{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2025:2RYEBC6IL5OPZ4OOVG7BTOEH2W","short_pith_number":"pith:2RYEBC6I","canonical_record":{"source":{"id":"2503.15850","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2025-03-20T05:04:29Z","cross_cats_sorted":[],"title_canon_sha256":"cb0f1a0a6de22904c2637e4d9dfa0e9d928ce94569b6844e99085446446e8345","abstract_canon_sha256":"b00b45ab0afd2b63c94110334affca96f7fe1f79dd094bf254520d7577d64c56"},"schema_version":"1.0"},"canonical_sha256":"d470408bc85f5cfcf1cea9be19b887d59e98ef03f9897bda99030020439fa342","source":{"kind":"arxiv","id":"2503.15850","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2503.15850","created_at":"2026-07-05T11:15:20Z"},{"alias_kind":"arxiv_version","alias_value":"2503.15850v2","created_at":"2026-07-05T11:15:20Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2503.15850","created_at":"2026-07-05T11:15:20Z"},{"alias_kind":"pith_short_12","alias_value":"2RYEBC6IL5OP","created_at":"2026-07-05T11:15:20Z"},{"alias_kind":"pith_short_16","alias_value":"2RYEBC6IL5OPZ4OO","created_at":"2026-07-05T11:15:20Z"},{"alias_kind":"pith_short_8","alias_value":"2RYEBC6I","created_at":"2026-07-05T11:15:20Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2025:2RYEBC6IL5OPZ4OOVG7BTOEH2W","target":"record","payload":{"canonical_record":{"source":{"id":"2503.15850","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2025-03-20T05:04:29Z","cross_cats_sorted":[],"title_canon_sha256":"cb0f1a0a6de22904c2637e4d9dfa0e9d928ce94569b6844e99085446446e8345","abstract_canon_sha256":"b00b45ab0afd2b63c94110334affca96f7fe1f79dd094bf254520d7577d64c56"},"schema_version":"1.0"},"canonical_sha256":"d470408bc85f5cfcf1cea9be19b887d59e98ef03f9897bda99030020439fa342","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:15:20.118100Z","signature_b64":"ZHs91GIwbToo6lOUREjqHcWcX3+vLPzqH2+cEvo81K5Tn7R/EkHa90d7ONsxHI73ta/imDZk9dwnuwbzijrhDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"d470408bc85f5cfcf1cea9be19b887d59e98ef03f9897bda99030020439fa342","last_reissued_at":"2026-07-05T11:15:20.117540Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:15:20.117540Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2503.15850","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T11:15:20Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"34SCT7dzJl+kT0mhfC/FwDBTl0v+7R24oPE96uoksWIcYo0ynLRymC3m5rPLhZiyFY2lyawkmPHuothk82vFCg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-09T06:18:48.750113Z"},"content_sha256":"280b1530648f5838dc16e79b10049def5e9ba530536a627f0dc2e9e9cb6a2886","schema_version":"1.0","event_id":"sha256:280b1530648f5838dc16e79b10049def5e9ba530536a627f0dc2e9e9cb6a2886"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2025:2RYEBC6IL5OPZ4OOVG7BTOEH2W","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Uncertainty Quantification and Confidence Calibration in Large Language Models: A Survey","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Chacha Chen, Hua Wei, Longchao Da, Tiejin Chen, Xiaoou Liu, Zhen Lin","submitted_at":"2025-03-20T05:04:29Z","abstract_excerpt":"Large Language Models (LLMs) excel in text generation, reasoning, and decision-making, enabling their adoption in high-stakes domains such as healthcare, law, and transportation. However, their reliability is a major concern, as they often produce plausible but incorrect responses. Uncertainty quantification (UQ) enhances trustworthiness by estimating confidence in outputs, enabling risk mitigation and selective prediction. However, traditional UQ methods struggle with LLMs due to computational constraints and decoding inconsistencies. Moreover, LLMs introduce unique uncertainty sources, such "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2503.15850","kind":"arxiv","version":2},"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/2503.15850/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":"2026-07-05T11:15:20Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"dmr5sZlinWwIuMfUkXseE0OmaoNQXTBFPtCSePvivLPb2WwQlxp7dz6+cxkaDYyguUE1U1UzPc4zcpkI/8tXAA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-09T06:18:48.750778Z"},"content_sha256":"3fbb89e3b2fbb52a18c7ff1dffac20f83bc32dd73994fd1cde2c18680030b316","schema_version":"1.0","event_id":"sha256:3fbb89e3b2fbb52a18c7ff1dffac20f83bc32dd73994fd1cde2c18680030b316"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/2RYEBC6IL5OPZ4OOVG7BTOEH2W/bundle.json","state_url":"https://pith.science/pith/2RYEBC6IL5OPZ4OOVG7BTOEH2W/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/2RYEBC6IL5OPZ4OOVG7BTOEH2W/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":"2026-07-09T06:18:48Z","links":{"resolver":"https://pith.science/pith/2RYEBC6IL5OPZ4OOVG7BTOEH2W","bundle":"https://pith.science/pith/2RYEBC6IL5OPZ4OOVG7BTOEH2W/bundle.json","state":"https://pith.science/pith/2RYEBC6IL5OPZ4OOVG7BTOEH2W/state.json","well_known_bundle":"https://pith.science/.well-known/pith/2RYEBC6IL5OPZ4OOVG7BTOEH2W/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:2RYEBC6IL5OPZ4OOVG7BTOEH2W","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":"b00b45ab0afd2b63c94110334affca96f7fe1f79dd094bf254520d7577d64c56","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2025-03-20T05:04:29Z","title_canon_sha256":"cb0f1a0a6de22904c2637e4d9dfa0e9d928ce94569b6844e99085446446e8345"},"schema_version":"1.0","source":{"id":"2503.15850","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2503.15850","created_at":"2026-07-05T11:15:20Z"},{"alias_kind":"arxiv_version","alias_value":"2503.15850v2","created_at":"2026-07-05T11:15:20Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2503.15850","created_at":"2026-07-05T11:15:20Z"},{"alias_kind":"pith_short_12","alias_value":"2RYEBC6IL5OP","created_at":"2026-07-05T11:15:20Z"},{"alias_kind":"pith_short_16","alias_value":"2RYEBC6IL5OPZ4OO","created_at":"2026-07-05T11:15:20Z"},{"alias_kind":"pith_short_8","alias_value":"2RYEBC6I","created_at":"2026-07-05T11:15:20Z"}],"graph_snapshots":[{"event_id":"sha256:3fbb89e3b2fbb52a18c7ff1dffac20f83bc32dd73994fd1cde2c18680030b316","target":"graph","created_at":"2026-07-05T11:15:20Z","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/2503.15850/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Large Language Models (LLMs) excel in text generation, reasoning, and decision-making, enabling their adoption in high-stakes domains such as healthcare, law, and transportation. However, their reliability is a major concern, as they often produce plausible but incorrect responses. Uncertainty quantification (UQ) enhances trustworthiness by estimating confidence in outputs, enabling risk mitigation and selective prediction. However, traditional UQ methods struggle with LLMs due to computational constraints and decoding inconsistencies. Moreover, LLMs introduce unique uncertainty sources, such ","authors_text":"Chacha Chen, Hua Wei, Longchao Da, Tiejin Chen, Xiaoou Liu, Zhen Lin","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2025-03-20T05:04:29Z","title":"Uncertainty Quantification and Confidence Calibration in Large Language Models: A Survey"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2503.15850","kind":"arxiv","version":2},"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":"sha256:280b1530648f5838dc16e79b10049def5e9ba530536a627f0dc2e9e9cb6a2886","target":"record","created_at":"2026-07-05T11:15:20Z","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":"b00b45ab0afd2b63c94110334affca96f7fe1f79dd094bf254520d7577d64c56","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2025-03-20T05:04:29Z","title_canon_sha256":"cb0f1a0a6de22904c2637e4d9dfa0e9d928ce94569b6844e99085446446e8345"},"schema_version":"1.0","source":{"id":"2503.15850","kind":"arxiv","version":2}},"canonical_sha256":"d470408bc85f5cfcf1cea9be19b887d59e98ef03f9897bda99030020439fa342","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"d470408bc85f5cfcf1cea9be19b887d59e98ef03f9897bda99030020439fa342","first_computed_at":"2026-07-05T11:15:20.117540Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T11:15:20.117540Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"ZHs91GIwbToo6lOUREjqHcWcX3+vLPzqH2+cEvo81K5Tn7R/EkHa90d7ONsxHI73ta/imDZk9dwnuwbzijrhDQ==","signature_status":"signed_v1","signed_at":"2026-07-05T11:15:20.118100Z","signed_message":"canonical_sha256_bytes"},"source_id":"2503.15850","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:280b1530648f5838dc16e79b10049def5e9ba530536a627f0dc2e9e9cb6a2886","sha256:3fbb89e3b2fbb52a18c7ff1dffac20f83bc32dd73994fd1cde2c18680030b316"],"state_sha256":"14ab8e850d7195a8096d9ca255cf7dc673b63de26740594b263f0732e103e3c5"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"293vKQlSvRLnqax/6e80ph6Ij6QAHdmRg7XKvrOaHwpEjAgX85BG250i9oROyn/M0dB0HikDWMzD5zzLA+8cDw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-09T06:18:48.754076Z","bundle_sha256":"f51094d3ab701ad2de164584c81cbf81e76e69c9ca569c89f718f06760a957c5"}}