{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2025:QKPZTRU5PJB55VQQSELLWKPLZY","short_pith_number":"pith:QKPZTRU5","canonical_record":{"source":{"id":"2508.09804","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2025-08-13T13:39:17Z","cross_cats_sorted":[],"title_canon_sha256":"396f09de44c717f8a92d0cbd1e2072cf215ccb988e777ba605a69723ee59c655","abstract_canon_sha256":"30805da009290a092e149471c7f4277b0db4cd6d0c899ae7e8d7d2f966d5484d"},"schema_version":"1.0"},"canonical_sha256":"829f99c69d7a43ded6109116bb29ebce3cd5a9abc975a9fd9d3a9965d3c4ad5f","source":{"kind":"arxiv","id":"2508.09804","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2508.09804","created_at":"2026-07-05T11:53:22Z"},{"alias_kind":"arxiv_version","alias_value":"2508.09804v1","created_at":"2026-07-05T11:53:22Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2508.09804","created_at":"2026-07-05T11:53:22Z"},{"alias_kind":"pith_short_12","alias_value":"QKPZTRU5PJB5","created_at":"2026-07-05T11:53:22Z"},{"alias_kind":"pith_short_16","alias_value":"QKPZTRU5PJB55VQQ","created_at":"2026-07-05T11:53:22Z"},{"alias_kind":"pith_short_8","alias_value":"QKPZTRU5","created_at":"2026-07-05T11:53:22Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2025:QKPZTRU5PJB55VQQSELLWKPLZY","target":"record","payload":{"canonical_record":{"source":{"id":"2508.09804","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2025-08-13T13:39:17Z","cross_cats_sorted":[],"title_canon_sha256":"396f09de44c717f8a92d0cbd1e2072cf215ccb988e777ba605a69723ee59c655","abstract_canon_sha256":"30805da009290a092e149471c7f4277b0db4cd6d0c899ae7e8d7d2f966d5484d"},"schema_version":"1.0"},"canonical_sha256":"829f99c69d7a43ded6109116bb29ebce3cd5a9abc975a9fd9d3a9965d3c4ad5f","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:53:22.108039Z","signature_b64":"GDUgVXNBiVfUXPnJKCF8V0HdFSCrnENBuNM/z7VXjLdSUSqgmyu4SgQOd2E9AxQi13WvTHt+acM3pAeu50DYCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"829f99c69d7a43ded6109116bb29ebce3cd5a9abc975a9fd9d3a9965d3c4ad5f","last_reissued_at":"2026-07-05T11:53:22.107562Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:53:22.107562Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2508.09804","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":"2026-07-05T11:53:22Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"xFHKQK/KZOkDW/Lg94Ms1g1nVXXcatxBSdDwt52fdnHUCju30n5jzpg5rhkjGmgdQ27QHN2w73SPqDsgnZBoDg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-03T03:41:24.736825Z"},"content_sha256":"3e3841aabeba2293d4cb6bf7f933132120aff4710989dd136efa6cb2d33a719a","schema_version":"1.0","event_id":"sha256:3e3841aabeba2293d4cb6bf7f933132120aff4710989dd136efa6cb2d33a719a"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2025:QKPZTRU5PJB55VQQSELLWKPLZY","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"BigCharts-R1: Enhanced Chart Reasoning with Visual Reinforcement Finetuning","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Abhay Puri, Ahmed Masry, Alexandre Pich\\'e, Christopher Pal, David Vazquez, Dzmitry Bahdanau, Enamul Hoque, Juan A. Rodriguez, Khyati Mahajan, Masoud Hashemi, Megh Thakkar, Perouz Taslakian, Sai Rajeswar, Sathwik Tejaswi Madhusudhan, Spandana Gella, Vikas Yadav","submitted_at":"2025-08-13T13:39:17Z","abstract_excerpt":"Charts are essential to data analysis, transforming raw data into clear visual representations that support human decision-making. Although current vision-language models (VLMs) have made significant progress, they continue to struggle with chart comprehension due to training on datasets that lack diversity and real-world authenticity, or on automatically extracted underlying data tables of charts, which can contain numerous estimation errors. Furthermore, existing models only rely on supervised fine-tuning using these low-quality datasets, severely limiting their effectiveness. To address the"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2508.09804","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/2508.09804/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":"2026-07-05T11:53:22Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"EAKLV5GmqCb1bdlmYUoyylooc1A9rwnR1cLtnfM77EvaiWZ+CJ0cnvV4So8+9Fbynj1hIyqO1jVVITbQh8G6DQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-03T03:41:24.737405Z"},"content_sha256":"787edf6532c54c586beda58b27b9aab285d5338963a8a660d12cf4f7d58ad762","schema_version":"1.0","event_id":"sha256:787edf6532c54c586beda58b27b9aab285d5338963a8a660d12cf4f7d58ad762"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/QKPZTRU5PJB55VQQSELLWKPLZY/bundle.json","state_url":"https://pith.science/pith/QKPZTRU5PJB55VQQSELLWKPLZY/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/QKPZTRU5PJB55VQQSELLWKPLZY/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":"2026-08-03T03:41:24Z","links":{"resolver":"https://pith.science/pith/QKPZTRU5PJB55VQQSELLWKPLZY","bundle":"https://pith.science/pith/QKPZTRU5PJB55VQQSELLWKPLZY/bundle.json","state":"https://pith.science/pith/QKPZTRU5PJB55VQQSELLWKPLZY/state.json","well_known_bundle":"https://pith.science/.well-known/pith/QKPZTRU5PJB55VQQSELLWKPLZY/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:QKPZTRU5PJB55VQQSELLWKPLZY","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":"30805da009290a092e149471c7f4277b0db4cd6d0c899ae7e8d7d2f966d5484d","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2025-08-13T13:39:17Z","title_canon_sha256":"396f09de44c717f8a92d0cbd1e2072cf215ccb988e777ba605a69723ee59c655"},"schema_version":"1.0","source":{"id":"2508.09804","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2508.09804","created_at":"2026-07-05T11:53:22Z"},{"alias_kind":"arxiv_version","alias_value":"2508.09804v1","created_at":"2026-07-05T11:53:22Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2508.09804","created_at":"2026-07-05T11:53:22Z"},{"alias_kind":"pith_short_12","alias_value":"QKPZTRU5PJB5","created_at":"2026-07-05T11:53:22Z"},{"alias_kind":"pith_short_16","alias_value":"QKPZTRU5PJB55VQQ","created_at":"2026-07-05T11:53:22Z"},{"alias_kind":"pith_short_8","alias_value":"QKPZTRU5","created_at":"2026-07-05T11:53:22Z"}],"graph_snapshots":[{"event_id":"sha256:787edf6532c54c586beda58b27b9aab285d5338963a8a660d12cf4f7d58ad762","target":"graph","created_at":"2026-07-05T11:53:22Z","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/2508.09804/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Charts are essential to data analysis, transforming raw data into clear visual representations that support human decision-making. Although current vision-language models (VLMs) have made significant progress, they continue to struggle with chart comprehension due to training on datasets that lack diversity and real-world authenticity, or on automatically extracted underlying data tables of charts, which can contain numerous estimation errors. Furthermore, existing models only rely on supervised fine-tuning using these low-quality datasets, severely limiting their effectiveness. To address the","authors_text":"Abhay Puri, Ahmed Masry, Alexandre Pich\\'e, Christopher Pal, David Vazquez, Dzmitry Bahdanau, Enamul Hoque, Juan A. Rodriguez, Khyati Mahajan, Masoud Hashemi, Megh Thakkar, Perouz Taslakian, Sai Rajeswar, Sathwik Tejaswi Madhusudhan, Spandana Gella, Vikas Yadav","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2025-08-13T13:39:17Z","title":"BigCharts-R1: Enhanced Chart Reasoning with Visual Reinforcement Finetuning"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2508.09804","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":"sha256:3e3841aabeba2293d4cb6bf7f933132120aff4710989dd136efa6cb2d33a719a","target":"record","created_at":"2026-07-05T11:53:22Z","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":"30805da009290a092e149471c7f4277b0db4cd6d0c899ae7e8d7d2f966d5484d","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2025-08-13T13:39:17Z","title_canon_sha256":"396f09de44c717f8a92d0cbd1e2072cf215ccb988e777ba605a69723ee59c655"},"schema_version":"1.0","source":{"id":"2508.09804","kind":"arxiv","version":1}},"canonical_sha256":"829f99c69d7a43ded6109116bb29ebce3cd5a9abc975a9fd9d3a9965d3c4ad5f","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"829f99c69d7a43ded6109116bb29ebce3cd5a9abc975a9fd9d3a9965d3c4ad5f","first_computed_at":"2026-07-05T11:53:22.107562Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T11:53:22.107562Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"GDUgVXNBiVfUXPnJKCF8V0HdFSCrnENBuNM/z7VXjLdSUSqgmyu4SgQOd2E9AxQi13WvTHt+acM3pAeu50DYCg==","signature_status":"signed_v1","signed_at":"2026-07-05T11:53:22.108039Z","signed_message":"canonical_sha256_bytes"},"source_id":"2508.09804","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:3e3841aabeba2293d4cb6bf7f933132120aff4710989dd136efa6cb2d33a719a","sha256:787edf6532c54c586beda58b27b9aab285d5338963a8a660d12cf4f7d58ad762"],"state_sha256":"ca1bb80bf5a4b3531415df67e3e73cb89178e5ce1f7b9248b03aabd569418692"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"H0ecFVTwpvQcC+zUVOu1hGo3oUSk/JKeOhM4BSc6nkc5Wtj2uTCoJnI3j5Vh5IXGSJrhqqgqs1oCGO7z8GrbBA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-03T03:41:24.740952Z","bundle_sha256":"a81c8609d7fa173864bacb59c757751ea9258cd777500d3d0a27ce3701b341b2"}}