{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2024:LLRFTSPJSQ3AZQL4YXHAFCN3P2","short_pith_number":"pith:LLRFTSPJ","canonical_record":{"source":{"id":"2411.09072","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2024-11-13T22:55:45Z","cross_cats_sorted":[],"title_canon_sha256":"7cc5251801c3079506dfb84631b04aa67adbead59ec2b278a8f10d9766a66dfe","abstract_canon_sha256":"770b08e6e7aef4ecf754b4545ada15e80719892d1f5955f64f1c218cc16156c6"},"schema_version":"1.0"},"canonical_sha256":"5ae259c9e994360cc17cc5ce0289bb7ea2ef6f14c3a1e1a1aa695fc5d8fdebe6","source":{"kind":"arxiv","id":"2411.09072","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2411.09072","created_at":"2026-07-05T10:00:43Z"},{"alias_kind":"arxiv_version","alias_value":"2411.09072v2","created_at":"2026-07-05T10:00:43Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2411.09072","created_at":"2026-07-05T10:00:43Z"},{"alias_kind":"pith_short_12","alias_value":"LLRFTSPJSQ3A","created_at":"2026-07-05T10:00:43Z"},{"alias_kind":"pith_short_16","alias_value":"LLRFTSPJSQ3AZQL4","created_at":"2026-07-05T10:00:43Z"},{"alias_kind":"pith_short_8","alias_value":"LLRFTSPJ","created_at":"2026-07-05T10:00:43Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2024:LLRFTSPJSQ3AZQL4YXHAFCN3P2","target":"record","payload":{"canonical_record":{"source":{"id":"2411.09072","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2024-11-13T22:55:45Z","cross_cats_sorted":[],"title_canon_sha256":"7cc5251801c3079506dfb84631b04aa67adbead59ec2b278a8f10d9766a66dfe","abstract_canon_sha256":"770b08e6e7aef4ecf754b4545ada15e80719892d1f5955f64f1c218cc16156c6"},"schema_version":"1.0"},"canonical_sha256":"5ae259c9e994360cc17cc5ce0289bb7ea2ef6f14c3a1e1a1aa695fc5d8fdebe6","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:00:43.181997Z","signature_b64":"I17pcAbghO5uUXweDut3EsyWw2LR31q6n+bNCgmVbEVUEQpdRH/yn4TDJvenIaLsidAxZwMRyOL6KjQCI+vtDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"5ae259c9e994360cc17cc5ce0289bb7ea2ef6f14c3a1e1a1aa695fc5d8fdebe6","last_reissued_at":"2026-07-05T10:00:43.181599Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:00:43.181599Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2411.09072","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T10:00:43Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"pj+cmaykqG5Qn6JbDXSqBkpIZD2iMZ5QVIWfl0MYGhcGKFH8s2LIyTLrFF53qFtarScbF1FhRQoIjtaJED9kBw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-13T04:15:41.176441Z"},"content_sha256":"d643b1bf632f6a7daafc49df98915b8bcc7daba02550b5701caa783e0b90b1ce","schema_version":"1.0","event_id":"sha256:d643b1bf632f6a7daafc49df98915b8bcc7daba02550b5701caa783e0b90b1ce"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2024:LLRFTSPJSQ3AZQL4YXHAFCN3P2","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Continuous GNN-based Anomaly Detection on Edge using Efficient Adaptive Knowledge Graph Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Minhyoung Na, Mohsen Imani, Nathaniel Bastian, Ryozo Masukawa, Sanggeon Yun, William Youngwoo Chung","submitted_at":"2024-11-13T22:55:45Z","abstract_excerpt":"The increasing demand for robust security solutions across various industries has made Video Anomaly Detection (VAD) a critical task in applications such as intelligent surveillance, evidence investigation, and violence detection. Traditional approaches to VAD often rely on finetuning large pre-trained models, which can be computationally expensive and impractical for real-time or resource-constrained environments. To address this, MissionGNN introduced a more efficient method by training a graph neural network (GNN) using a fixed knowledge graph (KG) derived from large language models (LLMs) "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2411.09072","kind":"arxiv","version":2},"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/2411.09072/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":"2026-07-05T10:00:43Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"JrJLnCvG3ieHhKZn5kRoJLmIGpxV1Z30Cfh5pYBCUqZ86HXyERXueCD9JR8fZ4DrWDEpSZqrz//GQCKpNU2aDw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-13T04:15:41.176981Z"},"content_sha256":"6b8d0a41dc3ef74e4ce863177aaeef89081d584038a6bb22e583cf85d0da80f9","schema_version":"1.0","event_id":"sha256:6b8d0a41dc3ef74e4ce863177aaeef89081d584038a6bb22e583cf85d0da80f9"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/LLRFTSPJSQ3AZQL4YXHAFCN3P2/bundle.json","state_url":"https://pith.science/pith/LLRFTSPJSQ3AZQL4YXHAFCN3P2/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/LLRFTSPJSQ3AZQL4YXHAFCN3P2/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":"2026-08-13T04:15:41Z","links":{"resolver":"https://pith.science/pith/LLRFTSPJSQ3AZQL4YXHAFCN3P2","bundle":"https://pith.science/pith/LLRFTSPJSQ3AZQL4YXHAFCN3P2/bundle.json","state":"https://pith.science/pith/LLRFTSPJSQ3AZQL4YXHAFCN3P2/state.json","well_known_bundle":"https://pith.science/.well-known/pith/LLRFTSPJSQ3AZQL4YXHAFCN3P2/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:LLRFTSPJSQ3AZQL4YXHAFCN3P2","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":"770b08e6e7aef4ecf754b4545ada15e80719892d1f5955f64f1c218cc16156c6","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2024-11-13T22:55:45Z","title_canon_sha256":"7cc5251801c3079506dfb84631b04aa67adbead59ec2b278a8f10d9766a66dfe"},"schema_version":"1.0","source":{"id":"2411.09072","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2411.09072","created_at":"2026-07-05T10:00:43Z"},{"alias_kind":"arxiv_version","alias_value":"2411.09072v2","created_at":"2026-07-05T10:00:43Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2411.09072","created_at":"2026-07-05T10:00:43Z"},{"alias_kind":"pith_short_12","alias_value":"LLRFTSPJSQ3A","created_at":"2026-07-05T10:00:43Z"},{"alias_kind":"pith_short_16","alias_value":"LLRFTSPJSQ3AZQL4","created_at":"2026-07-05T10:00:43Z"},{"alias_kind":"pith_short_8","alias_value":"LLRFTSPJ","created_at":"2026-07-05T10:00:43Z"}],"graph_snapshots":[{"event_id":"sha256:6b8d0a41dc3ef74e4ce863177aaeef89081d584038a6bb22e583cf85d0da80f9","target":"graph","created_at":"2026-07-05T10:00:43Z","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/2411.09072/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"The increasing demand for robust security solutions across various industries has made Video Anomaly Detection (VAD) a critical task in applications such as intelligent surveillance, evidence investigation, and violence detection. Traditional approaches to VAD often rely on finetuning large pre-trained models, which can be computationally expensive and impractical for real-time or resource-constrained environments. To address this, MissionGNN introduced a more efficient method by training a graph neural network (GNN) using a fixed knowledge graph (KG) derived from large language models (LLMs) ","authors_text":"Minhyoung Na, Mohsen Imani, Nathaniel Bastian, Ryozo Masukawa, Sanggeon Yun, William Youngwoo Chung","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2024-11-13T22:55:45Z","title":"Continuous GNN-based Anomaly Detection on Edge using Efficient Adaptive Knowledge Graph Learning"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2411.09072","kind":"arxiv","version":2},"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:d643b1bf632f6a7daafc49df98915b8bcc7daba02550b5701caa783e0b90b1ce","target":"record","created_at":"2026-07-05T10:00:43Z","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":"770b08e6e7aef4ecf754b4545ada15e80719892d1f5955f64f1c218cc16156c6","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2024-11-13T22:55:45Z","title_canon_sha256":"7cc5251801c3079506dfb84631b04aa67adbead59ec2b278a8f10d9766a66dfe"},"schema_version":"1.0","source":{"id":"2411.09072","kind":"arxiv","version":2}},"canonical_sha256":"5ae259c9e994360cc17cc5ce0289bb7ea2ef6f14c3a1e1a1aa695fc5d8fdebe6","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"5ae259c9e994360cc17cc5ce0289bb7ea2ef6f14c3a1e1a1aa695fc5d8fdebe6","first_computed_at":"2026-07-05T10:00:43.181599Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T10:00:43.181599Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"I17pcAbghO5uUXweDut3EsyWw2LR31q6n+bNCgmVbEVUEQpdRH/yn4TDJvenIaLsidAxZwMRyOL6KjQCI+vtDQ==","signature_status":"signed_v1","signed_at":"2026-07-05T10:00:43.181997Z","signed_message":"canonical_sha256_bytes"},"source_id":"2411.09072","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:d643b1bf632f6a7daafc49df98915b8bcc7daba02550b5701caa783e0b90b1ce","sha256:6b8d0a41dc3ef74e4ce863177aaeef89081d584038a6bb22e583cf85d0da80f9"],"state_sha256":"94fa541833f03d4fd0375c2fb5893aa1b3fc7ee0b98f39d77491eef857c20b42"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"YeLfRYUIDHIfM9+w5Khr9ZlmBgIlnBKeL+HWHjL6gyIdkc7Yw8vctWaMVznrRrouY4fPo9QXAD+7zbuw98+ACQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-13T04:15:41.183059Z","bundle_sha256":"b5239a9015d86e2b60efe018ab4971456d3d6451b091677d55e54f3c73197711"}}