{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2023:ZQ5XGJHOAIXKIYSCJ7XVLU6S22","short_pith_number":"pith:ZQ5XGJHO","canonical_record":{"source":{"id":"2308.01430","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2023-07-31T07:44:15Z","cross_cats_sorted":[],"title_canon_sha256":"feb151402b64dcd3cd564174a1d7d171884863a4886f9de32ce67bae4328641e","abstract_canon_sha256":"37abb3a24c954529aa28b3eda6666dcab404f1f7c9b9fe276084c80aa1770d74"},"schema_version":"1.0"},"canonical_sha256":"cc3b7324ee022ea462424fef55d3d2d6ba05a5b63c9c37781fbb336368051310","source":{"kind":"arxiv","id":"2308.01430","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2308.01430","created_at":"2026-07-05T06:37:18Z"},{"alias_kind":"arxiv_version","alias_value":"2308.01430v1","created_at":"2026-07-05T06:37:18Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2308.01430","created_at":"2026-07-05T06:37:18Z"},{"alias_kind":"pith_short_12","alias_value":"ZQ5XGJHOAIXK","created_at":"2026-07-05T06:37:18Z"},{"alias_kind":"pith_short_16","alias_value":"ZQ5XGJHOAIXKIYSC","created_at":"2026-07-05T06:37:18Z"},{"alias_kind":"pith_short_8","alias_value":"ZQ5XGJHO","created_at":"2026-07-05T06:37:18Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2023:ZQ5XGJHOAIXKIYSCJ7XVLU6S22","target":"record","payload":{"canonical_record":{"source":{"id":"2308.01430","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2023-07-31T07:44:15Z","cross_cats_sorted":[],"title_canon_sha256":"feb151402b64dcd3cd564174a1d7d171884863a4886f9de32ce67bae4328641e","abstract_canon_sha256":"37abb3a24c954529aa28b3eda6666dcab404f1f7c9b9fe276084c80aa1770d74"},"schema_version":"1.0"},"canonical_sha256":"cc3b7324ee022ea462424fef55d3d2d6ba05a5b63c9c37781fbb336368051310","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T06:37:18.320282Z","signature_b64":"8FfEEUhvgF6CPR9Z8QHHQR48CtafsjR3odGH73obs7hOMdCeHU+zRPht1RAXdr4Sw/KxJJ5TSC2CCWD7ug45Aw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"cc3b7324ee022ea462424fef55d3d2d6ba05a5b63c9c37781fbb336368051310","last_reissued_at":"2026-07-05T06:37:18.319804Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T06:37:18.319804Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2308.01430","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":"2026-07-05T06:37:18Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"D6sp6gOw5Cw+0BLkkPl7Umf9FyLJtm8I/j5/DpjNHRPUitP36wdOFYXN5JRCAZetkKoEX4vqdMyAsmHHVz8NBQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-07T22:41:28.666720Z"},"content_sha256":"4185de12cf1ff3d91a4ea6bd1a03e087dbea596dd65f21f676cea2b07774302f","schema_version":"1.0","event_id":"sha256:4185de12cf1ff3d91a4ea6bd1a03e087dbea596dd65f21f676cea2b07774302f"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2023:ZQ5XGJHOAIXKIYSCJ7XVLU6S22","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"FinVis-GPT: A Multimodal Large Language Model for Financial Chart Analysis","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Jaehyeon Soon, Junda Wu, Xiaofeng Zhang, Yuhang Li, Ziao Wang","submitted_at":"2023-07-31T07:44:15Z","abstract_excerpt":"In this paper, we propose FinVis-GPT, a novel multimodal large language model (LLM) specifically designed for financial chart analysis. By leveraging the power of LLMs and incorporating instruction tuning and multimodal capabilities, FinVis-GPT is capable of interpreting financial charts and providing valuable analysis. To train FinVis-GPT, a financial task oriented dataset was generated for pre-training alignment and instruction tuning, comprising various types of financial charts and their corresponding descriptions. We evaluate the model performance via several case studies due to the time "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2308.01430","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/2308.01430/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":"2026-07-05T06:37:18Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"jF/LVuzSyfpdvCOMF/Sn3fdVA7V/1nrTW3eu6eZOF6b6JcYHmvrngmYPflb5Cdra6rdu1L5pVVexKlzktSsPCQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-07T22:41:28.667635Z"},"content_sha256":"860cd1e9f0e58b529b7ef155649bbbede88990ef041446f456dbc059d09949a8","schema_version":"1.0","event_id":"sha256:860cd1e9f0e58b529b7ef155649bbbede88990ef041446f456dbc059d09949a8"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/ZQ5XGJHOAIXKIYSCJ7XVLU6S22/bundle.json","state_url":"https://pith.science/pith/ZQ5XGJHOAIXKIYSCJ7XVLU6S22/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/ZQ5XGJHOAIXKIYSCJ7XVLU6S22/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":"2026-08-07T22:41:28Z","links":{"resolver":"https://pith.science/pith/ZQ5XGJHOAIXKIYSCJ7XVLU6S22","bundle":"https://pith.science/pith/ZQ5XGJHOAIXKIYSCJ7XVLU6S22/bundle.json","state":"https://pith.science/pith/ZQ5XGJHOAIXKIYSCJ7XVLU6S22/state.json","well_known_bundle":"https://pith.science/.well-known/pith/ZQ5XGJHOAIXKIYSCJ7XVLU6S22/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2023:ZQ5XGJHOAIXKIYSCJ7XVLU6S22","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":"37abb3a24c954529aa28b3eda6666dcab404f1f7c9b9fe276084c80aa1770d74","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2023-07-31T07:44:15Z","title_canon_sha256":"feb151402b64dcd3cd564174a1d7d171884863a4886f9de32ce67bae4328641e"},"schema_version":"1.0","source":{"id":"2308.01430","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2308.01430","created_at":"2026-07-05T06:37:18Z"},{"alias_kind":"arxiv_version","alias_value":"2308.01430v1","created_at":"2026-07-05T06:37:18Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2308.01430","created_at":"2026-07-05T06:37:18Z"},{"alias_kind":"pith_short_12","alias_value":"ZQ5XGJHOAIXK","created_at":"2026-07-05T06:37:18Z"},{"alias_kind":"pith_short_16","alias_value":"ZQ5XGJHOAIXKIYSC","created_at":"2026-07-05T06:37:18Z"},{"alias_kind":"pith_short_8","alias_value":"ZQ5XGJHO","created_at":"2026-07-05T06:37:18Z"}],"graph_snapshots":[{"event_id":"sha256:860cd1e9f0e58b529b7ef155649bbbede88990ef041446f456dbc059d09949a8","target":"graph","created_at":"2026-07-05T06:37:18Z","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/2308.01430/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"In this paper, we propose FinVis-GPT, a novel multimodal large language model (LLM) specifically designed for financial chart analysis. By leveraging the power of LLMs and incorporating instruction tuning and multimodal capabilities, FinVis-GPT is capable of interpreting financial charts and providing valuable analysis. To train FinVis-GPT, a financial task oriented dataset was generated for pre-training alignment and instruction tuning, comprising various types of financial charts and their corresponding descriptions. We evaluate the model performance via several case studies due to the time ","authors_text":"Jaehyeon Soon, Junda Wu, Xiaofeng Zhang, Yuhang Li, Ziao Wang","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2023-07-31T07:44:15Z","title":"FinVis-GPT: A Multimodal Large Language Model for Financial Chart Analysis"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2308.01430","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":"sha256:4185de12cf1ff3d91a4ea6bd1a03e087dbea596dd65f21f676cea2b07774302f","target":"record","created_at":"2026-07-05T06:37:18Z","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":"37abb3a24c954529aa28b3eda6666dcab404f1f7c9b9fe276084c80aa1770d74","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2023-07-31T07:44:15Z","title_canon_sha256":"feb151402b64dcd3cd564174a1d7d171884863a4886f9de32ce67bae4328641e"},"schema_version":"1.0","source":{"id":"2308.01430","kind":"arxiv","version":1}},"canonical_sha256":"cc3b7324ee022ea462424fef55d3d2d6ba05a5b63c9c37781fbb336368051310","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"cc3b7324ee022ea462424fef55d3d2d6ba05a5b63c9c37781fbb336368051310","first_computed_at":"2026-07-05T06:37:18.319804Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T06:37:18.319804Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"8FfEEUhvgF6CPR9Z8QHHQR48CtafsjR3odGH73obs7hOMdCeHU+zRPht1RAXdr4Sw/KxJJ5TSC2CCWD7ug45Aw==","signature_status":"signed_v1","signed_at":"2026-07-05T06:37:18.320282Z","signed_message":"canonical_sha256_bytes"},"source_id":"2308.01430","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:4185de12cf1ff3d91a4ea6bd1a03e087dbea596dd65f21f676cea2b07774302f","sha256:860cd1e9f0e58b529b7ef155649bbbede88990ef041446f456dbc059d09949a8"],"state_sha256":"d0b14787522d874e7ff8d08bdd1f3cd9e564828680b870632aeb6fe2b8e8646e"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"hyn34wjrr4TDfbwQL6CZIw3dslOhBZcVkqGaMjSG6DBP9wCk14xAfvedxNExYiKZF3sXqiXOCAaWr989F6ABDw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-07T22:41:28.674803Z","bundle_sha256":"7c763b455cdcedc892292a80fcec17e745c9fe842ef69a879da8a7eda85e7a04"}}