{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2024:ST5A73XEB3JY6I4T5MT6PSPS72","short_pith_number":"pith:ST5A73XE","canonical_record":{"source":{"id":"2410.01744","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2024-10-02T16:55:01Z","cross_cats_sorted":["cs.CL"],"title_canon_sha256":"dbab08b21e31e34700dfa29924b0bdb3ef95f4a87d53aababf4fce036adedabf","abstract_canon_sha256":"f6c2eb87587f95a66a2ab7994d8002c2919f63f3e3e07ef672532c9ecacad7cd"},"schema_version":"1.0"},"canonical_sha256":"94fa0feee40ed38f2393eb27e7c9f2fea5932f60408ff724a9e804709c03579c","source":{"kind":"arxiv","id":"2410.01744","version":3},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2410.01744","created_at":"2026-07-05T11:17:22Z"},{"alias_kind":"arxiv_version","alias_value":"2410.01744v3","created_at":"2026-07-05T11:17:22Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2410.01744","created_at":"2026-07-05T11:17:22Z"},{"alias_kind":"pith_short_12","alias_value":"ST5A73XEB3JY","created_at":"2026-07-05T11:17:22Z"},{"alias_kind":"pith_short_16","alias_value":"ST5A73XEB3JY6I4T","created_at":"2026-07-05T11:17:22Z"},{"alias_kind":"pith_short_8","alias_value":"ST5A73XE","created_at":"2026-07-05T11:17:22Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2024:ST5A73XEB3JY6I4T5MT6PSPS72","target":"record","payload":{"canonical_record":{"source":{"id":"2410.01744","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2024-10-02T16:55:01Z","cross_cats_sorted":["cs.CL"],"title_canon_sha256":"dbab08b21e31e34700dfa29924b0bdb3ef95f4a87d53aababf4fce036adedabf","abstract_canon_sha256":"f6c2eb87587f95a66a2ab7994d8002c2919f63f3e3e07ef672532c9ecacad7cd"},"schema_version":"1.0"},"canonical_sha256":"94fa0feee40ed38f2393eb27e7c9f2fea5932f60408ff724a9e804709c03579c","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:17:22.465183Z","signature_b64":"dVCvC0dFMPtk0jKpwI8gXWjXvqXq8oABWfWIvnws/DeCp1CsE04RQMS/eqoGwpSbDu9C4ShXTDGuSVXdTvkCBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"94fa0feee40ed38f2393eb27e7c9f2fea5932f60408ff724a9e804709c03579c","last_reissued_at":"2026-07-05T11:17:22.464635Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:17:22.464635Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2410.01744","source_version":3,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T11:17:22Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"L43o9WlxZst562g/dh8P3HFjGpvEYFiqke+/IraoXQnUL/GWr3vT4rs7wb7GrpsN5vAWVp2WkMj+6cwhrQx8Cg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-03T12:15:29.397041Z"},"content_sha256":"43e2a96059726462a76b338804b16f9bd91bab3ea3af16054e6325ab4f1233f9","schema_version":"1.0","event_id":"sha256:43e2a96059726462a76b338804b16f9bd91bab3ea3af16054e6325ab4f1233f9"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2024:ST5A73XEB3JY6I4T5MT6PSPS72","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Leopard: A Vision Language Model For Text-Rich Multi-Image Tasks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CL"],"primary_cat":"cs.CV","authors_text":"Dong Yu, Hongming Zhang, Kaixin Ma, Meng Jiang, Mengzhao Jia, Siru Ouyang, Tianqing Fang, Wenhao Yu, Zhihan Zhang","submitted_at":"2024-10-02T16:55:01Z","abstract_excerpt":"Text-rich images, where text serves as the central visual element guiding the overall understanding, are prevalent in real-world applications, such as presentation slides, scanned documents, and webpage snapshots. Tasks involving multiple text-rich images are especially challenging, as they require not only understanding the content of individual images but reasoning about inter-relationships and logical flows across multiple visual inputs. Despite the importance of these scenarios, current multimodal large language models (MLLMs) struggle to handle such tasks due to two key challenges: (1) th"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2410.01744","kind":"arxiv","version":3},"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/2410.01744/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":"2026-07-05T11:17:22Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Rwo1jpF3HjySFgs2dX0eX/BeW+tuN7mR9mOUqbhPqS8Bj1AzkTI7tFqRs1qJcalzvCBDoRLiHbe6Z6GQefFYBg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-03T12:15:29.397671Z"},"content_sha256":"11325d50c5e0ea1f3a52ed4f6d3439caf86d3de396a77820f015b6f8bfa8d2ef","schema_version":"1.0","event_id":"sha256:11325d50c5e0ea1f3a52ed4f6d3439caf86d3de396a77820f015b6f8bfa8d2ef"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/ST5A73XEB3JY6I4T5MT6PSPS72/bundle.json","state_url":"https://pith.science/pith/ST5A73XEB3JY6I4T5MT6PSPS72/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/ST5A73XEB3JY6I4T5MT6PSPS72/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":"2026-08-03T12:15:29Z","links":{"resolver":"https://pith.science/pith/ST5A73XEB3JY6I4T5MT6PSPS72","bundle":"https://pith.science/pith/ST5A73XEB3JY6I4T5MT6PSPS72/bundle.json","state":"https://pith.science/pith/ST5A73XEB3JY6I4T5MT6PSPS72/state.json","well_known_bundle":"https://pith.science/.well-known/pith/ST5A73XEB3JY6I4T5MT6PSPS72/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:ST5A73XEB3JY6I4T5MT6PSPS72","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":"f6c2eb87587f95a66a2ab7994d8002c2919f63f3e3e07ef672532c9ecacad7cd","cross_cats_sorted":["cs.CL"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2024-10-02T16:55:01Z","title_canon_sha256":"dbab08b21e31e34700dfa29924b0bdb3ef95f4a87d53aababf4fce036adedabf"},"schema_version":"1.0","source":{"id":"2410.01744","kind":"arxiv","version":3}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2410.01744","created_at":"2026-07-05T11:17:22Z"},{"alias_kind":"arxiv_version","alias_value":"2410.01744v3","created_at":"2026-07-05T11:17:22Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2410.01744","created_at":"2026-07-05T11:17:22Z"},{"alias_kind":"pith_short_12","alias_value":"ST5A73XEB3JY","created_at":"2026-07-05T11:17:22Z"},{"alias_kind":"pith_short_16","alias_value":"ST5A73XEB3JY6I4T","created_at":"2026-07-05T11:17:22Z"},{"alias_kind":"pith_short_8","alias_value":"ST5A73XE","created_at":"2026-07-05T11:17:22Z"}],"graph_snapshots":[{"event_id":"sha256:11325d50c5e0ea1f3a52ed4f6d3439caf86d3de396a77820f015b6f8bfa8d2ef","target":"graph","created_at":"2026-07-05T11:17:22Z","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/2410.01744/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Text-rich images, where text serves as the central visual element guiding the overall understanding, are prevalent in real-world applications, such as presentation slides, scanned documents, and webpage snapshots. Tasks involving multiple text-rich images are especially challenging, as they require not only understanding the content of individual images but reasoning about inter-relationships and logical flows across multiple visual inputs. Despite the importance of these scenarios, current multimodal large language models (MLLMs) struggle to handle such tasks due to two key challenges: (1) th","authors_text":"Dong Yu, Hongming Zhang, Kaixin Ma, Meng Jiang, Mengzhao Jia, Siru Ouyang, Tianqing Fang, Wenhao Yu, Zhihan Zhang","cross_cats":["cs.CL"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2024-10-02T16:55:01Z","title":"Leopard: A Vision Language Model For Text-Rich Multi-Image Tasks"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2410.01744","kind":"arxiv","version":3},"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":"sha256:43e2a96059726462a76b338804b16f9bd91bab3ea3af16054e6325ab4f1233f9","target":"record","created_at":"2026-07-05T11:17:22Z","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":"f6c2eb87587f95a66a2ab7994d8002c2919f63f3e3e07ef672532c9ecacad7cd","cross_cats_sorted":["cs.CL"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2024-10-02T16:55:01Z","title_canon_sha256":"dbab08b21e31e34700dfa29924b0bdb3ef95f4a87d53aababf4fce036adedabf"},"schema_version":"1.0","source":{"id":"2410.01744","kind":"arxiv","version":3}},"canonical_sha256":"94fa0feee40ed38f2393eb27e7c9f2fea5932f60408ff724a9e804709c03579c","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"94fa0feee40ed38f2393eb27e7c9f2fea5932f60408ff724a9e804709c03579c","first_computed_at":"2026-07-05T11:17:22.464635Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T11:17:22.464635Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"dVCvC0dFMPtk0jKpwI8gXWjXvqXq8oABWfWIvnws/DeCp1CsE04RQMS/eqoGwpSbDu9C4ShXTDGuSVXdTvkCBQ==","signature_status":"signed_v1","signed_at":"2026-07-05T11:17:22.465183Z","signed_message":"canonical_sha256_bytes"},"source_id":"2410.01744","source_kind":"arxiv","source_version":3}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:43e2a96059726462a76b338804b16f9bd91bab3ea3af16054e6325ab4f1233f9","sha256:11325d50c5e0ea1f3a52ed4f6d3439caf86d3de396a77820f015b6f8bfa8d2ef"],"state_sha256":"d6c3daae6814b7590c6253cee5bd6df6033f881292bc078ca2ffb46ce9ad91ad"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"BN1O7I2a+FLp4fjsJ8FLUL/RzD74NMLQjE3zk1IACTRQySEcEkLNIajNndMvbxpCFtH0tDK9DjtCk+GXPdr7Aw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-03T12:15:29.405366Z","bundle_sha256":"f4cde3eab8652b5553c68e615880b296b94346788ebf87201d536f59f1620e72"}}