{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2026:VDTHHZO4DLG3SQ4CIJWUIPYSR4","short_pith_number":"pith:VDTHHZO4","canonical_record":{"source":{"id":"2607.05095","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2026-07-06T13:54:01Z","cross_cats_sorted":[],"title_canon_sha256":"43fcacc34e4d71e6482c08a215b6f71508b691023105c6c46727a17292df5701","abstract_canon_sha256":"5d2cf4804778be7f78be5d0aaf5da64ff4af595e319b02dce301de88f73a790f"},"schema_version":"1.0"},"canonical_sha256":"a8e673e5dc1acdb94382426d443f128f344443692cb6755aa10e2e4e413e628d","source":{"kind":"arxiv","id":"2607.05095","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2607.05095","created_at":"2026-07-07T03:19:15Z"},{"alias_kind":"arxiv_version","alias_value":"2607.05095v1","created_at":"2026-07-07T03:19:15Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2607.05095","created_at":"2026-07-07T03:19:15Z"},{"alias_kind":"pith_short_12","alias_value":"VDTHHZO4DLG3","created_at":"2026-07-07T03:19:15Z"},{"alias_kind":"pith_short_16","alias_value":"VDTHHZO4DLG3SQ4C","created_at":"2026-07-07T03:19:15Z"},{"alias_kind":"pith_short_8","alias_value":"VDTHHZO4","created_at":"2026-07-07T03:19:15Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2026:VDTHHZO4DLG3SQ4CIJWUIPYSR4","target":"record","payload":{"canonical_record":{"source":{"id":"2607.05095","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2026-07-06T13:54:01Z","cross_cats_sorted":[],"title_canon_sha256":"43fcacc34e4d71e6482c08a215b6f71508b691023105c6c46727a17292df5701","abstract_canon_sha256":"5d2cf4804778be7f78be5d0aaf5da64ff4af595e319b02dce301de88f73a790f"},"schema_version":"1.0"},"canonical_sha256":"a8e673e5dc1acdb94382426d443f128f344443692cb6755aa10e2e4e413e628d","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-07T03:19:15.898404Z","signature_b64":"QIrd8CkXSleLplqqKEvtENhGnkcQmhsEDj3VJlhcagwO/TT0GBP7Z5778JFcq0GIKLg7kk+6lezf04jpIMK6Bg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"a8e673e5dc1acdb94382426d443f128f344443692cb6755aa10e2e4e413e628d","last_reissued_at":"2026-07-07T03:19:15.897953Z","signature_status":"signed_v1","first_computed_at":"2026-07-07T03:19:15.897953Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2607.05095","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":"2026-07-07T03:19:15Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Z73mcr1CxfPICPdf5DoscFmx9yat2UexP8uUptarIPJy3oqFnZH6N5h/odSuuB6zDM6A45V1Ial+Jp01/4O7Cg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-03T19:21:03.178885Z"},"content_sha256":"d6982c5a0cf84c52480eb4ebc83ea4f3c7212956da3b5d3cdb44dd90b0b292c2","schema_version":"1.0","event_id":"sha256:d6982c5a0cf84c52480eb4ebc83ea4f3c7212956da3b5d3cdb44dd90b0b292c2"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2026:VDTHHZO4DLG3SQ4CIJWUIPYSR4","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"FAST: A Holistic Framework for Optimizing Memory-I/O, Computation, and Sampling in Temporal GNN Training","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Hao Chen, Kai Sheng, Lei Liu, Qingrui Zhu, Xin He, Yushu Cai","submitted_at":"2026-07-06T13:54:01Z","abstract_excerpt":"Temporal Graph Neural Networks (TGNNs) are widely used for learning from dynamic graphs in applications such as recommendation, social network analysis, and traffic forecasting. However, scaling TGNN training to large dynamic graphs remains challenging due to three intertwined bottlenecks: memory I/O, irregular computation, and temporal neighbor sampling. Existing systems often optimize these stages in isolation, leaving substantial performance headroom on the table. We present FAST, a holistic framework that accelerates end-to-end TGNN training by jointly optimizing sampling, memory I/O, and "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2607.05095","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/2607.05095/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":"2026-07-07T03:19:15Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"UnVoJLtVCljfrnX7SEJ8FKxvSsV+fS4Zs5B1VoGYlpgTF0sBMK1GTSw8+QcgKjRbg2aTONfxrTzPYAQ67LiqAQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-03T19:21:03.179388Z"},"content_sha256":"1fe05df72e845628aa8321529e5a36605de7a8cbdbdfb75fb1324960065838dc","schema_version":"1.0","event_id":"sha256:1fe05df72e845628aa8321529e5a36605de7a8cbdbdfb75fb1324960065838dc"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/VDTHHZO4DLG3SQ4CIJWUIPYSR4/bundle.json","state_url":"https://pith.science/pith/VDTHHZO4DLG3SQ4CIJWUIPYSR4/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/VDTHHZO4DLG3SQ4CIJWUIPYSR4/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":"2026-08-03T19:21:03Z","links":{"resolver":"https://pith.science/pith/VDTHHZO4DLG3SQ4CIJWUIPYSR4","bundle":"https://pith.science/pith/VDTHHZO4DLG3SQ4CIJWUIPYSR4/bundle.json","state":"https://pith.science/pith/VDTHHZO4DLG3SQ4CIJWUIPYSR4/state.json","well_known_bundle":"https://pith.science/.well-known/pith/VDTHHZO4DLG3SQ4CIJWUIPYSR4/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:VDTHHZO4DLG3SQ4CIJWUIPYSR4","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":"5d2cf4804778be7f78be5d0aaf5da64ff4af595e319b02dce301de88f73a790f","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2026-07-06T13:54:01Z","title_canon_sha256":"43fcacc34e4d71e6482c08a215b6f71508b691023105c6c46727a17292df5701"},"schema_version":"1.0","source":{"id":"2607.05095","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2607.05095","created_at":"2026-07-07T03:19:15Z"},{"alias_kind":"arxiv_version","alias_value":"2607.05095v1","created_at":"2026-07-07T03:19:15Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2607.05095","created_at":"2026-07-07T03:19:15Z"},{"alias_kind":"pith_short_12","alias_value":"VDTHHZO4DLG3","created_at":"2026-07-07T03:19:15Z"},{"alias_kind":"pith_short_16","alias_value":"VDTHHZO4DLG3SQ4C","created_at":"2026-07-07T03:19:15Z"},{"alias_kind":"pith_short_8","alias_value":"VDTHHZO4","created_at":"2026-07-07T03:19:15Z"}],"graph_snapshots":[{"event_id":"sha256:1fe05df72e845628aa8321529e5a36605de7a8cbdbdfb75fb1324960065838dc","target":"graph","created_at":"2026-07-07T03:19:15Z","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/2607.05095/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Temporal Graph Neural Networks (TGNNs) are widely used for learning from dynamic graphs in applications such as recommendation, social network analysis, and traffic forecasting. However, scaling TGNN training to large dynamic graphs remains challenging due to three intertwined bottlenecks: memory I/O, irregular computation, and temporal neighbor sampling. Existing systems often optimize these stages in isolation, leaving substantial performance headroom on the table. We present FAST, a holistic framework that accelerates end-to-end TGNN training by jointly optimizing sampling, memory I/O, and ","authors_text":"Hao Chen, Kai Sheng, Lei Liu, Qingrui Zhu, Xin He, Yushu Cai","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2026-07-06T13:54:01Z","title":"FAST: A Holistic Framework for Optimizing Memory-I/O, Computation, and Sampling in Temporal GNN Training"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2607.05095","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:d6982c5a0cf84c52480eb4ebc83ea4f3c7212956da3b5d3cdb44dd90b0b292c2","target":"record","created_at":"2026-07-07T03:19:15Z","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":"5d2cf4804778be7f78be5d0aaf5da64ff4af595e319b02dce301de88f73a790f","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2026-07-06T13:54:01Z","title_canon_sha256":"43fcacc34e4d71e6482c08a215b6f71508b691023105c6c46727a17292df5701"},"schema_version":"1.0","source":{"id":"2607.05095","kind":"arxiv","version":1}},"canonical_sha256":"a8e673e5dc1acdb94382426d443f128f344443692cb6755aa10e2e4e413e628d","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"a8e673e5dc1acdb94382426d443f128f344443692cb6755aa10e2e4e413e628d","first_computed_at":"2026-07-07T03:19:15.897953Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-07T03:19:15.897953Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"QIrd8CkXSleLplqqKEvtENhGnkcQmhsEDj3VJlhcagwO/TT0GBP7Z5778JFcq0GIKLg7kk+6lezf04jpIMK6Bg==","signature_status":"signed_v1","signed_at":"2026-07-07T03:19:15.898404Z","signed_message":"canonical_sha256_bytes"},"source_id":"2607.05095","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:d6982c5a0cf84c52480eb4ebc83ea4f3c7212956da3b5d3cdb44dd90b0b292c2","sha256:1fe05df72e845628aa8321529e5a36605de7a8cbdbdfb75fb1324960065838dc"],"state_sha256":"38bcd4b9f18696b28c372b4f931324e26c70e7ffead2d170f42b6ea508f38ff1"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"ktKEiSYKaUmsiltIkJGf3QaG0teYUO3ecqrINMuZHWHCSEUFVC363bK5q2g4yLn94o7KlVK5hMV3dpyFM4ptAw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-03T19:21:03.185425Z","bundle_sha256":"e2398cc4efc0278b604a5b912cfbb1da6efa423da9044625060ea646f0b69acc"}}