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detailed role assignment remains source-bound", "roles": [ "discoverer" ], "source": { "citation": "LatentMoE: Toward Optimal Accuracy per FLOP and Parameter in Mixture of Experts", "url": "https://arxiv.org/abs/2601.18089v1" } }, { "attribution_basis": "SOURCE_CITED", "credit": "historical_scientific", "entity_kind": "historical_person", "name": "Mohammad Shoeybi", "note": "Archive attribution; detailed role assignment remains source-bound", "roles": [ "discoverer" ], "source": { "citation": "LatentMoE: Toward Optimal Accuracy per FLOP and Parameter in Mixture of Experts", "url": "https://arxiv.org/abs/2601.18089v1" } }, { "attribution_basis": "SOURCE_CITED", "credit": "historical_scientific", "entity_kind": "historical_person", "name": "Bita Rouhani", "note": "Archive attribution; detailed role assignment remains source-bound", "roles": [ "discoverer" ], "source": { "citation": "LatentMoE: Toward Optimal Accuracy per FLOP and Parameter in Mixture of Experts", "url": "https://arxiv.org/abs/2601.18089v1" } } ], "evidence": [ { "description": "Bibliographic/stated-result source; not a WRS proof verification", "id": "source-0", "kind": "source_reference", "source": { "citation": "LatentMoE: Toward Optimal Accuracy per FLOP and Parameter in Mixture of Experts", "url": "https://arxiv.org/abs/2601.18089v1" } }, { "description": "Bibliographic/stated-result source; not a WRS proof verification", "id": "source-1", "kind": "source_reference", "source": { "citation": "Elango et al.: LatentMoE compressed routed-expert computation — official method source", "url": "https://research.nvidia.com/labs/nemotron/LatentMoE/" } } ], "origin": "HUMAN_LEGACY", "record_profile": "wrs-local-research-notes-0.1", "record_type": "research_note", "rrp_version": "0.1", "scope": { "description": "Method-level design distinct from later Nemotron NVFP4 training/system integration. Accuracy-cost evidence is source-reported; trillion-scale serving gains include projections, not measured universal speedups. Excess compression can degrade quality.", "domain": "Serving-aware mixture-of-experts architecture" }, "sources": [ { "citation": "LatentMoE: Toward Optimal Accuracy per FLOP and Parameter in Mixture of Experts", "url": "https://arxiv.org/abs/2601.18089v1" }, { "citation": "Elango et al.: LatentMoE compressed routed-expert computation — official method source", "url": "https://research.nvidia.com/labs/nemotron/LatentMoE/" } ], "statement": "# Elango et al.: LatentMoE compressed routed-expert computation\n\nProjected routed payloads and expert computation into a lower-dimensional latent space while retaining routing decisions in the original representation, trading communication and weight traffic for expert capacity.\n\n## Scientific scope\n\nMethod-level design distinct from later Nemotron NVFP4 training/system integration. Accuracy-cost evidence is source-reported; trillion-scale serving gains include projections, not measured universal speedups. Excess compression can degrade quality.\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\nVenmugil Elango; Nidhi Bhatia; Roger Waleffe; Rasoul Shafipour; Tomer Asida; Abhinav Khattar; Nave Assaf; Maximilian Golub; Joey Guman; Tiyasa Mitra; Ritchie Zhao; Ritika Borkar; Ran Zilberstein; Mostofa Patwary; Mohammad Shoeybi; Bita Rouhani\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- [LatentMoE: Toward Optimal Accuracy per FLOP and Parameter in Mixture of Experts](https://arxiv.org/abs/2601.18089v1)\n- [Elango et al.: LatentMoE compressed routed-expert computation — official method source](https://research.nvidia.com/labs/nemotron/LatentMoE/)\n", "summary": "Projected routed payloads and expert computation into a lower-dimensional latent space while retaining routing decisions in the original representation, trading communication and weight traffic for expert capacity.", "title": "Elango et al.: LatentMoE compressed routed-expert computation" }, "record_id": "urn:uuid:50d667ac-4bc8-4b74-abd8-d242a7551b5a", "version": 1, "source_ref": { "record_id": "urn:uuid:50d667ac-4bc8-4b74-abd8-d242a7551b5a", "version": 1, "content_hash": "sha256:ecf7fc275f0719801655ed1c998c41f78e5ec48c4b125144fb9a61b0d7d62b33" }, "export_view": "wrs-public-editorial-view-1", "public_view_hash": "sha256:811e3eba5192d0a79c6f1ce14b35097eed0f0cf953d47fb854771039cabffe97" }, "target": { "record_id": "urn:uuid:50d667ac-4bc8-4b74-abd8-d242a7551b5a", "version": 1, "content_hash": "sha256:ecf7fc275f0719801655ed1c998c41f78e5ec48c4b125144fb9a61b0d7d62b33" }, "published_grade": { "profile": "wrs-published-grade-1", "edition": "2026-10-28.1", "id": "elango-et-al-latentmoe-serving-aware-2026", "entry_id": "elango-et-al-latentmoe-serving-aware-2026", "revision": 1, "title": "Elango et al.: LatentMoE compressed routed-expert computation", "context": "Serving-aware mixture-of-experts architecture", "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": "Projected routed payloads and expert computation into a lower-dimensional latent space while retaining routing decisions in the original representation, trading communication and weight traffic for expert capacity.", "scope": "Projected routed payloads and expert computation into a lower-dimensional latent space while retaining routing decisions in the original representation, trading communication and weight traffic for expert capacity. 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