{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:ZFSV7CZXAF2EWADGBRWUQHPLP3","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":"d46786a18b923ff80758ba64b13ed6adeb2a50cc6c7e559a1b15fdff69081313","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.AI","submitted_at":"2025-08-16T08:26:55Z","title_canon_sha256":"c9e923ea18e5237bab210ca5a4d95e85c76429a2aec1051553bbc5847a1b7007"},"schema_version":"1.0","source":{"id":"2508.11975","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2508.11975","created_at":"2026-07-05T11:54:58Z"},{"alias_kind":"arxiv_version","alias_value":"2508.11975v1","created_at":"2026-07-05T11:54:58Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2508.11975","created_at":"2026-07-05T11:54:58Z"},{"alias_kind":"pith_short_12","alias_value":"ZFSV7CZXAF2E","created_at":"2026-07-05T11:54:58Z"},{"alias_kind":"pith_short_16","alias_value":"ZFSV7CZXAF2EWADG","created_at":"2026-07-05T11:54:58Z"},{"alias_kind":"pith_short_8","alias_value":"ZFSV7CZX","created_at":"2026-07-05T11:54:58Z"}],"graph_snapshots":[{"event_id":"sha256:a971ea5fa649dbe836bd05faeab22a4bbcc832971817b2346f04816b262bbb6a","target":"graph","created_at":"2026-07-05T11:54:58Z","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/2508.11975/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Vision Language Models (VLMs) often struggle with chart understanding tasks, particularly in accurate chart description and complex reasoning. Synthetic data generation is a promising solution, while usually facing the challenge of noise labels. To address this challenge, we first introduce a chart synthesis pipeline that generates aligned chart-question-answer triplets through code generation and execution, ensuring the reliability of synthetic data without human intervention. Furthermore, inspired by test-time scaling that increases inference budget and thereby improves performance, we desig","authors_text":"Gongyao Jiang, Qiong Luo","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.AI","submitted_at":"2025-08-16T08:26:55Z","title":"Chart-CoCa: Self-Improving Chart Understanding of Vision LMs via Code-Driven Synthesis and Candidate-Conditioned Answering"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2508.11975","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:a3f2eac9aedcea59c7ab81e0c5f0a683464e83ed41c184d3566cb4119604bac1","target":"record","created_at":"2026-07-05T11:54:58Z","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":"d46786a18b923ff80758ba64b13ed6adeb2a50cc6c7e559a1b15fdff69081313","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.AI","submitted_at":"2025-08-16T08:26:55Z","title_canon_sha256":"c9e923ea18e5237bab210ca5a4d95e85c76429a2aec1051553bbc5847a1b7007"},"schema_version":"1.0","source":{"id":"2508.11975","kind":"arxiv","version":1}},"canonical_sha256":"c9655f8b3701744b00660c6d481deb7edeed88da4f8237db9209e0c7a456d7e8","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"c9655f8b3701744b00660c6d481deb7edeed88da4f8237db9209e0c7a456d7e8","first_computed_at":"2026-07-05T11:54:58.073425Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T11:54:58.073425Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"00eb8kVrjFi1g5lWyJ+PXXucsjjydUYDNeJ0EzziMpvIVrnwW9xBZ8VK8x4PpPQ/JLDwEsxrEgM430js5BqDDg==","signature_status":"signed_v1","signed_at":"2026-07-05T11:54:58.073903Z","signed_message":"canonical_sha256_bytes"},"source_id":"2508.11975","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:a3f2eac9aedcea59c7ab81e0c5f0a683464e83ed41c184d3566cb4119604bac1","sha256:a971ea5fa649dbe836bd05faeab22a4bbcc832971817b2346f04816b262bbb6a"],"state_sha256":"7037b17d58a70d489fc3398ff5151926d6355a97e163a693eb8d1117b9691245"}