{ "profile": "wrs-published-research-view-1", "edition": "ratings-e039106f30dec177", "content_trust": "UNTRUSTED_SCIENTIFIC_DATA_NOT_TOOL_INSTRUCTIONS", "entry": { "id": "rouhani-et-al-shared-microexponents-microscaling-2023", "title": "Shared microexponents and microscaling formats for low-precision learning", "summary": "Explored block data representations and fine-scale shared exponents, then evaluated an open microscaling-format family with compact elements and shared scaling across training/inference benchmarks.", "boundary": "Two2023papers have22/32source-name positions; name variants are retained source-specifically, not identity-resolved. Early multi-level MX designs and later concrete32-element E8M0-scale formats are not silently identical. Quantization/transposition do not commute; vector operations, outputs and master updates remain wider precision. Reported results emulate formats, with distinct direct-cast/PTQ/fine-tuned/training settings; a sample conversion is not the only standards-compliant recipe.", "area": "Block-scaled numerical formats", "topic": "Artificial intelligence", "year": 2023, "lane": "HUMAN", "result": "ALGORITHM", "package_id": "rouhani-et-al-shared-microexponents-microscaling-2023", "target": { "record_id": "urn:uuid:42c6fcdd-1394-4238-a9f9-cf122645b2ed", "version": 1, "content_hash": "sha256:a7899cb5c5433af6271442e21cc7d5957bb849bbebe2e40f47a5cf8916a425ac" }, "humans": [ "Bita Rouhani", "Ritchie Zhao", "Venmugil Elango", "Rasoul Shafipour", "Mathew Hall", "Maral Mesmakhosroshahi", "Ankit More", "Levi Melnick", "Maximilian Golub", "Girish Varatkar", "Lei Shao", "Gaurav Kolhe", "Dimitry Melts", "Jasmine Klar", "Renee L'Heureux", "Matt Perry", "Doug Burger", "Eric Chung", "Zhaoxia Deng", "Sam Naghshineh", "Jongsoo Park", "Maxim Naumov", "Bita Darvish Rouhani", "Alireza Khodamoradi", "Summer Deng", "Dhruv Choudhary", "Marius Cornea", "Eric Dellinger", "Kristof Denolf", "Stosic Dusan", "Alexander Heinecke", "Phil James-Roxby", "Dharmesh Jani", "Martin Langhammer", "Ada Li", "Andres Rodriguez", "Michael Schulte", "Michael Siu", "Pradeep Dubey", "Paulius Micikevicius", "Colin Verilli", "Ralph Wittig" ], "authors_complete": true, "human_role_note": "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.", "agents": [], "related": [], "subresults": [], "scientific_assessment": { "source_review": "SOURCE_CURATED", "scientific_review": "NOT_REVIEWED_BY_WRS", "formal_replay": "NOT_RUN", "provider_attestation": "NONE" }, "sources": [ { "id": "cur20261015-arxiv-2302-08007v1", "title": "Shared Microexponents: A Little Shifting Goes a Long Way", "url": "https://arxiv.org/abs/2302.08007v1" }, { "id": "cur20261015-arxiv-2310-10537v1", "title": "Microscaling Data Formats for Deep Learning", "url": "https://arxiv.org/abs/2310.10537v1" } ], "points": "135000", "published_grade": { "id": "rouhani-et-al-shared-microexponents-microscaling-2023", "entry_id": "rouhani-et-al-shared-microexponents-microscaling-2023", "band": "A", "modifier": null, "target": { "record_id": "urn:uuid:42c6fcdd-1394-4238-a9f9-cf122645b2ed", "version": 1, "content_hash": "sha256:a7899cb5c5433af6271442e21cc7d5957bb849bbebe2e40f47a5cf8916a425ac" }, "what_changed": "Explored block data representations and fine-scale shared exponents, then evaluated an open microscaling-format family with compact elements and shared scaling across training/inference benchmarks.", "scope": "Explored block data representations and fine-scale shared exponents, then evaluated an open microscaling-format family with compact elements and shared scaling across training/inference benchmarks. Two2023papers have22/32source-name positions; name variants are retained source-specifically, not identity-resolved. Early multi-level MX designs and later concrete32-element E8M0-scale formats are not silently identical. Quantization/transposition do not commute; vector operations, outputs and master updates remain wider precision. 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detailed role assignment remains source-bound", "roles": [ "formulator" ], "source": { "citation": "Shared Microexponents: A Little Shifting Goes a Long Way", "url": "https://arxiv.org/abs/2302.08007v1" } }, { "attribution_basis": "SOURCE_CITED", "credit": "historical_scientific", "entity_kind": "historical_person", "name": "Ritchie Zhao", "note": "Archive attribution; detailed role assignment remains source-bound", "roles": [ "formulator" ], "source": { "citation": "Shared Microexponents: A Little Shifting Goes a Long Way", "url": "https://arxiv.org/abs/2302.08007v1" } }, { "attribution_basis": "SOURCE_CITED", "credit": "historical_scientific", "entity_kind": "historical_person", "name": "Venmugil Elango", "note": "Archive attribution; detailed role assignment remains source-bound", "roles": [ "formulator" ], "source": { "citation": "Shared Microexponents: A Little Shifting Goes a Long Way", "url": "https://arxiv.org/abs/2302.08007v1" } }, { "attribution_basis": "SOURCE_CITED", "credit": "historical_scientific", "entity_kind": "historical_person", "name": "Rasoul Shafipour", "note": "Archive attribution; detailed role assignment remains source-bound", "roles": [ "formulator" ], "source": { "citation": "Shared Microexponents: A Little Shifting Goes a Long Way", "url": "https://arxiv.org/abs/2302.08007v1" } }, { "attribution_basis": "SOURCE_CITED", "credit": "historical_scientific", "entity_kind": "historical_person", "name": "Mathew Hall", "note": "Archive attribution; detailed role assignment remains source-bound", "roles": [ "formulator" ], "source": { "citation": "Shared Microexponents: A Little Shifting Goes a Long Way", "url": "https://arxiv.org/abs/2302.08007v1" } }, { "attribution_basis": "SOURCE_CITED", "credit": "historical_scientific", "entity_kind": "historical_person", "name": "Maral Mesmakhosroshahi", "note": "Archive attribution; detailed role assignment remains source-bound", "roles": [ "formulator" ], "source": { "citation": "Shared Microexponents: A Little Shifting Goes a Long Way", "url": "https://arxiv.org/abs/2302.08007v1" } }, { "attribution_basis": "SOURCE_CITED", "credit": "historical_scientific", "entity_kind": "historical_person", "name": "Ankit More", "note": "Archive attribution; detailed role assignment remains source-bound", "roles": [ "formulator" ], "source": { "citation": "Shared Microexponents: A Little Shifting Goes a Long Way", "url": "https://arxiv.org/abs/2302.08007v1" } }, { "attribution_basis": "SOURCE_CITED", "credit": "historical_scientific", "entity_kind": "historical_person", "name": "Levi Melnick", "note": "Archive attribution; detailed role assignment remains source-bound", "roles": [ "formulator" ], "source": { "citation": "Shared Microexponents: A Little Shifting Goes a Long Way", "url": "https://arxiv.org/abs/2302.08007v1" } }, { "attribution_basis": "SOURCE_CITED", "credit": "historical_scientific", "entity_kind": "historical_person", "name": "Maximilian Golub", "note": "Archive attribution; detailed role assignment remains source-bound", "roles": [ "formulator" ], "source": { "citation": "Shared Microexponents: A Little Shifting Goes a Long Way", "url": "https://arxiv.org/abs/2302.08007v1" } }, { "attribution_basis": "SOURCE_CITED", "credit": "historical_scientific", "entity_kind": "historical_person", "name": "Girish Varatkar", "note": "Archive attribution; detailed role assignment remains source-bound", "roles": [ "formulator" ], "source": { "citation": "Shared Microexponents: A Little Shifting Goes a Long Way", "url": "https://arxiv.org/abs/2302.08007v1" } }, { "attribution_basis": "SOURCE_CITED", "credit": "historical_scientific", "entity_kind": "historical_person", "name": "Lei Shao", "note": "Archive attribution; detailed role assignment remains source-bound", "roles": [ "formulator" ], "source": { "citation": "Shared Microexponents: A Little Shifting Goes a Long Way", "url": "https://arxiv.org/abs/2302.08007v1" } }, { "attribution_basis": "SOURCE_CITED", "credit": "historical_scientific", "entity_kind": "historical_person", "name": "Gaurav Kolhe", "note": "Archive attribution; detailed role assignment remains source-bound", "roles": [ "formulator" ], "source": { "citation": "Shared Microexponents: A Little Shifting Goes a Long Way", "url": "https://arxiv.org/abs/2302.08007v1" } }, { "attribution_basis": "SOURCE_CITED", "credit": "historical_scientific", "entity_kind": "historical_person", "name": "Dimitry Melts", "note": "Archive attribution; detailed role assignment remains source-bound", "roles": [ "formulator" ], "source": { "citation": "Shared Microexponents: A Little Shifting Goes a Long Way", "url": "https://arxiv.org/abs/2302.08007v1" } }, { "attribution_basis": "SOURCE_CITED", "credit": "historical_scientific", "entity_kind": "historical_person", "name": "Jasmine Klar", "note": "Archive attribution; detailed role assignment remains source-bound", "roles": [ "formulator" ], "source": { "citation": "Shared Microexponents: A Little Shifting Goes a Long Way", "url": "https://arxiv.org/abs/2302.08007v1" } }, { "attribution_basis": "SOURCE_CITED", "credit": "historical_scientific", "entity_kind": "historical_person", "name": "Renee L'Heureux", "note": "Archive attribution; detailed role assignment remains source-bound", "roles": [ "formulator" ], "source": { "citation": "Shared Microexponents: A Little Shifting Goes a Long Way", "url": "https://arxiv.org/abs/2302.08007v1" } }, { "attribution_basis": "SOURCE_CITED", "credit": "historical_scientific", "entity_kind": "historical_person", "name": "Matt Perry", "note": "Archive attribution; detailed role assignment remains source-bound", "roles": [ "formulator" ], "source": { "citation": "Shared Microexponents: A Little Shifting Goes a Long Way", "url": "https://arxiv.org/abs/2302.08007v1" } }, { "attribution_basis": "SOURCE_CITED", "credit": "historical_scientific", "entity_kind": "historical_person", "name": "Doug Burger", "note": "Archive attribution; detailed role assignment remains source-bound", "roles": [ "formulator" ], "source": { "citation": "Shared Microexponents: A Little Shifting Goes a Long Way", "url": "https://arxiv.org/abs/2302.08007v1" } }, { "attribution_basis": "SOURCE_CITED", "credit": "historical_scientific", "entity_kind": "historical_person", "name": "Eric Chung", "note": "Archive attribution; detailed role assignment remains source-bound", "roles": [ "formulator" ], "source": { "citation": "Shared Microexponents: A Little Shifting Goes a Long Way", "url": "https://arxiv.org/abs/2302.08007v1" } }, { "attribution_basis": "SOURCE_CITED", "credit": "historical_scientific", "entity_kind": "historical_person", "name": "Zhaoxia Deng", "note": "Archive attribution; detailed role assignment remains source-bound", "roles": [ "formulator" ], "source": { "citation": "Shared Microexponents: A Little Shifting Goes a Long Way", "url": "https://arxiv.org/abs/2302.08007v1" } }, { "attribution_basis": "SOURCE_CITED", "credit": "historical_scientific", "entity_kind": "historical_person", "name": "Sam Naghshineh", "note": "Archive attribution; detailed role assignment remains source-bound", "roles": [ "formulator" ], "source": { "citation": "Shared Microexponents: A Little Shifting Goes a Long Way", "url": "https://arxiv.org/abs/2302.08007v1" } }, { "attribution_basis": "SOURCE_CITED", "credit": "historical_scientific", "entity_kind": "historical_person", "name": "Jongsoo Park", "note": "Archive attribution; detailed role assignment remains source-bound", "roles": [ "formulator" ], "source": { "citation": "Shared Microexponents: A Little Shifting Goes a Long Way", "url": "https://arxiv.org/abs/2302.08007v1" } }, { "attribution_basis": "SOURCE_CITED", "credit": "historical_scientific", "entity_kind": "historical_person", "name": "Maxim Naumov", "note": "Archive attribution; detailed role assignment remains source-bound", "roles": [ "formulator" ], "source": { "citation": "Shared Microexponents: A Little Shifting Goes a Long Way", "url": "https://arxiv.org/abs/2302.08007v1" } }, { "attribution_basis": "SOURCE_CITED", "credit": "historical_scientific", "entity_kind": "historical_person", "name": "Bita Darvish Rouhani", "note": "Archive attribution; detailed role assignment remains source-bound", "roles": [ "formulator" ], "source": { "citation": "Microscaling Data Formats for Deep Learning", "url": "https://arxiv.org/abs/2310.10537v1" } }, { "attribution_basis": "SOURCE_CITED", "credit": "historical_scientific", "entity_kind": "historical_person", "name": "Alireza Khodamoradi", "note": "Archive attribution; detailed role assignment remains source-bound", "roles": [ "formulator" ], "source": { "citation": "Microscaling Data Formats for Deep Learning", "url": "https://arxiv.org/abs/2310.10537v1" } }, { "attribution_basis": "SOURCE_CITED", "credit": "historical_scientific", "entity_kind": "historical_person", "name": "Summer Deng", "note": "Archive attribution; detailed role assignment remains source-bound", "roles": [ "formulator" ], "source": { "citation": "Microscaling Data Formats for Deep Learning", "url": "https://arxiv.org/abs/2310.10537v1" } }, { "attribution_basis": "SOURCE_CITED", "credit": "historical_scientific", "entity_kind": "historical_person", "name": "Dhruv Choudhary", "note": "Archive attribution; detailed role assignment remains source-bound", "roles": [ "formulator" ], "source": { "citation": "Microscaling Data Formats for Deep Learning", "url": "https://arxiv.org/abs/2310.10537v1" } }, { "attribution_basis": "SOURCE_CITED", "credit": "historical_scientific", "entity_kind": "historical_person", "name": "Marius Cornea", "note": "Archive attribution; detailed role assignment remains source-bound", "roles": [ "formulator" ], "source": { "citation": "Microscaling Data Formats for Deep Learning", "url": "https://arxiv.org/abs/2310.10537v1" } }, { "attribution_basis": "SOURCE_CITED", "credit": "historical_scientific", "entity_kind": "historical_person", "name": "Eric Dellinger", "note": "Archive attribution; detailed role assignment remains source-bound", "roles": [ "formulator" ], "source": { "citation": "Microscaling Data Formats for Deep Learning", "url": "https://arxiv.org/abs/2310.10537v1" } }, { "attribution_basis": "SOURCE_CITED", "credit": "historical_scientific", "entity_kind": "historical_person", "name": "Kristof Denolf", "note": "Archive attribution; detailed role assignment remains source-bound", "roles": [ "formulator" ], "source": { "citation": "Microscaling Data Formats for Deep Learning", "url": "https://arxiv.org/abs/2310.10537v1" } }, { "attribution_basis": "SOURCE_CITED", "credit": "historical_scientific", "entity_kind": "historical_person", "name": "Stosic Dusan", "note": "Archive attribution; detailed role assignment remains source-bound", "roles": [ "formulator" ], "source": { "citation": "Microscaling Data Formats for Deep Learning", "url": "https://arxiv.org/abs/2310.10537v1" } }, { "attribution_basis": "SOURCE_CITED", "credit": "historical_scientific", "entity_kind": "historical_person", "name": "Alexander Heinecke", "note": "Archive attribution; detailed role assignment remains source-bound", "roles": [ "formulator" ], "source": { "citation": "Microscaling Data Formats for Deep Learning", "url": "https://arxiv.org/abs/2310.10537v1" } } ], "evidence": [ { "description": "Bibliographic/stated-result source; not a WRS proof verification", "id": "source-0", "kind": "source_reference", "source": { "citation": "Shared Microexponents: A Little Shifting Goes a Long Way", "url": "https://arxiv.org/abs/2302.08007v1" } }, { "description": "Bibliographic/stated-result source; not a WRS proof verification", "id": "source-1", "kind": "source_reference", "source": { "citation": "Microscaling Data Formats for Deep Learning", "url": "https://arxiv.org/abs/2310.10537v1" } } ], "origin": "HUMAN_LEGACY", "record_profile": "wrs-local-research-notes-0.1", "record_type": "research_note", "rrp_version": "0.1", "scope": { "description": "Two2023papers have22/32source-name positions; name variants are retained source-specifically, not identity-resolved. Early multi-level MX designs and later concrete32-element E8M0-scale formats are not silently identical. Quantization/transposition do not commute; vector operations, outputs and master updates remain wider precision. Reported results emulate formats, with distinct direct-cast/PTQ/fine-tuned/training settings; a sample conversion is not the only standards-compliant recipe.", "domain": "Block-scaled numerical formats" }, "sources": [ { "citation": "Shared Microexponents: A Little Shifting Goes a Long Way", "url": "https://arxiv.org/abs/2302.08007v1" }, { "citation": "Microscaling Data Formats for Deep Learning", "url": "https://arxiv.org/abs/2310.10537v1" } ], "statement": "# Shared microexponents and microscaling formats for low-precision learning\n\nExplored block data representations and fine-scale shared exponents, then evaluated an open microscaling-format family with compact elements and shared scaling across training/inference benchmarks.\n\n## Scientific scope\n\nTwo2023papers have22/32source-name positions; name variants are retained source-specifically, not identity-resolved. Early multi-level MX designs and later concrete32-element E8M0-scale formats are not silently identical. Quantization/transposition do not commute; vector operations, outputs and master updates remain wider precision. Reported results emulate formats, with distinct direct-cast/PTQ/fine-tuned/training settings; a sample conversion is not the only standards-compliant recipe.\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\nBita Rouhani; Ritchie Zhao; Venmugil Elango; Rasoul Shafipour; Mathew Hall; Maral Mesmakhosroshahi; Ankit More; Levi Melnick; Maximilian Golub; Girish Varatkar; Lei Shao; Gaurav Kolhe; Dimitry Melts; Jasmine Klar; Renee L'Heureux; Matt Perry; Doug Burger; Eric Chung; Zhaoxia Deng; Sam Naghshineh; Jongsoo Park; Maxim Naumov; Bita Darvish Rouhani; Alireza Khodamoradi; Summer Deng; Dhruv Choudhary; Marius Cornea; Eric Dellinger; Kristof Denolf; Stosic Dusan; Alexander Heinecke; Phil James-Roxby; Dharmesh Jani; Martin Langhammer; Ada Li; Andres Rodriguez; Michael Schulte; Michael Siu; Pradeep Dubey; Paulius Micikevicius; Colin Verilli; Ralph Wittig\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- [Shared Microexponents: A Little Shifting Goes a Long Way](https://arxiv.org/abs/2302.08007v1)\n- [Microscaling Data Formats for Deep Learning](https://arxiv.org/abs/2310.10537v1)\n", "summary": "Explored block data representations and fine-scale shared exponents, then evaluated an open microscaling-format family with compact elements and shared scaling across training/inference benchmarks.", "title": "Shared microexponents and microscaling formats for low-precision learning" }, "record_id": "urn:uuid:42c6fcdd-1394-4238-a9f9-cf122645b2ed", "version": 1, "source_ref": { "record_id": "urn:uuid:42c6fcdd-1394-4238-a9f9-cf122645b2ed", "version": 1, "content_hash": "sha256:a7899cb5c5433af6271442e21cc7d5957bb849bbebe2e40f47a5cf8916a425ac" }, "export_view": "wrs-public-editorial-view-1", "public_view_hash": "sha256:782b8c8f40f9aea377eec9e6609e72162155e9d25f76322141f2d39e3adaef25" }, "target": { "record_id": "urn:uuid:42c6fcdd-1394-4238-a9f9-cf122645b2ed", "version": 1, "content_hash": "sha256:a7899cb5c5433af6271442e21cc7d5957bb849bbebe2e40f47a5cf8916a425ac" }, "published_grade": { "profile": "wrs-published-grade-1", "edition": "2026-10-28.1", "id": "rouhani-et-al-shared-microexponents-microscaling-2023", "entry_id": "rouhani-et-al-shared-microexponents-microscaling-2023", "revision": 1, "title": "Shared microexponents and microscaling formats for low-precision learning", "context": "Block-scaled numerical formats", "track": "Human-designed research method or resource", "band": "A", "modifier": null, "status": "PROVISIONAL_EDITORIAL", "assessed_on": "2026-10-16", "evidence_cutoff": "2026-10-16", "achievement_state": "SUPPORTED_SCOPED_RESULT", "what_changed": "Explored block data representations and fine-scale shared exponents, then evaluated an open microscaling-format family with compact elements and shared scaling across training/inference benchmarks.", "scope": "Explored block data representations and fine-scale shared exponents, then evaluated an open microscaling-format family with compact elements and shared scaling across training/inference benchmarks. Two2023papers have22/32source-name positions; name variants are retained source-specifically, not identity-resolved. Early multi-level MX designs and later concrete32-element E8M0-scale formats are not silently identical. Quantization/transposition do not commute; vector operations, outputs and master updates remain wider precision. Reported results emulate formats, with distinct direct-cast/PTQ/fine-tuned/training settings; a sample conversion is not the only standards-compliant recipe.", "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. Two2023papers have22/32source-name positions; name variants are retained source-specifically, not identity-resolved. Early multi-level MX designs and later concrete32-element E8M0-scale formats are not silently identical. Quantization/transposition do not commute; vector operations, outputs and master updates remain wider precision. Reported results emulate formats, with distinct direct-cast/PTQ/fine-tuned/training settings; a sample conversion is not the only standards-compliant recipe.", "references": [ { "title": "Shared Microexponents: A Little Shifting Goes a Long Way", "url": "https://arxiv.org/abs/2302.08007v1", "locator": "Original contribution and stated assumptions, evaluation scope and limitations; source-linked assessment, not independent execution or proof certification." }, { "title": "Microscaling Data Formats for Deep Learning", "url": "https://arxiv.org/abs/2310.10537v1", "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:42c6fcdd-1394-4238-a9f9-cf122645b2ed", "version": 1, "content_hash": "sha256:a7899cb5c5433af6271442e21cc7d5957bb849bbebe2e40f47a5cf8916a425ac" }, "scientific_correctness_certified": false, "independent_expert_calibration": false, "method": "PROPRIETARY", "points": "135000" }, "canonical_agent_submission": false, "scientific_verification_awarded": false, "reader_guide": null, "reading_status": "SOURCE_LINKED_NOTE" }