{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:MBWEQDKASRJBFPVLGGZMB6NXP4","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":"138f456466dda2580c15859415128c3e4ebd8dc2bc6550a14f030d4fae58eaa3","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.DC","submitted_at":"2026-05-18T17:44:29Z","title_canon_sha256":"eae616d4169f48c0336242ff691e57d5c83aca1ee406c23f67cfc92815127476"},"schema_version":"1.0","source":{"id":"2605.18710","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2605.18710","created_at":"2026-05-20T00:06:16Z"},{"alias_kind":"arxiv_version","alias_value":"2605.18710v1","created_at":"2026-05-20T00:06:16Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.18710","created_at":"2026-05-20T00:06:16Z"},{"alias_kind":"pith_short_12","alias_value":"MBWEQDKASRJB","created_at":"2026-05-20T00:06:16Z"},{"alias_kind":"pith_short_16","alias_value":"MBWEQDKASRJBFPVL","created_at":"2026-05-20T00:06:16Z"},{"alias_kind":"pith_short_8","alias_value":"MBWEQDKA","created_at":"2026-05-20T00:06:16Z"}],"graph_snapshots":[{"event_id":"sha256:033364e8a370e5f8d1af23e045c323823a4c5fa8430c1e46e8d9d1b2736df493","target":"graph","created_at":"2026-05-20T00:06:16Z","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":[{"findings_count":0,"name":"claim_evidence","ran_at":"2026-05-20T00:01:59.060021Z","status":"completed","version":"1.0.0"}],"endpoint":"/pith/2605.18710/integrity.json","findings":[],"snapshot_sha256":"a0a25b0fed78f5faf5296dbca98426f6fa7e67217c86ca5f44edcefa1d9d9b4a","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"With the wide adoption of Multimodal Models (MMs) in real-world scenarios, it is significant to efficiently train emerging MMs that exhibit increasingly complex module architectures. For MM deployment, existing works allocate a GPU to only one MM module in a temporal-multiplexing manner; this compromises training efficiency because a single module often fails to achieve high GPU utilization. To improve GPU utilization and enable efficient MM training, we propose deploying MMs in a temporal-spatial multiplexing manner, allowing multiple MM modules to colocate on a GPU with well-controlled resou","authors_text":"Anbang Wu, Chen Chen, Chunyu Xue, Qizhen Weng, Quan Chen, Yanbo Wang, Yin Chen, Yu Feng, Yuxuan Wang","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.DC","submitted_at":"2026-05-18T17:44:29Z","title":"Mosaic: Towards Efficient Training of Multimodal Models with Spatial Resource Multiplexing"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2605.18710","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:cc17ac7be0ac6574b1a10ce26edd0be3278765b2a0a72f93f6fe8435526c0c72","target":"record","created_at":"2026-05-20T00:06:16Z","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":"138f456466dda2580c15859415128c3e4ebd8dc2bc6550a14f030d4fae58eaa3","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.DC","submitted_at":"2026-05-18T17:44:29Z","title_canon_sha256":"eae616d4169f48c0336242ff691e57d5c83aca1ee406c23f67cfc92815127476"},"schema_version":"1.0","source":{"id":"2605.18710","kind":"arxiv","version":1}},"canonical_sha256":"606c480d40945212beab31b2c0f9b77f37c7ab74f1b911b51620bbe2b9973bae","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"606c480d40945212beab31b2c0f9b77f37c7ab74f1b911b51620bbe2b9973bae","first_computed_at":"2026-05-20T00:06:16.486346Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-20T00:06:16.486346Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"/yCc/P8iKPmem55DcmszAdJeYQd2+/D/cCkOV4Mb6U4tbPsssrqgnZT11rmZltaO/DlCE5JWxODJXNK4XmkwAA==","signature_status":"signed_v1","signed_at":"2026-05-20T00:06:16.487076Z","signed_message":"canonical_sha256_bytes"},"source_id":"2605.18710","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:cc17ac7be0ac6574b1a10ce26edd0be3278765b2a0a72f93f6fe8435526c0c72","sha256:033364e8a370e5f8d1af23e045c323823a4c5fa8430c1e46e8d9d1b2736df493"],"state_sha256":"c2225f732d5c6e73fb2b48ffcb932cd800cc960348814fb9f2a5fa720043f9a6"}