{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2025:37N2353SCPKZIRTCQKNJ5NZMSH","short_pith_number":"pith:37N2353S","canonical_record":{"source":{"id":"2512.22105","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2025-12-26T18:19:39Z","cross_cats_sorted":[],"title_canon_sha256":"e480a818d92911148a18f51acd970180dee612a44a66feddda8822d17a6622c6","abstract_canon_sha256":"71cdab95f6c051d416ba627f349f44f5f4b72dcaedd17a06efe47686e40a7b22"},"schema_version":"1.0"},"canonical_sha256":"dfdbadf77213d5944662829a9eb72c91e3d21de6a051c4c72dc1b9a1489eaff4","source":{"kind":"arxiv","id":"2512.22105","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2512.22105","created_at":"2026-06-04T01:08:38Z"},{"alias_kind":"arxiv_version","alias_value":"2512.22105v2","created_at":"2026-06-04T01:08:38Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2512.22105","created_at":"2026-06-04T01:08:38Z"},{"alias_kind":"pith_short_12","alias_value":"37N2353SCPKZ","created_at":"2026-06-04T01:08:38Z"},{"alias_kind":"pith_short_16","alias_value":"37N2353SCPKZIRTC","created_at":"2026-06-04T01:08:38Z"},{"alias_kind":"pith_short_8","alias_value":"37N2353S","created_at":"2026-06-04T01:08:38Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2025:37N2353SCPKZIRTCQKNJ5NZMSH","target":"record","payload":{"canonical_record":{"source":{"id":"2512.22105","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2025-12-26T18:19:39Z","cross_cats_sorted":[],"title_canon_sha256":"e480a818d92911148a18f51acd970180dee612a44a66feddda8822d17a6622c6","abstract_canon_sha256":"71cdab95f6c051d416ba627f349f44f5f4b72dcaedd17a06efe47686e40a7b22"},"schema_version":"1.0"},"canonical_sha256":"dfdbadf77213d5944662829a9eb72c91e3d21de6a051c4c72dc1b9a1489eaff4","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-04T01:08:38.760293Z","signature_b64":"7wr777AMGRMRFI2R7SiPL1qsRB3MXHi1KcvFIy0N897QkWd1HgVvrMFv3HtLe891t4OIhW7nrcSLFmgZNaG2CA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"dfdbadf77213d5944662829a9eb72c91e3d21de6a051c4c72dc1b9a1489eaff4","last_reissued_at":"2026-06-04T01:08:38.759388Z","signature_status":"signed_v1","first_computed_at":"2026-06-04T01:08:38.759388Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2512.22105","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":"2026-06-04T01:08:38Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"mPd/QvZ28x3i/CDxusd5PmG8fiHNnHsdxRjhFdpYhZ0gRpv4erw5m5zFgnabeVuwt4rVtpTh6zKQqTmtNXpGDg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-04T05:43:16.706162Z"},"content_sha256":"54952e92977037d15dfa053d7688f2ee2465f51313229a63f057753822f61f6f","schema_version":"1.0","event_id":"sha256:54952e92977037d15dfa053d7688f2ee2465f51313229a63f057753822f61f6f"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2025:37N2353SCPKZIRTCQKNJ5NZMSH","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Learning Association via Track-Detection Matching for Multi-Object Tracking","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Momir Ad\\v{z}emovi\\'c","submitted_at":"2025-12-26T18:19:39Z","abstract_excerpt":"Multi-object tracking aims to maintain object identities over time by associating detections across video frames. Two dominant paradigms exist in literature: tracking-by-detection methods, which are computationally efficient but rely on handcrafted association heuristics, and end-to-end approaches, which learn association from data at the cost of higher computational complexity. We propose Track-Detection Link Prediction (TDLP), a tracking-by-detection method that performs per-frame association via link prediction between tracks and detections, i.e., by predicting the correct continuation of e"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2512.22105","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/2512.22105/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":"2026-06-04T01:08:38Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"M7E+f09g+2pgMWl8bQSzDsvDaGL1kIZckZmICgbU1NSEiCA25cRnpU+0MwQDBrXgf68gzhg9fWRfi5DPkvU8Dg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-04T05:43:16.706677Z"},"content_sha256":"9accbbfd2a91865efd1675678b088aa08dd6aa53bee02e48d6414b57a7a355f9","schema_version":"1.0","event_id":"sha256:9accbbfd2a91865efd1675678b088aa08dd6aa53bee02e48d6414b57a7a355f9"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/37N2353SCPKZIRTCQKNJ5NZMSH/bundle.json","state_url":"https://pith.science/pith/37N2353SCPKZIRTCQKNJ5NZMSH/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/37N2353SCPKZIRTCQKNJ5NZMSH/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":"2026-08-04T05:43:16Z","links":{"resolver":"https://pith.science/pith/37N2353SCPKZIRTCQKNJ5NZMSH","bundle":"https://pith.science/pith/37N2353SCPKZIRTCQKNJ5NZMSH/bundle.json","state":"https://pith.science/pith/37N2353SCPKZIRTCQKNJ5NZMSH/state.json","well_known_bundle":"https://pith.science/.well-known/pith/37N2353SCPKZIRTCQKNJ5NZMSH/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:37N2353SCPKZIRTCQKNJ5NZMSH","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":"71cdab95f6c051d416ba627f349f44f5f4b72dcaedd17a06efe47686e40a7b22","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2025-12-26T18:19:39Z","title_canon_sha256":"e480a818d92911148a18f51acd970180dee612a44a66feddda8822d17a6622c6"},"schema_version":"1.0","source":{"id":"2512.22105","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2512.22105","created_at":"2026-06-04T01:08:38Z"},{"alias_kind":"arxiv_version","alias_value":"2512.22105v2","created_at":"2026-06-04T01:08:38Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2512.22105","created_at":"2026-06-04T01:08:38Z"},{"alias_kind":"pith_short_12","alias_value":"37N2353SCPKZ","created_at":"2026-06-04T01:08:38Z"},{"alias_kind":"pith_short_16","alias_value":"37N2353SCPKZIRTC","created_at":"2026-06-04T01:08:38Z"},{"alias_kind":"pith_short_8","alias_value":"37N2353S","created_at":"2026-06-04T01:08:38Z"}],"graph_snapshots":[{"event_id":"sha256:9accbbfd2a91865efd1675678b088aa08dd6aa53bee02e48d6414b57a7a355f9","target":"graph","created_at":"2026-06-04T01:08:38Z","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/2512.22105/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Multi-object tracking aims to maintain object identities over time by associating detections across video frames. Two dominant paradigms exist in literature: tracking-by-detection methods, which are computationally efficient but rely on handcrafted association heuristics, and end-to-end approaches, which learn association from data at the cost of higher computational complexity. We propose Track-Detection Link Prediction (TDLP), a tracking-by-detection method that performs per-frame association via link prediction between tracks and detections, i.e., by predicting the correct continuation of e","authors_text":"Momir Ad\\v{z}emovi\\'c","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2025-12-26T18:19:39Z","title":"Learning Association via Track-Detection Matching for Multi-Object Tracking"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2512.22105","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":"sha256:54952e92977037d15dfa053d7688f2ee2465f51313229a63f057753822f61f6f","target":"record","created_at":"2026-06-04T01:08:38Z","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":"71cdab95f6c051d416ba627f349f44f5f4b72dcaedd17a06efe47686e40a7b22","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2025-12-26T18:19:39Z","title_canon_sha256":"e480a818d92911148a18f51acd970180dee612a44a66feddda8822d17a6622c6"},"schema_version":"1.0","source":{"id":"2512.22105","kind":"arxiv","version":2}},"canonical_sha256":"dfdbadf77213d5944662829a9eb72c91e3d21de6a051c4c72dc1b9a1489eaff4","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"dfdbadf77213d5944662829a9eb72c91e3d21de6a051c4c72dc1b9a1489eaff4","first_computed_at":"2026-06-04T01:08:38.759388Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-06-04T01:08:38.759388Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"7wr777AMGRMRFI2R7SiPL1qsRB3MXHi1KcvFIy0N897QkWd1HgVvrMFv3HtLe891t4OIhW7nrcSLFmgZNaG2CA==","signature_status":"signed_v1","signed_at":"2026-06-04T01:08:38.760293Z","signed_message":"canonical_sha256_bytes"},"source_id":"2512.22105","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:54952e92977037d15dfa053d7688f2ee2465f51313229a63f057753822f61f6f","sha256:9accbbfd2a91865efd1675678b088aa08dd6aa53bee02e48d6414b57a7a355f9"],"state_sha256":"ff3e28f96d7c6a566336be757a992fb34a542e24602d1581175ebf7b2cda8a6f"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"uCuzVq197FtoYjtDFMtRUKX+quvk7/GtpznhkdFOolL7mKWucIgl3pkJ99WFi/qkkMXSOR/bW1V0sx4ppkshCw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-04T05:43:16.710339Z","bundle_sha256":"5f36b3f9a47ba40be001767514485b750c44e8c138ec6c525df7508de2ae07da"}}