{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2025:VKJ2W63RCTFWGP5ISRUJ3YD54L","short_pith_number":"pith:VKJ2W63R","canonical_record":{"source":{"id":"2505.12526","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-05-18T19:30:33Z","cross_cats_sorted":[],"title_canon_sha256":"2a7c1f9b6fd7a0b6e2e36bd8a571300f86328ad0bbc5fe7c0d6d16bc3612e6af","abstract_canon_sha256":"ac8292d38071d9bfa8630e7588729fcb2412429f82155fcb996febfff192ee10"},"schema_version":"1.0"},"canonical_sha256":"aa93ab7b7114cb633fa894689de07de2f79bdd656e3d2ef839cdf9ee13ba9d21","source":{"kind":"arxiv","id":"2505.12526","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2505.12526","created_at":"2026-06-30T00:15:07Z"},{"alias_kind":"arxiv_version","alias_value":"2505.12526v2","created_at":"2026-06-30T00:15:07Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2505.12526","created_at":"2026-06-30T00:15:07Z"},{"alias_kind":"pith_short_12","alias_value":"VKJ2W63RCTFW","created_at":"2026-06-30T00:15:07Z"},{"alias_kind":"pith_short_16","alias_value":"VKJ2W63RCTFWGP5I","created_at":"2026-06-30T00:15:07Z"},{"alias_kind":"pith_short_8","alias_value":"VKJ2W63R","created_at":"2026-06-30T00:15:07Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2025:VKJ2W63RCTFWGP5ISRUJ3YD54L","target":"record","payload":{"canonical_record":{"source":{"id":"2505.12526","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-05-18T19:30:33Z","cross_cats_sorted":[],"title_canon_sha256":"2a7c1f9b6fd7a0b6e2e36bd8a571300f86328ad0bbc5fe7c0d6d16bc3612e6af","abstract_canon_sha256":"ac8292d38071d9bfa8630e7588729fcb2412429f82155fcb996febfff192ee10"},"schema_version":"1.0"},"canonical_sha256":"aa93ab7b7114cb633fa894689de07de2f79bdd656e3d2ef839cdf9ee13ba9d21","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-30T00:15:07.314805Z","signature_b64":"dmQdyHgRiBpbLXGr9WYUylbj8p5ipBebKZYEICf6Eaxa+zGE32nu6j/ZLcZgHMB8yLBqrRSyffSrfSEzK2LcDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"aa93ab7b7114cb633fa894689de07de2f79bdd656e3d2ef839cdf9ee13ba9d21","last_reissued_at":"2026-06-30T00:15:07.314313Z","signature_status":"signed_v1","first_computed_at":"2026-06-30T00:15:07.314313Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2505.12526","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":"2026-06-30T00:15:07Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"WwhLrRLLLeYVPAf/P7M8lviWyjyKzD/IUoCpQjT5r+pxlvxNN+yruFDTfJbFirNb4jQalRtRgc19VLQeyhElAw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-18T13:26:27.514381Z"},"content_sha256":"e4adfe276529d0049a86420929c79dc7377f60e44c1afb020cc397c605bade9d","schema_version":"1.0","event_id":"sha256:e4adfe276529d0049a86420929c79dc7377f60e44c1afb020cc397c605bade9d"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2025:VKJ2W63RCTFWGP5ISRUJ3YD54L","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Never Skip a Batch: Dense Learning of Temporal GNNs via Adaptive Pseudo-Supervision","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Alexander Panyshev, Alexey Zaytsev, Dmitry Vinichenko, Oleg Travkin, Roman Alferov","submitted_at":"2025-05-18T19:30:33Z","abstract_excerpt":"Temporal graph networks suffer from irregular supervision in realworld dynamic graphs, as most minibatches contain few labeled events. The lack of labels leads to high-variance gradient updates and, consequently, slow wall-clock convergence. To constructively reduce sparsity, our Moving-Averaged Labels (MAL) assigns soft pseudo-targets based on past supervised signals using a running label distribution while leaving the loss and the model architecture unchanged. Thus, supervision gaps are replaced with informative signals independent of a temporal graph model and the message passing or memory "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2505.12526","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/2505.12526/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":"2026-06-30T00:15:07Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"ZuNIlc6TloSuK1q1riWcXgibrPBl/dLSzy0CIPz0/Ad80x8LP4n8GbBhxL/S/OYNQ1iWZ4XW2c48gClc9tAVBA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-18T13:26:27.515228Z"},"content_sha256":"f6fdae0450ca5b7529868df109ed901e8239c71e09678f219a3d6cfe06d57393","schema_version":"1.0","event_id":"sha256:f6fdae0450ca5b7529868df109ed901e8239c71e09678f219a3d6cfe06d57393"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/VKJ2W63RCTFWGP5ISRUJ3YD54L/bundle.json","state_url":"https://pith.science/pith/VKJ2W63RCTFWGP5ISRUJ3YD54L/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/VKJ2W63RCTFWGP5ISRUJ3YD54L/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":"2026-08-18T13:26:27Z","links":{"resolver":"https://pith.science/pith/VKJ2W63RCTFWGP5ISRUJ3YD54L","bundle":"https://pith.science/pith/VKJ2W63RCTFWGP5ISRUJ3YD54L/bundle.json","state":"https://pith.science/pith/VKJ2W63RCTFWGP5ISRUJ3YD54L/state.json","well_known_bundle":"https://pith.science/.well-known/pith/VKJ2W63RCTFWGP5ISRUJ3YD54L/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:VKJ2W63RCTFWGP5ISRUJ3YD54L","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":"ac8292d38071d9bfa8630e7588729fcb2412429f82155fcb996febfff192ee10","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-05-18T19:30:33Z","title_canon_sha256":"2a7c1f9b6fd7a0b6e2e36bd8a571300f86328ad0bbc5fe7c0d6d16bc3612e6af"},"schema_version":"1.0","source":{"id":"2505.12526","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2505.12526","created_at":"2026-06-30T00:15:07Z"},{"alias_kind":"arxiv_version","alias_value":"2505.12526v2","created_at":"2026-06-30T00:15:07Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2505.12526","created_at":"2026-06-30T00:15:07Z"},{"alias_kind":"pith_short_12","alias_value":"VKJ2W63RCTFW","created_at":"2026-06-30T00:15:07Z"},{"alias_kind":"pith_short_16","alias_value":"VKJ2W63RCTFWGP5I","created_at":"2026-06-30T00:15:07Z"},{"alias_kind":"pith_short_8","alias_value":"VKJ2W63R","created_at":"2026-06-30T00:15:07Z"}],"graph_snapshots":[{"event_id":"sha256:f6fdae0450ca5b7529868df109ed901e8239c71e09678f219a3d6cfe06d57393","target":"graph","created_at":"2026-06-30T00:15:07Z","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/2505.12526/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Temporal graph networks suffer from irregular supervision in realworld dynamic graphs, as most minibatches contain few labeled events. The lack of labels leads to high-variance gradient updates and, consequently, slow wall-clock convergence. To constructively reduce sparsity, our Moving-Averaged Labels (MAL) assigns soft pseudo-targets based on past supervised signals using a running label distribution while leaving the loss and the model architecture unchanged. Thus, supervision gaps are replaced with informative signals independent of a temporal graph model and the message passing or memory ","authors_text":"Alexander Panyshev, Alexey Zaytsev, Dmitry Vinichenko, Oleg Travkin, Roman Alferov","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-05-18T19:30:33Z","title":"Never Skip a Batch: Dense Learning of Temporal GNNs via Adaptive Pseudo-Supervision"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2505.12526","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:e4adfe276529d0049a86420929c79dc7377f60e44c1afb020cc397c605bade9d","target":"record","created_at":"2026-06-30T00:15:07Z","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":"ac8292d38071d9bfa8630e7588729fcb2412429f82155fcb996febfff192ee10","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-05-18T19:30:33Z","title_canon_sha256":"2a7c1f9b6fd7a0b6e2e36bd8a571300f86328ad0bbc5fe7c0d6d16bc3612e6af"},"schema_version":"1.0","source":{"id":"2505.12526","kind":"arxiv","version":2}},"canonical_sha256":"aa93ab7b7114cb633fa894689de07de2f79bdd656e3d2ef839cdf9ee13ba9d21","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"aa93ab7b7114cb633fa894689de07de2f79bdd656e3d2ef839cdf9ee13ba9d21","first_computed_at":"2026-06-30T00:15:07.314313Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-06-30T00:15:07.314313Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"dmQdyHgRiBpbLXGr9WYUylbj8p5ipBebKZYEICf6Eaxa+zGE32nu6j/ZLcZgHMB8yLBqrRSyffSrfSEzK2LcDg==","signature_status":"signed_v1","signed_at":"2026-06-30T00:15:07.314805Z","signed_message":"canonical_sha256_bytes"},"source_id":"2505.12526","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:e4adfe276529d0049a86420929c79dc7377f60e44c1afb020cc397c605bade9d","sha256:f6fdae0450ca5b7529868df109ed901e8239c71e09678f219a3d6cfe06d57393"],"state_sha256":"f79305e6109b71a2311badf6d249008554b64db8f52aa18f707c22d7515d9f59"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"2N/qeDEdh4e8+Z0YZw6oiZrM+6EAZFg4ql1DaqeYrBMuaYcTyXMZHwHSOPiIi54SKlPnQdjEhWJ74hYqzo/XDw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-18T13:26:27.521411Z","bundle_sha256":"759747ea3f4d48f9926754f7a9234ab96a863856e14eed067f4ec7b240a23f1c"}}