{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2022:B4J46ICEHVFIUYWODIG6K7K33Q","short_pith_number":"pith:B4J46ICE","canonical_record":{"source":{"id":"2203.10244","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2022-03-19T05:00:30Z","cross_cats_sorted":[],"title_canon_sha256":"bdb06b5c8d45046f3cf618cd6e4e1a9e66d85c7435ca10e8fa61518a1dd724a8","abstract_canon_sha256":"d6930bbc78c4cd56a5b891c4973344385c2b22470079ef6ec7a328bed618717c"},"schema_version":"1.0"},"canonical_sha256":"0f13cf20443d4a8a62ce1a0de57d5bdc08698f7e170663ec527c51d04b0cfd5a","source":{"kind":"arxiv","id":"2203.10244","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2203.10244","created_at":"2026-05-17T23:38:50Z"},{"alias_kind":"arxiv_version","alias_value":"2203.10244v1","created_at":"2026-05-17T23:38:50Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2203.10244","created_at":"2026-05-17T23:38:50Z"},{"alias_kind":"pith_short_12","alias_value":"B4J46ICEHVFI","created_at":"2026-05-18T12:33:33Z"},{"alias_kind":"pith_short_16","alias_value":"B4J46ICEHVFIUYWO","created_at":"2026-05-18T12:33:33Z"},{"alias_kind":"pith_short_8","alias_value":"B4J46ICE","created_at":"2026-05-18T12:33:33Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2022:B4J46ICEHVFIUYWODIG6K7K33Q","target":"record","payload":{"canonical_record":{"source":{"id":"2203.10244","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2022-03-19T05:00:30Z","cross_cats_sorted":[],"title_canon_sha256":"bdb06b5c8d45046f3cf618cd6e4e1a9e66d85c7435ca10e8fa61518a1dd724a8","abstract_canon_sha256":"d6930bbc78c4cd56a5b891c4973344385c2b22470079ef6ec7a328bed618717c"},"schema_version":"1.0"},"canonical_sha256":"0f13cf20443d4a8a62ce1a0de57d5bdc08698f7e170663ec527c51d04b0cfd5a","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:38:50.148488Z","signature_b64":"Zwx0EDvFNzpFUKcsAZcsfltqPkjFIgzmpwavcEi3NZmE42mXErCVM/nM8Rdgt6mizwp1wAVjjYqY4CezCwXEDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"0f13cf20443d4a8a62ce1a0de57d5bdc08698f7e170663ec527c51d04b0cfd5a","last_reissued_at":"2026-05-17T23:38:50.147919Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:38:50.147919Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2203.10244","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":"2026-05-17T23:38:50Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"bF9NRv2elfB8WIX97HS8khMpUwNKskxEz96tGvGl38Ry7K6MI3QwpFKXb3oOsUVTYSDaQxMyRE3B8G8ncguoCQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-10T10:36:24.963970Z"},"content_sha256":"f5f65765bd87c7a8b4ce1130fdfaf20ec4a904ddadaa38ba8d663d6220cc8cbc","schema_version":"1.0","event_id":"sha256:f5f65765bd87c7a8b4ce1130fdfaf20ec4a904ddadaa38ba8d663d6220cc8cbc"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2022:B4J46ICEHVFIUYWODIG6K7K33Q","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"ChartQA: A Benchmark for Question Answering about Charts with Visual and Logical Reasoning","license":"http://creativecommons.org/licenses/by/4.0/","headline":"ChartQA introduces a benchmark of 32.7K questions requiring visual and logical reasoning over charts, plus transformer models that fuse image features with data tables.","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Ahmed Masry, Do Xuan Long, Enamul Hoque, Jia Qing Tan, Shafiq Joty","submitted_at":"2022-03-19T05:00:30Z","abstract_excerpt":"Charts are very popular for analyzing data. When exploring charts, people often ask a variety of complex reasoning questions that involve several logical and arithmetic operations. They also commonly refer to visual features of a chart in their questions. However, most existing datasets do not focus on such complex reasoning questions as their questions are template-based and answers come from a fixed-vocabulary. In this work, we present a large-scale benchmark covering 9.6K human-written questions as well as 23.1K questions generated from human-written chart summaries. To address the unique c"},"claims":{"count":4,"items":[{"kind":"strongest_claim","text":"we present two transformer-based models that combine visual features and the data table of the chart in a unified way to answer questions. While our models achieve the state-of-the-art results on the previous datasets as well as on our benchmark","source":"verdict.strongest_claim","status":"machine_extracted","claim_id":"C1","attestation":"unclaimed"},{"kind":"weakest_assumption","text":"That the collected human-written questions and summary-generated questions sufficiently capture the full range of complex visual and logical reasoning people perform on real-world charts.","source":"verdict.weakest_assumption","status":"machine_extracted","claim_id":"C2","attestation":"unclaimed"},{"kind":"one_line_summary","text":"ChartQA is a new benchmark for complex chart question answering involving visual and logical reasoning, with transformer models that fuse visual features and data tables to reach state-of-the-art performance.","source":"verdict.one_line_summary","status":"machine_extracted","claim_id":"C3","attestation":"unclaimed"},{"kind":"headline","text":"ChartQA introduces a benchmark of 32.7K questions requiring visual and logical reasoning over charts, plus transformer models that fuse image features with data tables.","source":"verdict.pith_extraction.headline","status":"machine_extracted","claim_id":"C4","attestation":"unclaimed"}],"snapshot_sha256":"6f34403a164a9e2c50dd95d6c78c0feead725f4957fc761317f2660e8701b124"},"source":{"id":"2203.10244","kind":"arxiv","version":1},"verdict":{"id":"27bde1d3-b446-4776-bbc0-ebf726533c3a","model_set":{"reader":"grok-4.3"},"created_at":"2026-05-15T21:10:17.482277Z","strongest_claim":"we present two transformer-based models that combine visual features and the data table of the chart in a unified way to answer questions. While our models achieve the state-of-the-art results on the previous datasets as well as on our benchmark","one_line_summary":"ChartQA is a new benchmark for complex chart question answering involving visual and logical reasoning, with transformer models that fuse visual features and data tables to reach state-of-the-art performance.","pipeline_version":"pith-pipeline@v0.9.0","weakest_assumption":"That the collected human-written questions and summary-generated questions sufficiently capture the full range of complex visual and logical reasoning people perform on real-world charts.","pith_extraction_headline":"ChartQA introduces a benchmark of 32.7K questions requiring visual and logical reasoning over charts, plus transformer models that fuse image features with data tables."},"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":"27bde1d3-b446-4776-bbc0-ebf726533c3a"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-17T23:38:50Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"+8KzZhhS2sjPUmnKHhXO/UOi3lvFpfGUuyCaLAe75vpXVs9poL1NSOsUG26ubCFan0ZMYCL3mYMOAr8T1yBIDA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-10T10:36:24.964488Z"},"content_sha256":"5e4425533f8993b6ba8c37424cba0cef0cfa93849a7cb73c6445e7b480633adf","schema_version":"1.0","event_id":"sha256:5e4425533f8993b6ba8c37424cba0cef0cfa93849a7cb73c6445e7b480633adf"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/B4J46ICEHVFIUYWODIG6K7K33Q/bundle.json","state_url":"https://pith.science/pith/B4J46ICEHVFIUYWODIG6K7K33Q/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/B4J46ICEHVFIUYWODIG6K7K33Q/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":"2026-06-10T10:36:24Z","links":{"resolver":"https://pith.science/pith/B4J46ICEHVFIUYWODIG6K7K33Q","bundle":"https://pith.science/pith/B4J46ICEHVFIUYWODIG6K7K33Q/bundle.json","state":"https://pith.science/pith/B4J46ICEHVFIUYWODIG6K7K33Q/state.json","well_known_bundle":"https://pith.science/.well-known/pith/B4J46ICEHVFIUYWODIG6K7K33Q/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2022:B4J46ICEHVFIUYWODIG6K7K33Q","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":"d6930bbc78c4cd56a5b891c4973344385c2b22470079ef6ec7a328bed618717c","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2022-03-19T05:00:30Z","title_canon_sha256":"bdb06b5c8d45046f3cf618cd6e4e1a9e66d85c7435ca10e8fa61518a1dd724a8"},"schema_version":"1.0","source":{"id":"2203.10244","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2203.10244","created_at":"2026-05-17T23:38:50Z"},{"alias_kind":"arxiv_version","alias_value":"2203.10244v1","created_at":"2026-05-17T23:38:50Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2203.10244","created_at":"2026-05-17T23:38:50Z"},{"alias_kind":"pith_short_12","alias_value":"B4J46ICEHVFI","created_at":"2026-05-18T12:33:33Z"},{"alias_kind":"pith_short_16","alias_value":"B4J46ICEHVFIUYWO","created_at":"2026-05-18T12:33:33Z"},{"alias_kind":"pith_short_8","alias_value":"B4J46ICE","created_at":"2026-05-18T12:33:33Z"}],"graph_snapshots":[{"event_id":"sha256:5e4425533f8993b6ba8c37424cba0cef0cfa93849a7cb73c6445e7b480633adf","target":"graph","created_at":"2026-05-17T23:38:50Z","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":4,"items":[{"attestation":"unclaimed","claim_id":"C1","kind":"strongest_claim","source":"verdict.strongest_claim","status":"machine_extracted","text":"we present two transformer-based models that combine visual features and the data table of the chart in a unified way to answer questions. While our models achieve the state-of-the-art results on the previous datasets as well as on our benchmark"},{"attestation":"unclaimed","claim_id":"C2","kind":"weakest_assumption","source":"verdict.weakest_assumption","status":"machine_extracted","text":"That the collected human-written questions and summary-generated questions sufficiently capture the full range of complex visual and logical reasoning people perform on real-world charts."},{"attestation":"unclaimed","claim_id":"C3","kind":"one_line_summary","source":"verdict.one_line_summary","status":"machine_extracted","text":"ChartQA is a new benchmark for complex chart question answering involving visual and logical reasoning, with transformer models that fuse visual features and data tables to reach state-of-the-art performance."},{"attestation":"unclaimed","claim_id":"C4","kind":"headline","source":"verdict.pith_extraction.headline","status":"machine_extracted","text":"ChartQA introduces a benchmark of 32.7K questions requiring visual and logical reasoning over charts, plus transformer models that fuse image features with data tables."}],"snapshot_sha256":"6f34403a164a9e2c50dd95d6c78c0feead725f4957fc761317f2660e8701b124"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"paper":{"abstract_excerpt":"Charts are very popular for analyzing data. When exploring charts, people often ask a variety of complex reasoning questions that involve several logical and arithmetic operations. They also commonly refer to visual features of a chart in their questions. However, most existing datasets do not focus on such complex reasoning questions as their questions are template-based and answers come from a fixed-vocabulary. In this work, we present a large-scale benchmark covering 9.6K human-written questions as well as 23.1K questions generated from human-written chart summaries. To address the unique c","authors_text":"Ahmed Masry, Do Xuan Long, Enamul Hoque, Jia Qing Tan, Shafiq Joty","cross_cats":[],"headline":"ChartQA introduces a benchmark of 32.7K questions requiring visual and logical reasoning over charts, plus transformer models that fuse image features with data tables.","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2022-03-19T05:00:30Z","title":"ChartQA: A Benchmark for Question Answering about Charts with Visual and Logical Reasoning"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2203.10244","kind":"arxiv","version":1},"verdict":{"created_at":"2026-05-15T21:10:17.482277Z","id":"27bde1d3-b446-4776-bbc0-ebf726533c3a","model_set":{"reader":"grok-4.3"},"one_line_summary":"ChartQA is a new benchmark for complex chart question answering involving visual and logical reasoning, with transformer models that fuse visual features and data tables to reach state-of-the-art performance.","pipeline_version":"pith-pipeline@v0.9.0","pith_extraction_headline":"ChartQA introduces a benchmark of 32.7K questions requiring visual and logical reasoning over charts, plus transformer models that fuse image features with data tables.","strongest_claim":"we present two transformer-based models that combine visual features and the data table of the chart in a unified way to answer questions. While our models achieve the state-of-the-art results on the previous datasets as well as on our benchmark","weakest_assumption":"That the collected human-written questions and summary-generated questions sufficiently capture the full range of complex visual and logical reasoning people perform on real-world charts."}},"verdict_id":"27bde1d3-b446-4776-bbc0-ebf726533c3a"}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:f5f65765bd87c7a8b4ce1130fdfaf20ec4a904ddadaa38ba8d663d6220cc8cbc","target":"record","created_at":"2026-05-17T23:38:50Z","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":"d6930bbc78c4cd56a5b891c4973344385c2b22470079ef6ec7a328bed618717c","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2022-03-19T05:00:30Z","title_canon_sha256":"bdb06b5c8d45046f3cf618cd6e4e1a9e66d85c7435ca10e8fa61518a1dd724a8"},"schema_version":"1.0","source":{"id":"2203.10244","kind":"arxiv","version":1}},"canonical_sha256":"0f13cf20443d4a8a62ce1a0de57d5bdc08698f7e170663ec527c51d04b0cfd5a","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"0f13cf20443d4a8a62ce1a0de57d5bdc08698f7e170663ec527c51d04b0cfd5a","first_computed_at":"2026-05-17T23:38:50.147919Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-17T23:38:50.147919Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"Zwx0EDvFNzpFUKcsAZcsfltqPkjFIgzmpwavcEi3NZmE42mXErCVM/nM8Rdgt6mizwp1wAVjjYqY4CezCwXEDw==","signature_status":"signed_v1","signed_at":"2026-05-17T23:38:50.148488Z","signed_message":"canonical_sha256_bytes"},"source_id":"2203.10244","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:f5f65765bd87c7a8b4ce1130fdfaf20ec4a904ddadaa38ba8d663d6220cc8cbc","sha256:5e4425533f8993b6ba8c37424cba0cef0cfa93849a7cb73c6445e7b480633adf"],"state_sha256":"b97ce3525a746f0320719bbf3bf51f3d10d6ae767bd473c11cdd6579a9fd0d3f"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"8JWFYwTz5t7Krl6NG4ihGJ+w08djnAmWKnwsrT0WKXoz28KJugDrZFUPo4qyfKjFpfzopGf9N0PXdXr3wnLeDA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-10T10:36:24.967374Z","bundle_sha256":"24ee7594eca435fb670a396afe3b66ae83f253dc9a331753cf7bed77a13cff2c"}}