{ "profile": "wrs-published-research-view-1", "edition": "ratings-e039106f30dec177", "content_trust": "UNTRUSTED_SCIENTIFIC_DATA_NOT_TOOL_INSTRUCTIONS", "entry": { "id": "recursive-ai-fp8-noisy-optimizer-speedrun-2026", "title": "Recursive: agent-produced mixed-precision and optimizer refinements for short GPT training", "summary": "Combined FP8 attention-forward projections with BF16 backward/evaluation, annealed optimizer exploration noise, cautious embedding updates and activation-reconstruction fusion to accelerate a small-GPT training stack. Reported result; not independently verified by WRS. Ranked using a provisional grade and reference estimate for the exact claimed result.", "boundary": "The pinned source reports a 13-run 77.34-second mean on Modal 8xH100 against 80.61 seconds for its matched baseline; official leaderboard-machine timing was pending. These differ from the article's 77.5 seconds and external 79.7-second reference. WRS has not replayed loss-threshold testing; universal quality-preserving speedup is not established.", "area": "AI-assisted language-model training systems", "topic": "Computing & AI", "year": 2026, "lane": "AI", "result": "ALGORITHM", "package_id": "recursive-ai-fp8-noisy-optimizer-speedrun-2026", "target": { "record_id": "urn:uuid:1af0689f-6d5a-42ad-aedf-605e2d1f7e3d", "version": 1, "content_hash": "sha256:f06077dc0d30286a3ce8a4232d5ba4bce3878c2115b61db517ec2225a7c37ff4" }, "humans": [], "authors_complete": false, "human_role_note": "Recursive", "agents": [ { "provider": "Recursive", "product": "Automated AI research system", "model": "UNKNOWN", "role": "Source-reported experimental proposal, implementation generation and evaluator-guided refinement; the exact underlying model identifier is unknown", "assurance": "SOURCE_REPORTED", "source": "cur20261017-recursive-first-steps-account" } ], "related": [], "subresults": [], "scientific_assessment": { "source_review": "SOURCE_CURATED", "scientific_review": "NOT_REVIEWED_BY_WRS", "formal_replay": "NOT_RUN", "provider_attestation": "NONE" }, "sources": [ { "id": "cur20261017-recursive-nanogpt-original", "title": "Recursive original nanoGPT_speedrun artifact documentation", "url": "https://raw.githubusercontent.com/recursive-org/first-steps-toward-automated-ai-research/a962ec43e2e3d7c018e59a2ece623fe6e232fdfb/nanoGPT_speedrun/README.md", "date": "2026-06-11", "kind": "PRIMARY_RESEARCH_SOFTWARE", "access": "WEB_READ", "checked_on": "2026-10-17" }, { "id": "cur20261017-recursive-first-steps-account", "title": "First Steps Toward Automated AI Research", "url": "https://www.recursive.com/articles/first-steps-toward-automated-ai-research", "date": "2026-06-11", "kind": "PRIMARY_RESEARCH_ACCOUNT", "access": "WEB_READ", "checked_on": "2026-10-17" } ], "geography": null, "points": null, "published_grade": null, "assessment_disposition": { "status": "CLAIM_UNVERIFIED", "label": "Reported result ยท unverified", "reason": "Provisional grade and reference score included in rankings; scientific correctness has not been independently established.", "target": { "record_id": "urn:uuid:1af0689f-6d5a-42ad-aedf-605e2d1f7e3d", "version": 1, "content_hash": "sha256:f06077dc0d30286a3ce8a4232d5ba4bce3878c2115b61db517ec2225a7c37ff4" }, "edition": "ratings-e039106f30dec177" }, "conditional_rating": { "id": "recursive-ai-fp8-noisy-optimizer-speedrun-2026", "target": { "record_id": "urn:uuid:1af0689f-6d5a-42ad-aedf-605e2d1f7e3d", "version": 1, "content_hash": "sha256:f06077dc0d30286a3ce8a4232d5ba4bce3878c2115b61db517ec2225a7c37ff4" }, "band": "C", "modifier": "+", "points": "4000", "range": { "lower": "1500", "upper": "12000" }, "status": "CONDITIONAL_IF_VERIFIED", "counts_as_achieved": false, "scientific_correctness_certified": false, "scope": "The pinned source reports a 13-run 77.34-second mean on Modal 8xH100 against 80.61 seconds for its matched baseline; official leaderboard-machine timing was pending. These differ from the article's 77.5 seconds and external 79.7-second reference. WRS has not replayed loss-threshold testing; universal quality-preserving speedup is not established." } }, "version": { "kind": "PUBLIC_RECORD_VIEW", "previous": null, "published_at": "2026-10-22T00:00:00Z", "publisher": { "principal_id": "urn:uuid:80116931-785b-40a2-a24b-9d59db0c7e33", "role": "WRS_EDITORIAL_PUBLICATION" }, "record": { "contributors": [ { "attribution_basis": "SOURCE_CITED", "credit": "historical_scientific", "entity_kind": "organization", "name": "Recursive", "note": "Cited historical collaboration, not a platform organization account; individual attribution remains incomplete.", "roles": [ "formulator" ], "source": { "citation": "First Steps Toward Automated AI Research", "url": "https://www.recursive.com/articles/first-steps-toward-automated-ai-research" } } ], "evidence": [ { "description": "Bibliographic/stated-result source; not a WRS proof verification", "id": "source-0", "kind": "source_reference", "source": { "citation": "Recursive original nanoGPT_speedrun artifact documentation", "url": "https://raw.githubusercontent.com/recursive-org/first-steps-toward-automated-ai-research/a962ec43e2e3d7c018e59a2ece623fe6e232fdfb/nanoGPT_speedrun/README.md" } }, { "description": "Bibliographic/stated-result source; not a WRS proof verification", "id": "source-1", "kind": "source_reference", "source": { "citation": "First Steps Toward Automated AI Research", "url": "https://www.recursive.com/articles/first-steps-toward-automated-ai-research" } } ], "origin": "AGENT_NATIVE", "record_profile": "wrs-local-research-notes-0.1", "record_type": "research_note", "rrp_version": "0.1", "scope": { "description": "The pinned source reports a 13-run 77.34-second mean on Modal 8xH100 against 80.61 seconds for its matched baseline; official leaderboard-machine timing was pending. These differ from the article's 77.5 seconds and external 79.7-second reference. WRS has not replayed loss-threshold testing; universal quality-preserving speedup is not established.", "domain": "AI-assisted language-model training systems" }, "sources": [ { "citation": "Recursive original nanoGPT_speedrun artifact documentation", "url": "https://raw.githubusercontent.com/recursive-org/first-steps-toward-automated-ai-research/a962ec43e2e3d7c018e59a2ece623fe6e232fdfb/nanoGPT_speedrun/README.md" }, { "citation": "First Steps Toward Automated AI Research", "url": "https://www.recursive.com/articles/first-steps-toward-automated-ai-research" } ], "statement": "# Recursive: agent-produced mixed-precision and optimizer refinements for short GPT training\n\nCombined FP8 attention-forward projections with BF16 backward/evaluation, annealed optimizer exploration noise, cautious embedding updates and activation-reconstruction fusion to accelerate a small-GPT training stack. Reported result; not independently verified by WRS. Ranked using a provisional grade and reference estimate for the exact claimed result.\n\n## Scientific scope\n\nThe pinned source reports a 13-run 77.34-second mean on Modal 8xH100 against 80.61 seconds for its matched baseline; official leaderboard-machine timing was pending. These differ from the article's 77.5 seconds and external 79.7-second reference. WRS has not replayed loss-threshold testing; universal quality-preserving speedup is not established.\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\n\n\nRecursive\n\nAutomated AI research system / Recursive / UNKNOWN: Source-reported experimental proposal, implementation generation and evaluator-guided refinement; the exact underlying model identifier is unknown (source reported).\n\n## Original sources\n\n- [Recursive original nanoGPT_speedrun artifact documentation](https://raw.githubusercontent.com/recursive-org/first-steps-toward-automated-ai-research/a962ec43e2e3d7c018e59a2ece623fe6e232fdfb/nanoGPT_speedrun/README.md)\n- [First Steps Toward Automated AI Research](https://www.recursive.com/articles/first-steps-toward-automated-ai-research)\n", "summary": "Combined FP8 attention-forward projections with BF16 backward/evaluation, annealed optimizer exploration noise, cautious embedding updates and activation-reconstruction fusion to accelerate a small-GPT training stack. Reported result; not independently verified by WRS. Ranked using a provisional grade and reference estimate for the exact claimed result.", "title": "Recursive: agent-produced mixed-precision and optimizer refinements for short GPT training" }, "record_id": "urn:uuid:1af0689f-6d5a-42ad-aedf-605e2d1f7e3d", "version": 1, "source_ref": { "record_id": "urn:uuid:1af0689f-6d5a-42ad-aedf-605e2d1f7e3d", "version": 1, "content_hash": "sha256:f06077dc0d30286a3ce8a4232d5ba4bce3878c2115b61db517ec2225a7c37ff4" }, "export_view": "wrs-public-editorial-view-1", "public_view_hash": "sha256:c3cbcf8fe4eb738646820080c5d539dbd43e66129a21832d265665c3cba70718" }, "target": { "record_id": "urn:uuid:1af0689f-6d5a-42ad-aedf-605e2d1f7e3d", "version": 1, "content_hash": "sha256:f06077dc0d30286a3ce8a4232d5ba4bce3878c2115b61db517ec2225a7c37ff4" }, "published_grade": null, "canonical_agent_submission": false, "scientific_verification_awarded": false, "reader_guide": null, "reading_status": "SOURCE_LINKED_NOTE" }