{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:WN6AD3D37BBOB5UV4M5OAEPXL7","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":"b4275e4c565a16b981b38f75480af1399650aed199e668313f528a35ab991fd0","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.DC","submitted_at":"2025-03-09T04:14:06Z","title_canon_sha256":"6f83b9db788e1fb994cedb5b584561deaa563d360c9dc606e7d5ef789ce837f0"},"schema_version":"1.0","source":{"id":"2503.06433","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2503.06433","created_at":"2026-07-05T10:27:33Z"},{"alias_kind":"arxiv_version","alias_value":"2503.06433v1","created_at":"2026-07-05T10:27:33Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2503.06433","created_at":"2026-07-05T10:27:33Z"},{"alias_kind":"pith_short_12","alias_value":"WN6AD3D37BBO","created_at":"2026-07-05T10:27:33Z"},{"alias_kind":"pith_short_16","alias_value":"WN6AD3D37BBOB5UV","created_at":"2026-07-05T10:27:33Z"},{"alias_kind":"pith_short_8","alias_value":"WN6AD3D3","created_at":"2026-07-05T10:27:33Z"}],"graph_snapshots":[{"event_id":"sha256:e5cf5da2a3874cd1a6bf6f763caf6878f0bccbbc9e631b55026f39af20410a59","target":"graph","created_at":"2026-07-05T10:27:33Z","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/2503.06433/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"To improve the efficiency of distributed large language model (LLM) inference, various parallelization strategies, such as tensor and pipeline parallelism, have been proposed. However, the distinct computational characteristics inherent in the two stages of LLM inference-prefilling and decoding-render a single static parallelization strategy insufficient for the effective optimization of both stages. In this work, we present Seesaw, an LLM inference engine optimized for throughput-oriented tasks. The key idea behind Seesaw is dynamic model re-sharding, a technique that facilitates the dynamic ","authors_text":"Chenhao Jiang, Christina Giannoula, Gennady Pekhimenko, Kevin Song, Muralidhar Andoorveedu, Qidong Su, Wei Zhao, Xin Li, Zhanda Zhu","cross_cats":["cs.AI"],"headline":"","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.DC","submitted_at":"2025-03-09T04:14:06Z","title":"Seesaw: High-throughput LLM Inference via Model Re-sharding"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2503.06433","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":"sha256:0a3c7fd9375ad335fbf3d296fff3ac7258bd0015333aea6a51ac73e882fd8168","target":"record","created_at":"2026-07-05T10:27:33Z","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":"b4275e4c565a16b981b38f75480af1399650aed199e668313f528a35ab991fd0","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.DC","submitted_at":"2025-03-09T04:14:06Z","title_canon_sha256":"6f83b9db788e1fb994cedb5b584561deaa563d360c9dc606e7d5ef789ce837f0"},"schema_version":"1.0","source":{"id":"2503.06433","kind":"arxiv","version":1}},"canonical_sha256":"b37c01ec7bf842e0f695e33ae011f75fdb17a13ea5df6e911998b06951939165","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"b37c01ec7bf842e0f695e33ae011f75fdb17a13ea5df6e911998b06951939165","first_computed_at":"2026-07-05T10:27:33.623463Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T10:27:33.623463Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"KZ0orginYv9wrYyWWkir6NcoAotlgTnr2RCjcgHPsAH8OY3dG/UnqqYdojKqiHDDwHqJGB2SJf/WLmUCWoApDw==","signature_status":"signed_v1","signed_at":"2026-07-05T10:27:33.624025Z","signed_message":"canonical_sha256_bytes"},"source_id":"2503.06433","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:0a3c7fd9375ad335fbf3d296fff3ac7258bd0015333aea6a51ac73e882fd8168","sha256:e5cf5da2a3874cd1a6bf6f763caf6878f0bccbbc9e631b55026f39af20410a59"],"state_sha256":"6bcd0a761b2f14dae122afbc2e333aafcf191e5c055cc7dd2116a1683d5e051d"}