{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:HZFBGQRIDYEGUB4MXQHLZXBPWL","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":"f637c556acc9d3b8b5844c027a8a3d3091c757e7163351f126a99135981da709","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2025-01-24T20:13:29Z","title_canon_sha256":"4ef10ba85869e05e12114881aca3ad5bddd71655d42f9da73258b7a4ecdcbc45"},"schema_version":"1.0","source":{"id":"2501.14905","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2501.14905","created_at":"2026-07-05T10:05:20Z"},{"alias_kind":"arxiv_version","alias_value":"2501.14905v1","created_at":"2026-07-05T10:05:20Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2501.14905","created_at":"2026-07-05T10:05:20Z"},{"alias_kind":"pith_short_12","alias_value":"HZFBGQRIDYEG","created_at":"2026-07-05T10:05:20Z"},{"alias_kind":"pith_short_16","alias_value":"HZFBGQRIDYEGUB4M","created_at":"2026-07-05T10:05:20Z"},{"alias_kind":"pith_short_8","alias_value":"HZFBGQRI","created_at":"2026-07-05T10:05:20Z"}],"graph_snapshots":[{"event_id":"sha256:a7b1d27b94b21057adac45b4bece8ff16b6c6136bca338e02aff88445ac34198","target":"graph","created_at":"2026-07-05T10:05:20Z","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/2501.14905/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Vision language models have achieved impressive results across various fields. However, adoption in remote sensing remains limited, largely due to the scarcity of paired image-text data. To bridge this gap, synthetic caption generation has gained interest, traditionally relying on rule-based methods that use metadata or bounding boxes. While these approaches provide some description, they often lack the depth needed to capture complex wide-area scenes. Large language models (LLMs) offer a promising alternative for generating more descriptive captions, yet they can produce generic outputs and a","authors_text":"J. Taylor Perron, Kerri Cahoy, Madeline Anderson, Miriam Cha, Nathaniel Maidel, William T. Freeman","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2025-01-24T20:13:29Z","title":"Measuring and Mitigating Hallucinations in Vision-Language Dataset Generation for Remote Sensing"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2501.14905","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:c4a71015735da3d7fae0c5c227de9818f24483a6c9be58793e32c3951a4d1c32","target":"record","created_at":"2026-07-05T10:05:20Z","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":"f637c556acc9d3b8b5844c027a8a3d3091c757e7163351f126a99135981da709","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2025-01-24T20:13:29Z","title_canon_sha256":"4ef10ba85869e05e12114881aca3ad5bddd71655d42f9da73258b7a4ecdcbc45"},"schema_version":"1.0","source":{"id":"2501.14905","kind":"arxiv","version":1}},"canonical_sha256":"3e4a1342281e086a078cbc0ebcdc2fb2f270bf1a8820531dea6152530a0b42a9","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"3e4a1342281e086a078cbc0ebcdc2fb2f270bf1a8820531dea6152530a0b42a9","first_computed_at":"2026-07-05T10:05:20.532697Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T10:05:20.532697Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"qdYTuR8PdkNsvYv2m9uJSbpTOoHEN8y/WwIRMnfVkU1s/mNwq6XcU+JaioZ/od7g3Qvy3QfTDgJOVqIbx3iUDg==","signature_status":"signed_v1","signed_at":"2026-07-05T10:05:20.533111Z","signed_message":"canonical_sha256_bytes"},"source_id":"2501.14905","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:c4a71015735da3d7fae0c5c227de9818f24483a6c9be58793e32c3951a4d1c32","sha256:a7b1d27b94b21057adac45b4bece8ff16b6c6136bca338e02aff88445ac34198"],"state_sha256":"a9468fa3fb40a7a5f78c6c4888314a1af71f8644ea9286bda933d75c6c655348"}