{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2024:MCH77ZRRTAGB7T2NRFMCZDRKYI","short_pith_number":"pith:MCH77ZRR","canonical_record":{"source":{"id":"2410.12109","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2024-10-15T23:16:28Z","cross_cats_sorted":["cs.CV"],"title_canon_sha256":"3e7ff4e98f29fe6c87d41f3ccd4f03a100b960a3b1abb6364372a5501b18bf55","abstract_canon_sha256":"81fa72deaa4a58f1af98004eade4806a341ce6c2900b4266df1e3889c8d18c15"},"schema_version":"1.0"},"canonical_sha256":"608fffe631980c1fcf4d89582c8e2ac23fbd48be599c269ed2e372124fc89829","source":{"kind":"arxiv","id":"2410.12109","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2410.12109","created_at":"2026-07-05T09:21:14Z"},{"alias_kind":"arxiv_version","alias_value":"2410.12109v1","created_at":"2026-07-05T09:21:14Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2410.12109","created_at":"2026-07-05T09:21:14Z"},{"alias_kind":"pith_short_12","alias_value":"MCH77ZRRTAGB","created_at":"2026-07-05T09:21:14Z"},{"alias_kind":"pith_short_16","alias_value":"MCH77ZRRTAGB7T2N","created_at":"2026-07-05T09:21:14Z"},{"alias_kind":"pith_short_8","alias_value":"MCH77ZRR","created_at":"2026-07-05T09:21:14Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2024:MCH77ZRRTAGB7T2NRFMCZDRKYI","target":"record","payload":{"canonical_record":{"source":{"id":"2410.12109","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2024-10-15T23:16:28Z","cross_cats_sorted":["cs.CV"],"title_canon_sha256":"3e7ff4e98f29fe6c87d41f3ccd4f03a100b960a3b1abb6364372a5501b18bf55","abstract_canon_sha256":"81fa72deaa4a58f1af98004eade4806a341ce6c2900b4266df1e3889c8d18c15"},"schema_version":"1.0"},"canonical_sha256":"608fffe631980c1fcf4d89582c8e2ac23fbd48be599c269ed2e372124fc89829","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:21:14.338874Z","signature_b64":"d0jYAFaIbUvmj/y2Qn9RvHXkV/ovZ/uaGqT3bB6EeiFJjyaG/hor/PdAeKwXAjdC9PZvOXeAOfUDYrZn4c3EBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"608fffe631980c1fcf4d89582c8e2ac23fbd48be599c269ed2e372124fc89829","last_reissued_at":"2026-07-05T09:21:14.338380Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:21:14.338380Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2410.12109","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":"2026-07-05T09:21:14Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"q9hdH2Yw4BcrZPyvi04o28VUw9CzTVMrSwouGX2Kb8my0fGsMZeLd09DkEiKlXJAcafTBGWqoOrlwzn4adI3Aw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-10T21:25:24.267660Z"},"content_sha256":"613db020cb4d1c1b2b155ab3be0fe22bc00afaa6d04bebceb39231c7b6b35eba","schema_version":"1.0","event_id":"sha256:613db020cb4d1c1b2b155ab3be0fe22bc00afaa6d04bebceb39231c7b6b35eba"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2024:MCH77ZRRTAGB7T2NRFMCZDRKYI","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"OMCAT: Omni Context Aware Transformer","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.CV"],"primary_cat":"cs.CL","authors_text":"Andrew Tao, Arushi Goel, Bryan Catanzaro, Karan Sapra, Matthieu Le, Rafael Valle","submitted_at":"2024-10-15T23:16:28Z","abstract_excerpt":"Large Language Models (LLMs) have made significant strides in text generation and comprehension, with recent advancements extending into multimodal LLMs that integrate visual and audio inputs. However, these models continue to struggle with fine-grained, cross-modal temporal understanding, particularly when correlating events across audio and video streams. We address these challenges with two key contributions: a new dataset and model, called OCTAV and OMCAT respectively. OCTAV (Omni Context and Temporal Audio Video) is a novel dataset designed to capture event transitions across audio and vi"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2410.12109","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/2410.12109/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":"2026-07-05T09:21:14Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"IBMV1Ff53cUDST69IpHEnAVgdPPK1A1alvyCiT/k4/KeD2xOtd7UY9M6Dxtym9rj1/MS0wtlQetRtxpE9CM/Cw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-10T21:25:24.268152Z"},"content_sha256":"61ece9c1dae1315a4290edb6859da9f1da8c9b38847660d22344e1a328ec305d","schema_version":"1.0","event_id":"sha256:61ece9c1dae1315a4290edb6859da9f1da8c9b38847660d22344e1a328ec305d"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/MCH77ZRRTAGB7T2NRFMCZDRKYI/bundle.json","state_url":"https://pith.science/pith/MCH77ZRRTAGB7T2NRFMCZDRKYI/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/MCH77ZRRTAGB7T2NRFMCZDRKYI/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":"2026-08-10T21:25:24Z","links":{"resolver":"https://pith.science/pith/MCH77ZRRTAGB7T2NRFMCZDRKYI","bundle":"https://pith.science/pith/MCH77ZRRTAGB7T2NRFMCZDRKYI/bundle.json","state":"https://pith.science/pith/MCH77ZRRTAGB7T2NRFMCZDRKYI/state.json","well_known_bundle":"https://pith.science/.well-known/pith/MCH77ZRRTAGB7T2NRFMCZDRKYI/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:MCH77ZRRTAGB7T2NRFMCZDRKYI","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":"81fa72deaa4a58f1af98004eade4806a341ce6c2900b4266df1e3889c8d18c15","cross_cats_sorted":["cs.CV"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2024-10-15T23:16:28Z","title_canon_sha256":"3e7ff4e98f29fe6c87d41f3ccd4f03a100b960a3b1abb6364372a5501b18bf55"},"schema_version":"1.0","source":{"id":"2410.12109","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2410.12109","created_at":"2026-07-05T09:21:14Z"},{"alias_kind":"arxiv_version","alias_value":"2410.12109v1","created_at":"2026-07-05T09:21:14Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2410.12109","created_at":"2026-07-05T09:21:14Z"},{"alias_kind":"pith_short_12","alias_value":"MCH77ZRRTAGB","created_at":"2026-07-05T09:21:14Z"},{"alias_kind":"pith_short_16","alias_value":"MCH77ZRRTAGB7T2N","created_at":"2026-07-05T09:21:14Z"},{"alias_kind":"pith_short_8","alias_value":"MCH77ZRR","created_at":"2026-07-05T09:21:14Z"}],"graph_snapshots":[{"event_id":"sha256:61ece9c1dae1315a4290edb6859da9f1da8c9b38847660d22344e1a328ec305d","target":"graph","created_at":"2026-07-05T09:21:14Z","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/2410.12109/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Large Language Models (LLMs) have made significant strides in text generation and comprehension, with recent advancements extending into multimodal LLMs that integrate visual and audio inputs. However, these models continue to struggle with fine-grained, cross-modal temporal understanding, particularly when correlating events across audio and video streams. We address these challenges with two key contributions: a new dataset and model, called OCTAV and OMCAT respectively. OCTAV (Omni Context and Temporal Audio Video) is a novel dataset designed to capture event transitions across audio and vi","authors_text":"Andrew Tao, Arushi Goel, Bryan Catanzaro, Karan Sapra, Matthieu Le, Rafael Valle","cross_cats":["cs.CV"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2024-10-15T23:16:28Z","title":"OMCAT: Omni Context Aware Transformer"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2410.12109","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":"sha256:613db020cb4d1c1b2b155ab3be0fe22bc00afaa6d04bebceb39231c7b6b35eba","target":"record","created_at":"2026-07-05T09:21:14Z","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":"81fa72deaa4a58f1af98004eade4806a341ce6c2900b4266df1e3889c8d18c15","cross_cats_sorted":["cs.CV"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2024-10-15T23:16:28Z","title_canon_sha256":"3e7ff4e98f29fe6c87d41f3ccd4f03a100b960a3b1abb6364372a5501b18bf55"},"schema_version":"1.0","source":{"id":"2410.12109","kind":"arxiv","version":1}},"canonical_sha256":"608fffe631980c1fcf4d89582c8e2ac23fbd48be599c269ed2e372124fc89829","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"608fffe631980c1fcf4d89582c8e2ac23fbd48be599c269ed2e372124fc89829","first_computed_at":"2026-07-05T09:21:14.338380Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T09:21:14.338380Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"d0jYAFaIbUvmj/y2Qn9RvHXkV/ovZ/uaGqT3bB6EeiFJjyaG/hor/PdAeKwXAjdC9PZvOXeAOfUDYrZn4c3EBQ==","signature_status":"signed_v1","signed_at":"2026-07-05T09:21:14.338874Z","signed_message":"canonical_sha256_bytes"},"source_id":"2410.12109","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:613db020cb4d1c1b2b155ab3be0fe22bc00afaa6d04bebceb39231c7b6b35eba","sha256:61ece9c1dae1315a4290edb6859da9f1da8c9b38847660d22344e1a328ec305d"],"state_sha256":"ceeefd66fbc80f381cd7b5a40846cb9caac924d03dd2c1687d3de9d848988761"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"P81AmN5SL06qfApwmW+NJhdyQs5HrCXS8CNG0AHQNTDwQV1wjUle3LH7xSvNcA0KUJB+cGVWJTqp7C3Zs6JoCA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-10T21:25:24.272974Z","bundle_sha256":"70a42e57003135a3866a7d3eeb999e96f5468864a8e172d5bab06c5400ff5967"}}