the pipeline with a machine-checked expected-runtime bound.","url":"https://arxiv.org/abs/2610.35595","relationship":"Source-stated"},{"text":"For CycleUncover, the paper reports an expected runtime of O(n²) for an agent-generated algorithm, compared with e^Ω(n) for the classical (1+1) EA.","excerpt":"The agent generates a black-box optimisation algorithm with expected runtime $O(n^2)$ on a CycleUncover problem, where the classical (1+1) EA needs runtime $e^{\\Omega(n)}$.","url":"https://arxiv.org/abs/2610.35595","relationship":"Source-stated"}],"sourceMedia":{"status":"not_found","sourceUrl":"https://arxiv.org/abs/2610.35595","checkedAt":"2026-11-10T20:48:10.960Z","items":[],"note":"No suitable media was found by the supported page extraction."},"assumptions":[],"openQuestion":{"attribution":"Open question","text":"What further testing would show whether these proved runtime bounds translate into useful performance on real optimisation tasks?"},"whatWouldChangeView":{"attribution":"Change condition not recorded","text":""},"editorialComparisons":[],"live":{"title":"Per Kristian Lehre’s pipeline uses Lean to prove runtime bounds for proposed algorithms","summary":"Per Kristian Lehre presents a staged pipeline in which an AI agent proposes black-box optimisation algorithms, then uses Lean—a proof assistant—to formalise and check mathematical proofs about their expected runtime. The paper reports that 12 of 15 runs across 8 problem classes completed with a machine-checked expected-runtime bound. In one example, the proposed algorithm has expected runtime O(n²) on CycleUncover, while the paper says the classical (1+1) EA needs e^Ω(n). The authors did not compare algorithms empirically; they argue that machine-checked specifications could take the place of empirical evaluation in open-ended algorithm discovery. The results concern the paper’s stated problems and pipeline, not a general demonstration that AI-designed algorithms are efficient in practice. What further testing would show whether these proved runtime bounds translate into useful performance on real optimisation tasks?","question":"What further testing would show whether these proved runtime bounds translate into useful performance on real optimisation tasks?","claims":[{"text":"Per Kristian Lehre presents a staged pipeline in which an agent proposes black-box optimisation algorithms and Lean is used to check formal proofs about their expected runtime.","excerpt":"The agent proposes black-box optimisation algorithms, and Lean guards filter out flawed designs.","support":"source-stated"},{"text":"The paper reports that 12 of 15 runs across 8 problem classes completed the pipeline with machine-checked expected-runtime bounds.","excerpt":"Across 8 problem classes, 12 of 15 runs with frontier models complete the pipeline with a machine-checked expected-runtime bound.","support":"source-stated"},{"text":"For CycleUncover, the paper reports an expected runtime of O(n²) for an agent-generated algorithm, compared with e^Ω(n) for the classical (1+1) EA.","excerpt":"The agent generates a black-box optimisation algorithm with expected runtime $O(n^2)$ on a CycleUncover problem, where the classical (1+1) EA needs runtime $e^{\\Omega(n)}$.","support":"source-stated"}],"limitations":["The paper says the algorithms are not compared empirically, so it does not establish how they perform in practice.","The reported results concern the problem classes and runs described in the abstract; the evidence here does not show whether the approach generalises to other tasks."],"mediaId":"239282ae28cf8a0cd302190514ff6dea2e013165d48595eff109375ca15f6de3","correctionReason":null,"id":"5ac0a9f6360b9b5534016ab682af27e7159f2b48a1e935b39bfa342fe2d1149c","publicationId":"3ba88414-d485-4c6b-8032-3c65f999f1df","version":1,"sourceUrl":"https://arxiv.org/abs/2610.35595","sourceTitle":"Agentic Algorithm Design: LLMs that Propose, Formalise, and Prove Runtime Bounds for Evolutionary Algorithms in Lean","publisher":"arXiv cs.AI","publishedAt":null,"originalPublished":{"value":"2026-10-26","precision":"day","basis":"publisher_citation_date"},"firstObservedAt":"2026-10-28T08:54:33.236Z","createdAt":"2026-11-10T20:17:58.049Z","historical":true,"entityIds":["arxiv"],"topicIds":["intelligence-and-discovery"],"media":[{"id":"239282ae28cf8a0cd302190514ff6dea2e013165d48595eff109375ca15f6de3","kind":"article","url":"https://arxiv.org/abs/2610.35595","sourceUrl":"https://arxiv.org/abs/2610.35595","label":"Read original","provenance":"source_link","verification":{"status":"verified","checkedAt":"2026-10-30T00:47:09.991Z","method":"acquired_source","contentType":"text/html"},"recommendationReason":"Recommended by Jev for understanding this source; checked by Luna."}],"sourceMedia":{"status":"not_found","sourceUrl":"https://arxiv.org/abs/2610.35595","checkedAt":"2026-11-10T20:48:10.960Z","items":[],"note":"No suitable media was found by the supported page extraction."},"editorialDecision":{"outcome":"accept","reasonCodes":["material_development"],"rubricVersion":"stream-admission-v5","mediaId":"239282ae28cf8a0cd302190514ff6dea2e013165d48595eff109375ca15f6de3","confidence":0.88,"provider":"typesafe","model":"jev-1.13.0","inputHash":"97ca31813ffedaa354212b3f90e36f068f30102da52ed1adba449b9ee519a0c1","decidedAt":"2026-10-30T00:47:10.536Z"},"provenance":{"textHash":"aed03f1600e22a93d4403dbfc514ccf9c0402589db86c5fa98017e10706fd643","observationId":"b60f18068dbc71e0fabbe590ff061bba480fa2861645932b8d10eb68caadc279","retrievedAt":"2026-10-30T00:47:09.890Z","kind":"publisher-page","preparer":{"model":"server-excerpt-repair/stream-excerpt-repair-v1 (original draft: gpt-6-luna)","runId":"597400b0-f132-4e62-8ec7-2c599867a052"},"reviewer":{"model":"gpt-6-luna","runId":"f47db8d3-9d6e-4f8d-87d6-47a4a8a0810b"}},"audit":{"status":"uncertain","reviewedAt":"2026-11-10T20:17:58.049Z","model":"anthropic/claude-opus-5.5"},"sourceAttribution":{"authors":[{"name":"Lehre, Per Kristian","url":null,"kind":"unknown"}],"authorRole":"article-author","hosts":[],"status":"stated","authorRoster":{"truncated":false,"omitted":0,"assertionGroupsOmitted":0},"participation":[{"actor":{"name":"Lehre, Per Kristian","url":null,"kind":"unknown","actorId":null},"role":"author","scope":{"kind":"work-credit","locator":null},"literalCredit":"Lehre, Per Kristian","status":"stated","provenance":{"basis":"head-meta","sourceUrl":"https://arxiv.org/abs/2610.35595","locator":"head/meta[name=author|citation_author]"},"assertionId":"879ca5b83a3533533a52a14bc0a64ba603eabd320b8d5c5a3fbd6672bef20fc2:participation:0","identity":{"version":"entity-identity-v1","status":"unresolved","actorId":null,"canonicalActorId":null,"candidateActorIds":[],"assertions":[],"sourceObservedAt":"2026-11-10T20:48:10.960Z","knowledgeAsOf":null},"sourceVersion":{"snapshotId":"879ca5b83a3533533a52a14bc0a64ba603eabd320b8d5c5a3fbd6672bef20fc2","sourceUrl":"https://arxiv.org/abs/2610.35595","contentSha256":"903a9eaa2eaf41da54b6b9c8ce1218369b338a734eaec9c52711402e27ffdc01","textHash":"aed03f1600e22a93d4403dbfc514ccf9c0402589db86c5fa98017e10706fd643","observedAt":"2026-11-10T20:48:10.960Z"}}],"participationCoverage":{"total":1,"returned":1,"complete":true,"fullAssertions":"source-metadata-endpoint"},"distribution":null,"publisher":null,"sourceUrl":"https://arxiv.org/abs/2610.35595","snapshotId":"879ca5b83a3533533a52a14bc0a64ba603eabd320b8d5c5a3fbd6672bef20fc2","contentSha256":"903a9eaa2eaf41da54b6b9c8ce1218369b338a734eaec9c52711402e27ffdc01","textHash":"aed03f1600e22a93d4403dbfc514ccf9c0402589db86c5fa98017e10706fd643","observedAt":"2026-11-10T20:48:10.960Z","basis":"publisher-page-metadata","sourceLinks":42,"linksTruncated":false,"metadataUrl":"/api/pipeline/stream/5ac0a9f6360b9b5534016ab682af27e7159f2b48a1e935b39bfa342fe2d1149c/source"}}}],"audits":[{"publicationId":"3ba88414-d485-4c6b-8032-3c65f999f1df","verdict":"uncertain","findings":[{"dimension":"attribution","status":"pass","explanation":"The paper is correctly attributed to Per Kristian Lehre, its sole listed author. The summary later says \"The authors did not compare\", which is a minor plural slip; the smallest repair is \"The author\" or \"The pipeline does not\". The findings remain attributed to the paper rather than presented as fact.","excerpt":"Authors:Per Kristian Lehre"},{"dimension":"summary","status":"uncertain","explanation":"The reading captures the pipeline, the 12 of 15 result, the absence of empirical comparison and the thesis that formal proofs could replace empirical evaluation. However, the headline comparison, O(n²) versus e^Ω(n), means nothing to a non-specialist without explanation. A general reader cannot tell that this contrasts polynomial with exponential growth. Black-box optimisation, the (1+1) EA and CycleUncover are also left unexplained. Smallest repair: add a plain gloss, or term-library tooltips, saying the classical method's runtime grows exponentially with problem size while the new algorithm's grows polynomially.","excerpt":"where the classical (1+1) EA needs runtime $e^{\\Omega(n)}$"},{"dimension":"date","status":"pass","explanation":"originalPublished is 2026-10-26, which matches the v1 submission date. It is not the date of the v2 revision on 27 Oct or the feed entry.","excerpt":"Submitted on 26 Oct 2026 (v1)"},{"dimension":"support","status":"pass","explanation":"Each claim matches the abstract: the staged propose-then-prove pipeline with Lean guards, 12 of 15 runs across 8 classes, and the CycleUncover bounds. The no-empirical-comparison point and the replacement thesis are source-stated and kept conditional. The limitations and the question follow from the source without inventing benefits or costs.","excerpt":"Our study suggests that machine-checked specifications in Lean can replace empirical evaluation in open-ended algorithm discovery."},{"dimension":"media","status":"pass","explanation":"The selected media is the verified arXiv abstract page for this paper. It is the direct original source.","excerpt":null}],"createdAt":"2026-11-10T20:17:58.049Z","model":"anthropic/claude-opus-5.5"}],"correctionUrl":"https://escapemeridian.com/corrections/"}