{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2024:Y6GGFJG6WQLMWCPFVLSVUYYZSU","short_pith_number":"pith:Y6GGFJG6","canonical_record":{"source":{"id":"2404.05567","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2024-04-08T14:39:49Z","cross_cats_sorted":["cs.AI","cs.CL"],"title_canon_sha256":"7415a4d09c17beb6dfd5530307e3505136d079a9348236482a5f6be2a19c723f","abstract_canon_sha256":"b954f5c2666c720fd22c083bb6e8ebb37dae2cc06c0011a0460f12b3995c36de"},"schema_version":"1.0"},"canonical_sha256":"c78c62a4deb416cb09e5aae55a63199508f30df67e280f22c918787efb17fd3f","source":{"kind":"arxiv","id":"2404.05567","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2404.05567","created_at":"2026-07-05T08:05:42Z"},{"alias_kind":"arxiv_version","alias_value":"2404.05567v1","created_at":"2026-07-05T08:05:42Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2404.05567","created_at":"2026-07-05T08:05:42Z"},{"alias_kind":"pith_short_12","alias_value":"Y6GGFJG6WQLM","created_at":"2026-07-05T08:05:42Z"},{"alias_kind":"pith_short_16","alias_value":"Y6GGFJG6WQLMWCPF","created_at":"2026-07-05T08:05:42Z"},{"alias_kind":"pith_short_8","alias_value":"Y6GGFJG6","created_at":"2026-07-05T08:05:42Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2024:Y6GGFJG6WQLMWCPFVLSVUYYZSU","target":"record","payload":{"canonical_record":{"source":{"id":"2404.05567","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2024-04-08T14:39:49Z","cross_cats_sorted":["cs.AI","cs.CL"],"title_canon_sha256":"7415a4d09c17beb6dfd5530307e3505136d079a9348236482a5f6be2a19c723f","abstract_canon_sha256":"b954f5c2666c720fd22c083bb6e8ebb37dae2cc06c0011a0460f12b3995c36de"},"schema_version":"1.0"},"canonical_sha256":"c78c62a4deb416cb09e5aae55a63199508f30df67e280f22c918787efb17fd3f","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:05:42.630535Z","signature_b64":"uH7WRaXvK9JiHc2IAIC/xc3Axn8XVBUDKhg+jCzgU1BY4z76ut4Xxl2jQCQpNuSA85f18oJCu6zWBNJmb1xBBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"c78c62a4deb416cb09e5aae55a63199508f30df67e280f22c918787efb17fd3f","last_reissued_at":"2026-07-05T08:05:42.630036Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:05:42.630036Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2404.05567","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T08:05:42Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"KiBQiadv81e/i38hVq7IKZyBXhqOSxEN0N0EmzTMu4qBb0ohIHwgHzsOpEan2wOv52gf7FxsuE1/TS2k+k1FDg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-09T00:11:51.445643Z"},"content_sha256":"637e2568fae67f51dad20a48142d691dd3ed14c9a50f97ac44eaf5e7c8615c83","schema_version":"1.0","event_id":"sha256:637e2568fae67f51dad20a48142d691dd3ed14c9a50f97ac44eaf5e7c8615c83"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2024:Y6GGFJG6WQLMWCPFVLSVUYYZSU","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Dense Training, Sparse Inference: Rethinking Training of Mixture-of-Experts Language Models","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.CL"],"primary_cat":"cs.LG","authors_text":"Aude Oliva, Bowen Pan, Colin Raffel, Gaoyuan Zhang, Haokun Liu, Mayank Mishra, Rameswar Panda, Yikang Shen","submitted_at":"2024-04-08T14:39:49Z","abstract_excerpt":"Mixture-of-Experts (MoE) language models can reduce computational costs by 2-4$\\times$ compared to dense models without sacrificing performance, making them more efficient in computation-bounded scenarios. However, MoE models generally require 2-4$\\times$ times more parameters to achieve comparable performance to a dense model, which incurs larger GPU memory requirements and makes MoE models less efficient in I/O-bounded scenarios like autoregressive generation. In this work, we propose a hybrid dense training and sparse inference framework for MoE models (DS-MoE) which achieves strong computa"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2404.05567","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2404.05567/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T08:05:42Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"mzvP/yjmQlM8dRduQI0enc4nG3l/ZuiA5t+fA7D9RtO11Q984JhgNNNniGj8yt70KwrL0ehs0qQIl1IVV2wWAw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-09T00:11:51.446142Z"},"content_sha256":"6ef2c28725e3f1faf5b4811f177aae5df5cb6d5ad5e13826c9ad18c73b931c47","schema_version":"1.0","event_id":"sha256:6ef2c28725e3f1faf5b4811f177aae5df5cb6d5ad5e13826c9ad18c73b931c47"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/Y6GGFJG6WQLMWCPFVLSVUYYZSU/bundle.json","state_url":"https://pith.science/pith/Y6GGFJG6WQLMWCPFVLSVUYYZSU/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/Y6GGFJG6WQLMWCPFVLSVUYYZSU/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-08-09T00:11:51Z","links":{"resolver":"https://pith.science/pith/Y6GGFJG6WQLMWCPFVLSVUYYZSU","bundle":"https://pith.science/pith/Y6GGFJG6WQLMWCPFVLSVUYYZSU/bundle.json","state":"https://pith.science/pith/Y6GGFJG6WQLMWCPFVLSVUYYZSU/state.json","well_known_bundle":"https://pith.science/.well-known/pith/Y6GGFJG6WQLMWCPFVLSVUYYZSU/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:Y6GGFJG6WQLMWCPFVLSVUYYZSU","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"b954f5c2666c720fd22c083bb6e8ebb37dae2cc06c0011a0460f12b3995c36de","cross_cats_sorted":["cs.AI","cs.CL"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2024-04-08T14:39:49Z","title_canon_sha256":"7415a4d09c17beb6dfd5530307e3505136d079a9348236482a5f6be2a19c723f"},"schema_version":"1.0","source":{"id":"2404.05567","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2404.05567","created_at":"2026-07-05T08:05:42Z"},{"alias_kind":"arxiv_version","alias_value":"2404.05567v1","created_at":"2026-07-05T08:05:42Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2404.05567","created_at":"2026-07-05T08:05:42Z"},{"alias_kind":"pith_short_12","alias_value":"Y6GGFJG6WQLM","created_at":"2026-07-05T08:05:42Z"},{"alias_kind":"pith_short_16","alias_value":"Y6GGFJG6WQLMWCPF","created_at":"2026-07-05T08:05:42Z"},{"alias_kind":"pith_short_8","alias_value":"Y6GGFJG6","created_at":"2026-07-05T08:05:42Z"}],"graph_snapshots":[{"event_id":"sha256:6ef2c28725e3f1faf5b4811f177aae5df5cb6d5ad5e13826c9ad18c73b931c47","target":"graph","created_at":"2026-07-05T08:05:42Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2404.05567/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Mixture-of-Experts (MoE) language models can reduce computational costs by 2-4$\\times$ compared to dense models without sacrificing performance, making them more efficient in computation-bounded scenarios. However, MoE models generally require 2-4$\\times$ times more parameters to achieve comparable performance to a dense model, which incurs larger GPU memory requirements and makes MoE models less efficient in I/O-bounded scenarios like autoregressive generation. In this work, we propose a hybrid dense training and sparse inference framework for MoE models (DS-MoE) which achieves strong computa","authors_text":"Aude Oliva, Bowen Pan, Colin Raffel, Gaoyuan Zhang, Haokun Liu, Mayank Mishra, Rameswar Panda, Yikang Shen","cross_cats":["cs.AI","cs.CL"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2024-04-08T14:39:49Z","title":"Dense Training, Sparse Inference: Rethinking Training of Mixture-of-Experts Language Models"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2404.05567","kind":"arxiv","version":1},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:637e2568fae67f51dad20a48142d691dd3ed14c9a50f97ac44eaf5e7c8615c83","target":"record","created_at":"2026-07-05T08:05:42Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"b954f5c2666c720fd22c083bb6e8ebb37dae2cc06c0011a0460f12b3995c36de","cross_cats_sorted":["cs.AI","cs.CL"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2024-04-08T14:39:49Z","title_canon_sha256":"7415a4d09c17beb6dfd5530307e3505136d079a9348236482a5f6be2a19c723f"},"schema_version":"1.0","source":{"id":"2404.05567","kind":"arxiv","version":1}},"canonical_sha256":"c78c62a4deb416cb09e5aae55a63199508f30df67e280f22c918787efb17fd3f","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"c78c62a4deb416cb09e5aae55a63199508f30df67e280f22c918787efb17fd3f","first_computed_at":"2026-07-05T08:05:42.630036Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T08:05:42.630036Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"uH7WRaXvK9JiHc2IAIC/xc3Axn8XVBUDKhg+jCzgU1BY4z76ut4Xxl2jQCQpNuSA85f18oJCu6zWBNJmb1xBBg==","signature_status":"signed_v1","signed_at":"2026-07-05T08:05:42.630535Z","signed_message":"canonical_sha256_bytes"},"source_id":"2404.05567","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:637e2568fae67f51dad20a48142d691dd3ed14c9a50f97ac44eaf5e7c8615c83","sha256:6ef2c28725e3f1faf5b4811f177aae5df5cb6d5ad5e13826c9ad18c73b931c47"],"state_sha256":"5e35b2685b978bfb41e1cf0d6380c05af383cba0389b80a1724eccf2bd4e8a7c"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"oY5HJw4oInlI6uUYvnLMsJlQ+/7aIXcwXZXgktnVgJY0ej7bXVMmlRlUst1qa4PO/w5ptU4tiE0a7lqeRUa6BQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-09T00:11:51.451223Z","bundle_sha256":"3d897b7b5bcfa15df67248605f7ee1e45ddf56aa0f04a9e877207e080a32a901"}}