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Acquired method/framework is distinct from claiming every generated candidate is mathematically novel or correct; reported packing/optimization improvements require task-specific independent checks. No search/program execution was run here.", "domain": "LLM-guided algorithm-search systems" }, "sources": [ { "citation": "ShinkaEvolve: Towards Open-Ended And Sample-Efficient Program Evolution", "url": "https://arxiv.org/abs/2509.19349v1" }, { "citation": "Lange et al.: novelty-filtered sample-efficient program evolution — author/institutional project", "url": "https://sakana.ai/shinka-evolve/" } ], "statement": "# Lange et al.: novelty-filtered sample-efficient program evolution\n\nCombined archive-parent sampling, code-novelty rejection and bandit-based model selection to allocate program-evolution attempts, testing sample efficiency across computational tasks.\n\n## Scientific scope\n\nEvolutionary program search and LLM mutation are inherited. Acquired method/framework is distinct from claiming every generated candidate is mathematically novel or correct; reported packing/optimization improvements require task-specific independent checks. No search/program execution was run here.\n\n## Reading status\n\nThis is a source-linked WRS catalogue note. A full explanatory guide and independent scientific reconstruction have not been completed for this entry. Read the identified original work for its complete narrative and arguments.\n\n## People and systems\n\nRobert Tjarko Lange; Yuki Imajuku; Edoardo Cetin\n\nSource-listed authors or explicitly credited technical contributors for the cited versions. Family entries retain source-specific attribution; a combined list is not one original-paper byline or equal individual work. Names alone do not establish resolved identities.\n\n\n\n## Original sources\n\n- [ShinkaEvolve: Towards Open-Ended And Sample-Efficient Program Evolution](https://arxiv.org/abs/2509.19349v1)\n- [Lange et al.: novelty-filtered sample-efficient program evolution — author/institutional project](https://sakana.ai/shinka-evolve/)\n", "summary": "Combined archive-parent sampling, code-novelty rejection and bandit-based model selection to allocate program-evolution attempts, testing sample efficiency across computational tasks.", "title": "Lange et al.: novelty-filtered sample-efficient program evolution" }, "record_id": "urn:uuid:4b17d1c9-de55-41cf-ae4f-2f69d8f7d709", "version": 1, "source_ref": { "record_id": "urn:uuid:4b17d1c9-de55-41cf-ae4f-2f69d8f7d709", "version": 1, "content_hash": "sha256:0d2759ffe80030f944a7ec418816f038d9e68cc5fba4d4d25c67918a8908ecb8" }, "export_view": "wrs-public-editorial-view-1", "public_view_hash": "sha256:7dad073b242119e4fa76e907c8621bd6501322f2efa52052dc3e4591f568f3db" }, "target": { "record_id": "urn:uuid:4b17d1c9-de55-41cf-ae4f-2f69d8f7d709", "version": 1, "content_hash": "sha256:0d2759ffe80030f944a7ec418816f038d9e68cc5fba4d4d25c67918a8908ecb8" }, "published_grade": { "profile": "wrs-published-grade-1", "edition": "2026-10-28.1", "id": "lange-et-al-shinka-program-evolution-2025", "entry_id": "lange-et-al-shinka-program-evolution-2025", "revision": 1, "title": "Lange et al.: novelty-filtered sample-efficient program evolution", "context": "LLM-guided algorithm-search systems", "track": "Human-designed research method or resource", "band": "B", "modifier": "+", "status": "PROVISIONAL_EDITORIAL", "assessed_on": "2026-10-16", "evidence_cutoff": "2026-10-16", "achievement_state": "SUPPORTED_SCOPED_RESULT", "what_changed": "Combined archive-parent sampling, code-novelty rejection and bandit-based model selection to allocate program-evolution attempts, testing sample efficiency across computational tasks.", "scope": "Combined archive-parent sampling, code-novelty rejection and bandit-based model selection to allocate program-evolution attempts, testing sample efficiency across computational tasks. Evolutionary program search and LLM mutation are inherited. Acquired method/framework is distinct from claiming every generated candidate is mathematically novel or correct; reported packing/optimization improvements require task-specific independent checks. No search/program execution was run here.", "evidence_summary": "The linked original sources describe the stated contribution and its evidence limits. WRS has not independently rerun the experiments or certified a proof. Evolutionary program search and LLM mutation are inherited. Acquired method/framework is distinct from claiming every generated candidate is mathematically novel or correct; reported packing/optimization improvements require task-specific independent checks. No search/program execution was run here.", "references": [ { "title": "ShinkaEvolve: Towards Open-Ended And Sample-Efficient Program Evolution", "url": "https://arxiv.org/abs/2509.19349v1", "locator": "Original contribution and stated assumptions, evaluation scope and limitations; source-linked assessment, not independent execution or proof certification." }, { "title": "Lange et al.: novelty-filtered sample-efficient program evolution — author/institutional project", "url": "https://sakana.ai/shinka-evolve/", "locator": "Original contribution and stated assumptions, evaluation scope and limitations; source-linked assessment, not independent execution or proof certification." } ], "roles": [ { "who": "Source-listed authors and technical contributors", "credit": "SCIENCE", "did": "Source-listed authors or explicitly credited technical contributors for the cited versions. Family entries retain source-specific attribution; a combined list is not one original-paper byline or equal individual work. Names alone do not establish resolved identities." } ], "target": { "record_id": "urn:uuid:4b17d1c9-de55-41cf-ae4f-2f69d8f7d709", "version": 1, "content_hash": "sha256:0d2759ffe80030f944a7ec418816f038d9e68cc5fba4d4d25c67918a8908ecb8" }, "scientific_correctness_certified": false, "independent_expert_calibration": false, "method": "PROPRIETARY", "points": "60000" }, "canonical_agent_submission": false, "scientific_verification_awarded": false, "reader_guide": null, "reading_status": "SOURCE_LINKED_NOTE" }