{ "profile": "wrs-published-research-view-1", "edition": "ratings-e039106f30dec177", "content_trust": "UNTRUSTED_SCIENTIFIC_DATA_NOT_TOOL_INSTRUCTIONS", "entry": { "id": "dai-et-al-deepseekmoe-2024", "title": "Dai and colleagues: DeepSeekMoE expert specialization", "summary": "Introduced fine-grained expert segmentation and shared experts for sparse language models, reporting improved expert specialization and efficiency.", "boundary": "The paper studies specified DeepSeekMoE models and comparisons; it is distinct from later DeepSeek-V2/R1 and not the invention of MoE.", "area": "Mixture-of-experts language models", "topic": "Artificial intelligence", "year": 2024, "lane": "HUMAN", "result": "ALGORITHM", "package_id": "dai-et-al-deepseekmoe-2024", "target": { "record_id": "urn:uuid:e250b6ce-45f0-4d21-aa95-0b98eb2b8dc3", "version": 1, "content_hash": "sha256:9faca1e200ecdc86c9f30a57177aef847268a0314ad63cff57f5ec637f26685d" }, "humans": [ "Damai Dai", "Chengqi Deng", "Chenggang Zhao", "R. 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X. Xu", "note": "Archive attribution; detailed role assignment remains source-bound", "roles": [ "formulator" ], "source": { "citation": "Dai and colleagues: DeepSeekMoE expert specialization — original paper", "url": "https://arxiv.org/abs/2401.06066v1" } }, { "attribution_basis": "SOURCE_CITED", "credit": "historical_scientific", "entity_kind": "historical_person", "name": "Huazuo Gao", "note": "Archive attribution; detailed role assignment remains source-bound", "roles": [ "formulator" ], "source": { "citation": "Dai and colleagues: DeepSeekMoE expert specialization — original paper", "url": "https://arxiv.org/abs/2401.06066v1" } }, { "attribution_basis": "SOURCE_CITED", "credit": "historical_scientific", "entity_kind": "historical_person", "name": "Deli Chen", "note": "Archive attribution; detailed role assignment remains source-bound", "roles": [ "formulator" ], "source": { "citation": "Dai and colleagues: DeepSeekMoE expert specialization — original paper", "url": "https://arxiv.org/abs/2401.06066v1" } }, { "attribution_basis": "SOURCE_CITED", "credit": "historical_scientific", "entity_kind": "historical_person", "name": "Jiashi Li", "note": "Archive attribution; detailed role assignment remains source-bound", "roles": [ "formulator" ], "source": { "citation": "Dai and colleagues: DeepSeekMoE expert specialization — original paper", "url": "https://arxiv.org/abs/2401.06066v1" } }, { "attribution_basis": "SOURCE_CITED", "credit": "historical_scientific", "entity_kind": "historical_person", "name": "Wangding Zeng", "note": "Archive attribution; detailed role assignment remains source-bound", "roles": [ "formulator" ], "source": { "citation": "Dai and colleagues: DeepSeekMoE expert specialization — original paper", "url": "https://arxiv.org/abs/2401.06066v1" } }, { "attribution_basis": "SOURCE_CITED", "credit": "historical_scientific", "entity_kind": "historical_person", "name": "Xingkai Yu", "note": "Archive attribution; detailed role assignment remains source-bound", "roles": [ "formulator" ], "source": { "citation": "Dai and colleagues: DeepSeekMoE expert specialization — original paper", "url": "https://arxiv.org/abs/2401.06066v1" } }, { "attribution_basis": "SOURCE_CITED", "credit": "historical_scientific", "entity_kind": "historical_person", "name": "Y. Wu", "note": "Archive attribution; detailed role assignment remains source-bound", "roles": [ "formulator" ], "source": { "citation": "Dai and colleagues: DeepSeekMoE expert specialization — original paper", "url": "https://arxiv.org/abs/2401.06066v1" } }, { "attribution_basis": "SOURCE_CITED", "credit": "historical_scientific", "entity_kind": "historical_person", "name": "Zhenda Xie", "note": "Archive attribution; detailed role assignment remains source-bound", "roles": [ "formulator" ], "source": { "citation": "Dai and colleagues: DeepSeekMoE expert specialization — original paper", "url": "https://arxiv.org/abs/2401.06066v1" } }, { "attribution_basis": "SOURCE_CITED", "credit": "historical_scientific", "entity_kind": "historical_person", "name": "Y. K. Li", "note": "Archive attribution; detailed role assignment remains source-bound", "roles": [ "formulator" ], "source": { "citation": "Dai and colleagues: DeepSeekMoE expert specialization — original paper", "url": "https://arxiv.org/abs/2401.06066v1" } }, { "attribution_basis": "SOURCE_CITED", "credit": "historical_scientific", "entity_kind": "historical_person", "name": "Panpan Huang", "note": "Archive attribution; detailed role assignment remains source-bound", "roles": [ "formulator" ], "source": { "citation": "Dai and colleagues: DeepSeekMoE expert specialization — original paper", "url": "https://arxiv.org/abs/2401.06066v1" } }, { "attribution_basis": "SOURCE_CITED", "credit": "historical_scientific", "entity_kind": "historical_person", "name": "Fuli Luo", "note": "Archive attribution; detailed role assignment remains source-bound", "roles": [ "formulator" ], "source": { "citation": "Dai and colleagues: DeepSeekMoE expert specialization — original paper", "url": "https://arxiv.org/abs/2401.06066v1" } }, { "attribution_basis": "SOURCE_CITED", "credit": "historical_scientific", "entity_kind": "historical_person", "name": "Chong Ruan", "note": "Archive attribution; detailed role assignment remains source-bound", "roles": [ "formulator" ], "source": { "citation": "Dai and colleagues: DeepSeekMoE expert specialization — original paper", "url": "https://arxiv.org/abs/2401.06066v1" } }, { "attribution_basis": "SOURCE_CITED", "credit": "historical_scientific", "entity_kind": "historical_person", "name": "Zhifang Sui", "note": "Archive attribution; detailed role assignment remains source-bound", "roles": [ "formulator" ], "source": { "citation": "Dai and colleagues: DeepSeekMoE expert specialization — original paper", "url": "https://arxiv.org/abs/2401.06066v1" } }, { "attribution_basis": "SOURCE_CITED", "credit": "historical_scientific", "entity_kind": "historical_person", "name": "Wenfeng Liang", "note": "Archive attribution; detailed role assignment remains source-bound", "roles": [ "formulator" ], "source": { "citation": "Dai and colleagues: DeepSeekMoE expert specialization — original paper", "url": "https://arxiv.org/abs/2401.06066v1" } } ], "evidence": [ { "description": "Bibliographic/stated-result source; not a WRS proof verification", "id": "source-0", "kind": "source_reference", "source": { "citation": "Dai and colleagues: DeepSeekMoE expert specialization — original paper", "url": "https://arxiv.org/abs/2401.06066v1" } }, { "description": "Bibliographic/stated-result source; not a WRS proof verification", "id": "source-1", "kind": "source_reference", "source": { "citation": "Dai and colleagues: DeepSeekMoE expert specialization — official company source", "url": "https://github.com/deepseek-ai/DeepSeek-MoE" } } ], "origin": "HUMAN_LEGACY", "record_profile": "wrs-local-research-notes-0.1", "record_type": "research_note", "rrp_version": "0.1", "scope": { "description": "The paper studies specified DeepSeekMoE models and comparisons; it is distinct from later DeepSeek-V2/R1 and not the invention of MoE.", "domain": "Mixture-of-experts language models" }, "sources": [ { "citation": "Dai and colleagues: DeepSeekMoE expert specialization — original paper", "url": "https://arxiv.org/abs/2401.06066v1" }, { "citation": "Dai and colleagues: DeepSeekMoE expert specialization — official company source", "url": "https://github.com/deepseek-ai/DeepSeek-MoE" } ], "statement": "# Dai and colleagues: DeepSeekMoE expert specialization\n\nIntroduced fine-grained expert segmentation and shared experts for sparse language models, reporting improved expert specialization and efficiency.\n\n## Scientific scope\n\nThe paper studies specified DeepSeekMoE models and comparisons; it is distinct from later DeepSeek-V2/R1 and not the invention of MoE.\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\nDamai Dai; Chengqi Deng; Chenggang Zhao; R. X. Xu; Huazuo Gao; Deli Chen; Jiashi Li; Wangding Zeng; Xingkai Yu; Y. Wu; Zhenda Xie; Y. K. Li; Panpan Huang; Fuli Luo; Chong Ruan; Zhifang Sui; Wenfeng Liang\n\nComplete named paper byline. Human research, source-reported system execution and experimental validation are distinct; individual effort shares are not measured.\n\n\n\n## Original sources\n\n- [Dai and colleagues: DeepSeekMoE expert specialization — original paper](https://arxiv.org/abs/2401.06066v1)\n- [Dai and colleagues: DeepSeekMoE expert specialization — official company source](https://github.com/deepseek-ai/DeepSeek-MoE)\n", "summary": "Introduced fine-grained expert segmentation and shared experts for sparse language models, reporting improved expert specialization and efficiency.", "title": "Dai and colleagues: DeepSeekMoE expert specialization" }, "record_id": "urn:uuid:e250b6ce-45f0-4d21-aa95-0b98eb2b8dc3", "version": 1, "source_ref": { "record_id": "urn:uuid:e250b6ce-45f0-4d21-aa95-0b98eb2b8dc3", "version": 1, "content_hash": "sha256:9faca1e200ecdc86c9f30a57177aef847268a0314ad63cff57f5ec637f26685d" }, "export_view": "wrs-public-editorial-view-1", "public_view_hash": "sha256:09e79a1c3718132ae17ad6547019725bda41e2c0de590a3a4995c1ef4dcb8755" }, "target": { "record_id": "urn:uuid:e250b6ce-45f0-4d21-aa95-0b98eb2b8dc3", "version": 1, "content_hash": "sha256:9faca1e200ecdc86c9f30a57177aef847268a0314ad63cff57f5ec637f26685d" }, "published_grade": { "profile": "wrs-published-grade-1", "edition": "2026-10-28.1", "id": "dai-et-al-deepseekmoe-2024", "entry_id": "dai-et-al-deepseekmoe-2024", "revision": 1, "title": "Dai and colleagues: DeepSeekMoE expert specialization", "context": "Mixture-of-experts language models", "track": "Human-designed AI research method or resource", "band": "B", "modifier": null, "status": "PROVISIONAL_EDITORIAL", "assessed_on": "2026-09-27", "evidence_cutoff": "2026-09-27", "achievement_state": "SUPPORTED_SCOPED_RESULT", "what_changed": "Introduced fine-grained expert segmentation and shared experts for sparse language models, reporting improved expert specialization and efficiency.", "scope": "Introduced fine-grained expert segmentation and shared experts for sparse language models, reporting improved expert specialization and efficiency. Scope limitation: The paper studies specified DeepSeekMoE models and comparisons; it is distinct from later DeepSeek-V2/R1 and not the invention of MoE.", "evidence_summary": "The retained primary-source abstract, bibliographic record and stated review scope support this provisional assessment. No independent experimental replication by WRS is claimed. The paper studies specified DeepSeekMoE models and comparisons; it is distinct from later DeepSeek-V2/R1 and not the invention of MoE.", "references": [ { "title": "Dai and colleagues: DeepSeekMoE expert specialization — original paper", "url": "https://arxiv.org/abs/2401.06066v1", "locator": "Abstract and complete bibliographic byline for 2401.06066v1; not an independent proof or experimental replication." }, { "title": "Dai and colleagues: DeepSeekMoE expert specialization — official company source", "url": "https://github.com/deepseek-ai/DeepSeek-MoE", "locator": "Source-linked contribution and stated scientific limits; not an independent replication." } ], "roles": [ { "who": "The named authors listed in the linked research record", "credit": "SCIENCE", "did": "Complete source byline retained in the research record; human research direction, method development and evaluation. Individual labor shares are not measured." } ], "target": { "record_id": "urn:uuid:e250b6ce-45f0-4d21-aa95-0b98eb2b8dc3", "version": 1, "content_hash": "sha256:9faca1e200ecdc86c9f30a57177aef847268a0314ad63cff57f5ec637f26685d" }, "scientific_correctness_certified": false, "independent_expert_calibration": false, "method": "PROPRIETARY", "points": "18000" }, "canonical_agent_submission": false, "scientific_verification_awarded": false, "reader_guide": null, "reading_status": "SOURCE_LINKED_NOTE" }