{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:26X42N3LHTMTPIR5HPDLPZNXZL","short_pith_number":"pith:26X42N3L","schema_version":"1.0","canonical_sha256":"d7afcd376b3cd937a23d3bc6b7e5b7caf5db1550b5ecaf79db779f739dd6363c","source":{"kind":"arxiv","id":"2411.09547","version":2},"attestation_state":"computed","paper":{"title":"Piecing It All Together: Verifying Multi-Hop Multimodal Claims","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CL","authors_text":"Aman Rangapur, Carl Yang, Haoran Wang, Haroon Gharwi, Kai Shu, Xiongxiao Xu, Yueqing Liang","submitted_at":"2024-11-14T16:01:33Z","abstract_excerpt":"Existing claim verification datasets often do not require systems to perform complex reasoning or effectively interpret multimodal evidence. To address this, we introduce a new task: multi-hop multimodal claim verification. This task challenges models to reason over multiple pieces of evidence from diverse sources, including text, images, and tables, and determine whether the combined multimodal evidence supports or refutes a given claim. To study this task, we construct MMCV, a large-scale dataset comprising 15k multi-hop claims paired with multimodal evidence, generated and refined using lar"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2411.09547","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2024-11-14T16:01:33Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"abef858e4918152b0a73d686a39661e653ed750e099334606eb703286cb3cb09","abstract_canon_sha256":"2146e4b7bed05d5311934e80892fc6cde5d9c987a31f1969588dedb20ec5c4ea"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:48:37.044698Z","signature_b64":"dbh+CPu4lHdHbKUkJT9Nf4Eu1Yf9dw6vgN9DYeAjbDxdZijaotFKZO7Jgc9tRn8sHdPMogSAp+/DeEjoiPkyBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"d7afcd376b3cd937a23d3bc6b7e5b7caf5db1550b5ecaf79db779f739dd6363c","last_reissued_at":"2026-07-05T09:48:37.044260Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:48:37.044260Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Piecing It All Together: Verifying Multi-Hop Multimodal Claims","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CL","authors_text":"Aman Rangapur, Carl Yang, Haoran Wang, Haroon Gharwi, Kai Shu, Xiongxiao Xu, Yueqing Liang","submitted_at":"2024-11-14T16:01:33Z","abstract_excerpt":"Existing claim verification datasets often do not require systems to perform complex reasoning or effectively interpret multimodal evidence. To address this, we introduce a new task: multi-hop multimodal claim verification. This task challenges models to reason over multiple pieces of evidence from diverse sources, including text, images, and tables, and determine whether the combined multimodal evidence supports or refutes a given claim. To study this task, we construct MMCV, a large-scale dataset comprising 15k multi-hop claims paired with multimodal evidence, generated and refined using lar"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2411.09547","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/2411.09547/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"},"aliases":[{"alias_kind":"arxiv","alias_value":"2411.09547","created_at":"2026-07-05T09:48:37.044325+00:00"},{"alias_kind":"arxiv_version","alias_value":"2411.09547v2","created_at":"2026-07-05T09:48:37.044325+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2411.09547","created_at":"2026-07-05T09:48:37.044325+00:00"},{"alias_kind":"pith_short_12","alias_value":"26X42N3LHTMT","created_at":"2026-07-05T09:48:37.044325+00:00"},{"alias_kind":"pith_short_16","alias_value":"26X42N3LHTMTPIR5","created_at":"2026-07-05T09:48:37.044325+00:00"},{"alias_kind":"pith_short_8","alias_value":"26X42N3L","created_at":"2026-07-05T09:48:37.044325+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2412.12567","citing_title":"FCMR: Robust Evaluation of Financial Cross-Modal Multi-Hop Reasoning","ref_index":34,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/26X42N3LHTMTPIR5HPDLPZNXZL","json":"https://pith.science/pith/26X42N3LHTMTPIR5HPDLPZNXZL.json","graph_json":"https://pith.science/api/pith-number/26X42N3LHTMTPIR5HPDLPZNXZL/graph.json","events_json":"https://pith.science/api/pith-number/26X42N3LHTMTPIR5HPDLPZNXZL/events.json","paper":"https://pith.science/paper/26X42N3L"},"agent_actions":{"view_html":"https://pith.science/pith/26X42N3LHTMTPIR5HPDLPZNXZL","download_json":"https://pith.science/pith/26X42N3LHTMTPIR5HPDLPZNXZL.json","view_paper":"https://pith.science/paper/26X42N3L","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2411.09547&json=true","fetch_graph":"https://pith.science/api/pith-number/26X42N3LHTMTPIR5HPDLPZNXZL/graph.json","fetch_events":"https://pith.science/api/pith-number/26X42N3LHTMTPIR5HPDLPZNXZL/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/26X42N3LHTMTPIR5HPDLPZNXZL/action/timestamp_anchor","attest_storage":"https://pith.science/pith/26X42N3LHTMTPIR5HPDLPZNXZL/action/storage_attestation","attest_author":"https://pith.science/pith/26X42N3LHTMTPIR5HPDLPZNXZL/action/author_attestation","sign_citation":"https://pith.science/pith/26X42N3LHTMTPIR5HPDLPZNXZL/action/citation_signature","submit_replication":"https://pith.science/pith/26X42N3LHTMTPIR5HPDLPZNXZL/action/replication_record"}},"created_at":"2026-07-05T09:48:37.044325+00:00","updated_at":"2026-07-05T09:48:37.044325+00:00"}