{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:JMJF5WTVI7RRBXXJLYLINAA2R4","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":"954678dc40d436409ae6d3487ca5928b48ce57120469e3606d6533d01104cc7f","cross_cats_sorted":["cs.AI","cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2025-04-24T15:39:46Z","title_canon_sha256":"6d1e786b99790f38f3c029a684efd7a5294d5720d716cf7b88b05a0697d771f5"},"schema_version":"1.0","source":{"id":"2504.17671","kind":"arxiv","version":3}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2504.17671","created_at":"2026-07-05T11:03:23Z"},{"alias_kind":"arxiv_version","alias_value":"2504.17671v3","created_at":"2026-07-05T11:03:23Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2504.17671","created_at":"2026-07-05T11:03:23Z"},{"alias_kind":"pith_short_12","alias_value":"JMJF5WTVI7RR","created_at":"2026-07-05T11:03:23Z"},{"alias_kind":"pith_short_16","alias_value":"JMJF5WTVI7RRBXXJ","created_at":"2026-07-05T11:03:23Z"},{"alias_kind":"pith_short_8","alias_value":"JMJF5WTV","created_at":"2026-07-05T11:03:23Z"}],"graph_snapshots":[{"event_id":"sha256:4f1331fc61f17616fd8fd11b2b2d78aee5e1d14decec032fc0e56224bf0e1f5f","target":"graph","created_at":"2026-07-05T11:03:23Z","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/2504.17671/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"This study addresses the critical challenge of hallucination mitigation in Large Vision-Language Models (LVLMs) for Visual Question Answering (VQA) tasks through a Split Conformal Prediction (SCP) framework. While LVLMs excel in multi-modal reasoning, their outputs often exhibit hallucinated content with high confidence, posing risks in safety-critical applications. We propose a model-agnostic uncertainty quantification method that integrates dynamic threshold calibration and cross-modal consistency verification. By partitioning data into calibration and test sets, the framework computes nonco","authors_text":"Weiyan Wen, Yuanchang Ye","cross_cats":["cs.AI","cs.LG"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2025-04-24T15:39:46Z","title":"Data-Driven Calibration of Prediction Sets in Large Vision-Language Models Based on Inductive Conformal Prediction"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2504.17671","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:fd607f6b90489c7d3c9146653c03f14c7792eed94276dbf5af4110f38d9e679a","target":"record","created_at":"2026-07-05T11:03:23Z","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":"954678dc40d436409ae6d3487ca5928b48ce57120469e3606d6533d01104cc7f","cross_cats_sorted":["cs.AI","cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2025-04-24T15:39:46Z","title_canon_sha256":"6d1e786b99790f38f3c029a684efd7a5294d5720d716cf7b88b05a0697d771f5"},"schema_version":"1.0","source":{"id":"2504.17671","kind":"arxiv","version":3}},"canonical_sha256":"4b125eda7547e310dee95e1686801a8f2880b41f01076b1286338927ad0153d4","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"4b125eda7547e310dee95e1686801a8f2880b41f01076b1286338927ad0153d4","first_computed_at":"2026-07-05T11:03:23.608406Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T11:03:23.608406Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"26jWcN1MNY+5KKQGu7AKMUZP6eA7lN0Nt3xD65Z3DjHPUsBCHne1qfDKrgSm/VzGUy+MibEIi4tLE4UObgiWBw==","signature_status":"signed_v1","signed_at":"2026-07-05T11:03:23.608804Z","signed_message":"canonical_sha256_bytes"},"source_id":"2504.17671","source_kind":"arxiv","source_version":3}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:fd607f6b90489c7d3c9146653c03f14c7792eed94276dbf5af4110f38d9e679a","sha256:4f1331fc61f17616fd8fd11b2b2d78aee5e1d14decec032fc0e56224bf0e1f5f"],"state_sha256":"cf4ec2b4c4d6b806f9465cc53da4d0b44e268e528c8f791bed4144e4bedc8b3c"}