{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:46NGDOY2AY35LXII55LMA32URX","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":"a5566c5915a363b46537fd38febceb6299c6f77500deda7df9ee18c76fa86a17","cross_cats_sorted":["cs.CL","cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2025-07-22T12:27:08Z","title_canon_sha256":"eccb6f91164362b42d2c6a36f4390421817a108f9674dd9e9e3609b124df3220"},"schema_version":"1.0","source":{"id":"2507.16518","kind":"arxiv","version":3}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2507.16518","created_at":"2026-06-25T01:17:45Z"},{"alias_kind":"arxiv_version","alias_value":"2507.16518v3","created_at":"2026-06-25T01:17:45Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2507.16518","created_at":"2026-06-25T01:17:45Z"},{"alias_kind":"pith_short_12","alias_value":"46NGDOY2AY35","created_at":"2026-06-25T01:17:45Z"},{"alias_kind":"pith_short_16","alias_value":"46NGDOY2AY35LXII","created_at":"2026-06-25T01:17:45Z"},{"alias_kind":"pith_short_8","alias_value":"46NGDOY2","created_at":"2026-06-25T01:17:45Z"}],"graph_snapshots":[{"event_id":"sha256:233d03441ba5368e4ea6fcddf8ecafe7aa1893453b9464a1716519599e2d3ddc","target":"graph","created_at":"2026-06-25T01:17:45Z","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/2507.16518/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Recent advances in multimodal large language models (MLLMs) have shown impressive reasoning capabilities. However, further enhancing existing MLLMs necessitates high-quality vision-language datasets with carefully curated task complexities, which are both costly and challenging to scale. Although recent self-improving models that iteratively refine themselves offer a feasible solution, they still suffer from two core challenges: (i) most existing methods augment visual or textual data separately, resulting in discrepancies in data complexity (e.g., over-simplified diagrams paired with redundan","authors_text":"Hang Xu, Hanhui Li, Jianhua Han, Kui Zhang, Meng Cao, Wentao Hu, Xiaodan Liang, Xiuwei Chen, Yihan Zeng, Yongxin Wang Jun Zhou, Yu-Jie Yuan, Zisheng Chen","cross_cats":["cs.CL","cs.LG"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2025-07-22T12:27:08Z","title":"SyncLoop: A Multimodal Dual-Loop Framework for Self-Improving Mathematical Reasoning"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2507.16518","kind":"arxiv","version":3},"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:abfee3cd8aa9787e741e881e6b62287173316825de934f7bed992c88b9885bbd","target":"record","created_at":"2026-06-25T01:17:45Z","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":"a5566c5915a363b46537fd38febceb6299c6f77500deda7df9ee18c76fa86a17","cross_cats_sorted":["cs.CL","cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2025-07-22T12:27:08Z","title_canon_sha256":"eccb6f91164362b42d2c6a36f4390421817a108f9674dd9e9e3609b124df3220"},"schema_version":"1.0","source":{"id":"2507.16518","kind":"arxiv","version":3}},"canonical_sha256":"e79a61bb1a0637d5dd08ef56c06f548dcf13d436778cac5e1c3c8457f12d02a3","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"e79a61bb1a0637d5dd08ef56c06f548dcf13d436778cac5e1c3c8457f12d02a3","first_computed_at":"2026-06-25T01:17:45.535149Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-06-25T01:17:45.535149Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"siaMztUxgPpX7VYuJZRRiDLMXdoI7xO9ZIpicKoBtNw6nQ0JDyAmVrqjVxnAEg8D9Quz/1piSSXmLaiXlEgqCw==","signature_status":"signed_v1","signed_at":"2026-06-25T01:17:45.535680Z","signed_message":"canonical_sha256_bytes"},"source_id":"2507.16518","source_kind":"arxiv","source_version":3}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:abfee3cd8aa9787e741e881e6b62287173316825de934f7bed992c88b9885bbd","sha256:233d03441ba5368e4ea6fcddf8ecafe7aa1893453b9464a1716519599e2d3ddc"],"state_sha256":"6570ae053eda7f6fc019bb09af23cab028d6f365d1584323307598c2bb672d29"}