{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2025:4VHY3LYM7QTMN7YYZTKFCAUNCO","short_pith_number":"pith:4VHY3LYM","canonical_record":{"source":{"id":"2503.04095","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2025-03-06T05:08:40Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"8f8d1cbd515c9f52ebbf829f5e75555d2c7acc5159d50eadf3821a63f3454838","abstract_canon_sha256":"3b4aff4f747ff8d08aeb5277e792c2ee5e5c8e32ce7f71f9b6a8174c08f4dfcd"},"schema_version":"1.0"},"canonical_sha256":"e54f8daf0cfc26c6ff18ccd451028d1385197d3a9972f6cb4292247e5d2d6700","source":{"kind":"arxiv","id":"2503.04095","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2503.04095","created_at":"2026-07-05T10:26:16Z"},{"alias_kind":"arxiv_version","alias_value":"2503.04095v2","created_at":"2026-07-05T10:26:16Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2503.04095","created_at":"2026-07-05T10:26:16Z"},{"alias_kind":"pith_short_12","alias_value":"4VHY3LYM7QTM","created_at":"2026-07-05T10:26:16Z"},{"alias_kind":"pith_short_16","alias_value":"4VHY3LYM7QTMN7YY","created_at":"2026-07-05T10:26:16Z"},{"alias_kind":"pith_short_8","alias_value":"4VHY3LYM","created_at":"2026-07-05T10:26:16Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2025:4VHY3LYM7QTMN7YYZTKFCAUNCO","target":"record","payload":{"canonical_record":{"source":{"id":"2503.04095","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2025-03-06T05:08:40Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"8f8d1cbd515c9f52ebbf829f5e75555d2c7acc5159d50eadf3821a63f3454838","abstract_canon_sha256":"3b4aff4f747ff8d08aeb5277e792c2ee5e5c8e32ce7f71f9b6a8174c08f4dfcd"},"schema_version":"1.0"},"canonical_sha256":"e54f8daf0cfc26c6ff18ccd451028d1385197d3a9972f6cb4292247e5d2d6700","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:26:16.542411Z","signature_b64":"RtRD1ly7weN45fDu9+m7GutRfjTSVztIwdzSju/JYkTTDf/NJwNwxrK4WBbNx/jS8oBZhPDu/toFdLKSvWRoAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"e54f8daf0cfc26c6ff18ccd451028d1385197d3a9972f6cb4292247e5d2d6700","last_reissued_at":"2026-07-05T10:26:16.541652Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:26:16.541652Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2503.04095","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T10:26:16Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"LaeHj9NxOudLD2kg6UQEmYkQ9xClFBFc6gConVLZiyF97ZLafEDNKc1QIOFL6FkYA7EV7DL8mOcNDXuVY0sPDg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-16T19:42:12.266591Z"},"content_sha256":"04a9e985aba194a020dec23192013cb14c88800862ab8f056296985b6e77853a","schema_version":"1.0","event_id":"sha256:04a9e985aba194a020dec23192013cb14c88800862ab8f056296985b6e77853a"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2025:4VHY3LYM7QTMN7YYZTKFCAUNCO","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Chart-HQA: A Benchmark for Hypothetical Question Answering in Charts","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CL","authors_text":"Juncheng Li, Jun Lin, Qian Xiao, Siliang Tang, Xiangnan Chen, Yi Yang, Yuancheng Fang, Yueting Zhuang","submitted_at":"2025-03-06T05:08:40Z","abstract_excerpt":"Multimodal Large Language Models (MLLMs) have garnered significant attention for their strong visual-semantic understanding. Most existing chart benchmarks evaluate MLLMs' ability to parse information from charts to answer questions. However, they overlook the inherent output biases of MLLMs, where models rely on their parametric memory to answer questions rather than genuinely understanding the chart content. To address this limitation, we introduce a novel Chart Hypothetical Question Answering (HQA) task, which imposes assumptions on the same question to compel models to engage in counterfac"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2503.04095","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/2503.04095/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":"2026-07-05T10:26:16Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"7ZU5z4XR5sn3wwMaCZpc7ZRD+QFtZ1W1V7lNcZ0urpCwreb2c2mh5xsDwmCcoE4PqhxN3r60yIdWHZ91r94GCg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-16T19:42:12.267408Z"},"content_sha256":"23c046f7d9cc7267b3af382f160ebeb2b94a63ba7e342bc6bb05c25f5102f0b2","schema_version":"1.0","event_id":"sha256:23c046f7d9cc7267b3af382f160ebeb2b94a63ba7e342bc6bb05c25f5102f0b2"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/4VHY3LYM7QTMN7YYZTKFCAUNCO/bundle.json","state_url":"https://pith.science/pith/4VHY3LYM7QTMN7YYZTKFCAUNCO/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/4VHY3LYM7QTMN7YYZTKFCAUNCO/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":"2026-08-16T19:42:12Z","links":{"resolver":"https://pith.science/pith/4VHY3LYM7QTMN7YYZTKFCAUNCO","bundle":"https://pith.science/pith/4VHY3LYM7QTMN7YYZTKFCAUNCO/bundle.json","state":"https://pith.science/pith/4VHY3LYM7QTMN7YYZTKFCAUNCO/state.json","well_known_bundle":"https://pith.science/.well-known/pith/4VHY3LYM7QTMN7YYZTKFCAUNCO/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:4VHY3LYM7QTMN7YYZTKFCAUNCO","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":"3b4aff4f747ff8d08aeb5277e792c2ee5e5c8e32ce7f71f9b6a8174c08f4dfcd","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2025-03-06T05:08:40Z","title_canon_sha256":"8f8d1cbd515c9f52ebbf829f5e75555d2c7acc5159d50eadf3821a63f3454838"},"schema_version":"1.0","source":{"id":"2503.04095","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2503.04095","created_at":"2026-07-05T10:26:16Z"},{"alias_kind":"arxiv_version","alias_value":"2503.04095v2","created_at":"2026-07-05T10:26:16Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2503.04095","created_at":"2026-07-05T10:26:16Z"},{"alias_kind":"pith_short_12","alias_value":"4VHY3LYM7QTM","created_at":"2026-07-05T10:26:16Z"},{"alias_kind":"pith_short_16","alias_value":"4VHY3LYM7QTMN7YY","created_at":"2026-07-05T10:26:16Z"},{"alias_kind":"pith_short_8","alias_value":"4VHY3LYM","created_at":"2026-07-05T10:26:16Z"}],"graph_snapshots":[{"event_id":"sha256:23c046f7d9cc7267b3af382f160ebeb2b94a63ba7e342bc6bb05c25f5102f0b2","target":"graph","created_at":"2026-07-05T10:26:16Z","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/2503.04095/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Multimodal Large Language Models (MLLMs) have garnered significant attention for their strong visual-semantic understanding. Most existing chart benchmarks evaluate MLLMs' ability to parse information from charts to answer questions. However, they overlook the inherent output biases of MLLMs, where models rely on their parametric memory to answer questions rather than genuinely understanding the chart content. To address this limitation, we introduce a novel Chart Hypothetical Question Answering (HQA) task, which imposes assumptions on the same question to compel models to engage in counterfac","authors_text":"Juncheng Li, Jun Lin, Qian Xiao, Siliang Tang, Xiangnan Chen, Yi Yang, Yuancheng Fang, Yueting Zhuang","cross_cats":["cs.AI"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2025-03-06T05:08:40Z","title":"Chart-HQA: A Benchmark for Hypothetical Question Answering in Charts"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2503.04095","kind":"arxiv","version":2},"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":"sha256:04a9e985aba194a020dec23192013cb14c88800862ab8f056296985b6e77853a","target":"record","created_at":"2026-07-05T10:26:16Z","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":"3b4aff4f747ff8d08aeb5277e792c2ee5e5c8e32ce7f71f9b6a8174c08f4dfcd","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2025-03-06T05:08:40Z","title_canon_sha256":"8f8d1cbd515c9f52ebbf829f5e75555d2c7acc5159d50eadf3821a63f3454838"},"schema_version":"1.0","source":{"id":"2503.04095","kind":"arxiv","version":2}},"canonical_sha256":"e54f8daf0cfc26c6ff18ccd451028d1385197d3a9972f6cb4292247e5d2d6700","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"e54f8daf0cfc26c6ff18ccd451028d1385197d3a9972f6cb4292247e5d2d6700","first_computed_at":"2026-07-05T10:26:16.541652Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T10:26:16.541652Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"RtRD1ly7weN45fDu9+m7GutRfjTSVztIwdzSju/JYkTTDf/NJwNwxrK4WBbNx/jS8oBZhPDu/toFdLKSvWRoAw==","signature_status":"signed_v1","signed_at":"2026-07-05T10:26:16.542411Z","signed_message":"canonical_sha256_bytes"},"source_id":"2503.04095","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:04a9e985aba194a020dec23192013cb14c88800862ab8f056296985b6e77853a","sha256:23c046f7d9cc7267b3af382f160ebeb2b94a63ba7e342bc6bb05c25f5102f0b2"],"state_sha256":"19e77696af54c7960d96ca22d4bb81e999711f3c3fe7167bfceb791badf28d3e"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Qe+xM6duPEYuqsJfbVEfbV3Ic2cPOTP0C1EEEhuZ2H3VW7/3PiN21vX5d6aUX9fTR/svz3permXFILG6ZsqUDA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-16T19:42:12.310745Z","bundle_sha256":"9a32a8c6b17e5038d4e273ac3f3d697b932d4ca4f6e5f12bb7c084f78617d20b"}}