{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2025:IGQSPBRSXE53YLTZLB37XITHGX","short_pith_number":"pith:IGQSPBRS","canonical_record":{"source":{"id":"2502.15079","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CV","submitted_at":"2025-02-20T22:43:22Z","cross_cats_sorted":["cs.AI","cs.CL"],"title_canon_sha256":"4088ac60bc608c1d7937ca77a401e1536ebbdf0a756c1202c3d7c1d2e1834996","abstract_canon_sha256":"3c84373a9121be5f80c1cd6d6ee5d7a42a321edf3ecc787a1fff943d8f0d455e"},"schema_version":"1.0"},"canonical_sha256":"41a1278632b93bbc2e795877fba26735d35f14e21ede535f4ef18ece2f227bfe","source":{"kind":"arxiv","id":"2502.15079","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2502.15079","created_at":"2026-07-05T10:17:55Z"},{"alias_kind":"arxiv_version","alias_value":"2502.15079v1","created_at":"2026-07-05T10:17:55Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2502.15079","created_at":"2026-07-05T10:17:55Z"},{"alias_kind":"pith_short_12","alias_value":"IGQSPBRSXE53","created_at":"2026-07-05T10:17:55Z"},{"alias_kind":"pith_short_16","alias_value":"IGQSPBRSXE53YLTZ","created_at":"2026-07-05T10:17:55Z"},{"alias_kind":"pith_short_8","alias_value":"IGQSPBRS","created_at":"2026-07-05T10:17:55Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2025:IGQSPBRSXE53YLTZLB37XITHGX","target":"record","payload":{"canonical_record":{"source":{"id":"2502.15079","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CV","submitted_at":"2025-02-20T22:43:22Z","cross_cats_sorted":["cs.AI","cs.CL"],"title_canon_sha256":"4088ac60bc608c1d7937ca77a401e1536ebbdf0a756c1202c3d7c1d2e1834996","abstract_canon_sha256":"3c84373a9121be5f80c1cd6d6ee5d7a42a321edf3ecc787a1fff943d8f0d455e"},"schema_version":"1.0"},"canonical_sha256":"41a1278632b93bbc2e795877fba26735d35f14e21ede535f4ef18ece2f227bfe","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:17:55.781632Z","signature_b64":"aLeJxx7P8yowKESBThChmvwYf4W/0EFD2ifIp364IIV3jSSAAGZL6APkR8IFehBgu7EM8+ZkdNbgLvMYNtozAQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"41a1278632b93bbc2e795877fba26735d35f14e21ede535f4ef18ece2f227bfe","last_reissued_at":"2026-07-05T10:17:55.781036Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:17:55.781036Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2502.15079","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":"2026-07-05T10:17:55Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Oo3eWipHfQG4lQ5uAnGUE1VWoUXRSIMFVJYm/dAjnHrfJFVDrcEz+3vnA6W/S4lBDOvgHe6Jn3EnfiOT9zHICg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-07T04:50:49.208946Z"},"content_sha256":"27a711729ee1b523142a0788d53c50c74808687220e3fa46e05855eacdeb2b43","schema_version":"1.0","event_id":"sha256:27a711729ee1b523142a0788d53c50c74808687220e3fa46e05855eacdeb2b43"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2025:IGQSPBRSXE53YLTZLB37XITHGX","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Can Hallucination Correction Improve Video-Language Alignment?","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":["cs.AI","cs.CL"],"primary_cat":"cs.CV","authors_text":"Hal Daum\\'e III, Kwonjoon Lee, Lingjun Zhao, Mingyang Xie, Paola Cascante-Bonilla","submitted_at":"2025-02-20T22:43:22Z","abstract_excerpt":"Large Vision-Language Models often generate hallucinated content that is not grounded in its visual inputs. While prior work focuses on mitigating hallucinations, we instead explore leveraging hallucination correction as a training objective to improve video-language alignment. We introduce HACA, a self-training framework learning to correct hallucinations in descriptions that do not align with the video content. By identifying and correcting inconsistencies, HACA enhances the model's ability to align video and textual representations for spatio-temporal reasoning. Our experimental results sho"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2502.15079","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/2502.15079/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":"2026-07-05T10:17:55Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"dulmRy4MRw75+20Z175Wa2DAuNmV4MFPqyLChuHDKwYqvG1MVlVFp7Qsyq/K0eUIEVLjdU7PKitLhvvSDe4xCQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-07T04:50:49.209358Z"},"content_sha256":"df2219abe7bcfd9be621f74cc53631403f93a1932db800420345607737f36e37","schema_version":"1.0","event_id":"sha256:df2219abe7bcfd9be621f74cc53631403f93a1932db800420345607737f36e37"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/IGQSPBRSXE53YLTZLB37XITHGX/bundle.json","state_url":"https://pith.science/pith/IGQSPBRSXE53YLTZLB37XITHGX/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/IGQSPBRSXE53YLTZLB37XITHGX/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":"2026-07-07T04:50:49Z","links":{"resolver":"https://pith.science/pith/IGQSPBRSXE53YLTZLB37XITHGX","bundle":"https://pith.science/pith/IGQSPBRSXE53YLTZLB37XITHGX/bundle.json","state":"https://pith.science/pith/IGQSPBRSXE53YLTZLB37XITHGX/state.json","well_known_bundle":"https://pith.science/.well-known/pith/IGQSPBRSXE53YLTZLB37XITHGX/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:IGQSPBRSXE53YLTZLB37XITHGX","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":"3c84373a9121be5f80c1cd6d6ee5d7a42a321edf3ecc787a1fff943d8f0d455e","cross_cats_sorted":["cs.AI","cs.CL"],"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CV","submitted_at":"2025-02-20T22:43:22Z","title_canon_sha256":"4088ac60bc608c1d7937ca77a401e1536ebbdf0a756c1202c3d7c1d2e1834996"},"schema_version":"1.0","source":{"id":"2502.15079","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2502.15079","created_at":"2026-07-05T10:17:55Z"},{"alias_kind":"arxiv_version","alias_value":"2502.15079v1","created_at":"2026-07-05T10:17:55Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2502.15079","created_at":"2026-07-05T10:17:55Z"},{"alias_kind":"pith_short_12","alias_value":"IGQSPBRSXE53","created_at":"2026-07-05T10:17:55Z"},{"alias_kind":"pith_short_16","alias_value":"IGQSPBRSXE53YLTZ","created_at":"2026-07-05T10:17:55Z"},{"alias_kind":"pith_short_8","alias_value":"IGQSPBRS","created_at":"2026-07-05T10:17:55Z"}],"graph_snapshots":[{"event_id":"sha256:df2219abe7bcfd9be621f74cc53631403f93a1932db800420345607737f36e37","target":"graph","created_at":"2026-07-05T10:17:55Z","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/2502.15079/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Large Vision-Language Models often generate hallucinated content that is not grounded in its visual inputs. While prior work focuses on mitigating hallucinations, we instead explore leveraging hallucination correction as a training objective to improve video-language alignment. We introduce HACA, a self-training framework learning to correct hallucinations in descriptions that do not align with the video content. By identifying and correcting inconsistencies, HACA enhances the model's ability to align video and textual representations for spatio-temporal reasoning. Our experimental results sho","authors_text":"Hal Daum\\'e III, Kwonjoon Lee, Lingjun Zhao, Mingyang Xie, Paola Cascante-Bonilla","cross_cats":["cs.AI","cs.CL"],"headline":"","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CV","submitted_at":"2025-02-20T22:43:22Z","title":"Can Hallucination Correction Improve Video-Language Alignment?"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2502.15079","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":"sha256:27a711729ee1b523142a0788d53c50c74808687220e3fa46e05855eacdeb2b43","target":"record","created_at":"2026-07-05T10:17:55Z","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":"3c84373a9121be5f80c1cd6d6ee5d7a42a321edf3ecc787a1fff943d8f0d455e","cross_cats_sorted":["cs.AI","cs.CL"],"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CV","submitted_at":"2025-02-20T22:43:22Z","title_canon_sha256":"4088ac60bc608c1d7937ca77a401e1536ebbdf0a756c1202c3d7c1d2e1834996"},"schema_version":"1.0","source":{"id":"2502.15079","kind":"arxiv","version":1}},"canonical_sha256":"41a1278632b93bbc2e795877fba26735d35f14e21ede535f4ef18ece2f227bfe","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"41a1278632b93bbc2e795877fba26735d35f14e21ede535f4ef18ece2f227bfe","first_computed_at":"2026-07-05T10:17:55.781036Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T10:17:55.781036Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"aLeJxx7P8yowKESBThChmvwYf4W/0EFD2ifIp364IIV3jSSAAGZL6APkR8IFehBgu7EM8+ZkdNbgLvMYNtozAQ==","signature_status":"signed_v1","signed_at":"2026-07-05T10:17:55.781632Z","signed_message":"canonical_sha256_bytes"},"source_id":"2502.15079","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:27a711729ee1b523142a0788d53c50c74808687220e3fa46e05855eacdeb2b43","sha256:df2219abe7bcfd9be621f74cc53631403f93a1932db800420345607737f36e37"],"state_sha256":"ca2c595a7902fdb89bb4784a4111b64c0ae5b22455e9e543139c3c02e0c02a5c"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"bFq8J/FqvN9p6Y1o7OKENasOOH4QOa6gAm7tNmCRZKHOREzD6BvpiTBlOMi/I3MCSHCVoMwQjSK8sqfH/A7sBg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-07T04:50:49.211844Z","bundle_sha256":"965f02645e2fb342d11a32a6b5321349bb1e0cd94f851eda280942885c7bb39f"}}