{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2025:B45OJOSQOMBUVYSU5ZJKWIEU3K","short_pith_number":"pith:B45OJOSQ","canonical_record":{"source":{"id":"2512.19134","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2025-12-22T08:28:05Z","cross_cats_sorted":["cs.IR"],"title_canon_sha256":"40788fbbaa988fb862a3e85698ebedb0eb73c74e5afc3086eb4a025b6e3e673a","abstract_canon_sha256":"baa4ba33d3c4085ce67793cf3fdb61d915cac173491f454b1a1836ac3497910b"},"schema_version":"1.0"},"canonical_sha256":"0f3ae4ba5073034ae254ee52ab2094dab8f421f61ad05586cb353fa07bb2c16b","source":{"kind":"arxiv","id":"2512.19134","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2512.19134","created_at":"2026-05-20T00:04:19Z"},{"alias_kind":"arxiv_version","alias_value":"2512.19134v2","created_at":"2026-05-20T00:04:19Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2512.19134","created_at":"2026-05-20T00:04:19Z"},{"alias_kind":"pith_short_12","alias_value":"B45OJOSQOMBU","created_at":"2026-05-20T00:04:19Z"},{"alias_kind":"pith_short_16","alias_value":"B45OJOSQOMBUVYSU","created_at":"2026-05-20T00:04:19Z"},{"alias_kind":"pith_short_8","alias_value":"B45OJOSQ","created_at":"2026-05-20T00:04:19Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2025:B45OJOSQOMBUVYSU5ZJKWIEU3K","target":"record","payload":{"canonical_record":{"source":{"id":"2512.19134","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2025-12-22T08:28:05Z","cross_cats_sorted":["cs.IR"],"title_canon_sha256":"40788fbbaa988fb862a3e85698ebedb0eb73c74e5afc3086eb4a025b6e3e673a","abstract_canon_sha256":"baa4ba33d3c4085ce67793cf3fdb61d915cac173491f454b1a1836ac3497910b"},"schema_version":"1.0"},"canonical_sha256":"0f3ae4ba5073034ae254ee52ab2094dab8f421f61ad05586cb353fa07bb2c16b","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-20T00:04:19.400938Z","signature_b64":"h6LtwkH8FPHGCQCRfjOE1fR1S6WXMrE8dj14Z/7XlGtyW1DrtD2LgpBFL5VFppw2aF4uHxmtqd+BdgzwY/lBDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"0f3ae4ba5073034ae254ee52ab2094dab8f421f61ad05586cb353fa07bb2c16b","last_reissued_at":"2026-05-20T00:04:19.400104Z","signature_status":"signed_v1","first_computed_at":"2026-05-20T00:04:19.400104Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2512.19134","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":"2026-05-20T00:04:19Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"UrImnLZGjb1Sa3/PbCxuxkP4Zc8S+PXZX9OfMgl5ync59JaCY29Hs8f5oUTOj4C+6RpoymRcix1fLOvGHtpWBA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-30T13:54:37.534914Z"},"content_sha256":"75846c5aec50f683afadeab739ab97313370fd29a263ca409607f6e8d2dddde0","schema_version":"1.0","event_id":"sha256:75846c5aec50f683afadeab739ab97313370fd29a263ca409607f6e8d2dddde0"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2025:B45OJOSQOMBUVYSU5ZJKWIEU3K","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"QuCo-RAG: Quantifying Uncertainty from the Pre-training Corpus for Dynamic Retrieval-Augmented Generation","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.IR"],"primary_cat":"cs.CL","authors_text":"Dehai Min, Kailin Zhang, Lu Cheng, Tongtong Wu","submitted_at":"2025-12-22T08:28:05Z","abstract_excerpt":"Dynamic Retrieval-Augmented Generation adaptively determines when to retrieve during generation to mitigate hallucinations in large language models (LLMs). However, existing methods rely on model-internal signals (e.g., logits, entropy), which are fundamentally unreliable because LLMs are typically ill-calibrated and often exhibit high confidence in erroneous outputs. We propose QuCo-RAG, which shifts from subjective confidence to objective statistics computed from pre-training data. Our method quantifies uncertainty through two stages: (1) before generation, we identify low-frequency entities"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2512.19134","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/2512.19134/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":"2026-05-20T00:04:19Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"7W3T3m5SH+uDgA3oTbAHBpfPdRUkSSdWLRVFqCp4EDy/plRQzriwtB1vbdtpden5XfvY2C3uT6f+5Vcp/1sQAg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-30T13:54:37.535302Z"},"content_sha256":"7bfa0fbbd222905f4bac1e627a092bdf06e7c4bc7004cd5c5a57762646115504","schema_version":"1.0","event_id":"sha256:7bfa0fbbd222905f4bac1e627a092bdf06e7c4bc7004cd5c5a57762646115504"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/B45OJOSQOMBUVYSU5ZJKWIEU3K/bundle.json","state_url":"https://pith.science/pith/B45OJOSQOMBUVYSU5ZJKWIEU3K/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/B45OJOSQOMBUVYSU5ZJKWIEU3K/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":"2026-05-30T13:54:37Z","links":{"resolver":"https://pith.science/pith/B45OJOSQOMBUVYSU5ZJKWIEU3K","bundle":"https://pith.science/pith/B45OJOSQOMBUVYSU5ZJKWIEU3K/bundle.json","state":"https://pith.science/pith/B45OJOSQOMBUVYSU5ZJKWIEU3K/state.json","well_known_bundle":"https://pith.science/.well-known/pith/B45OJOSQOMBUVYSU5ZJKWIEU3K/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:B45OJOSQOMBUVYSU5ZJKWIEU3K","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":"baa4ba33d3c4085ce67793cf3fdb61d915cac173491f454b1a1836ac3497910b","cross_cats_sorted":["cs.IR"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2025-12-22T08:28:05Z","title_canon_sha256":"40788fbbaa988fb862a3e85698ebedb0eb73c74e5afc3086eb4a025b6e3e673a"},"schema_version":"1.0","source":{"id":"2512.19134","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2512.19134","created_at":"2026-05-20T00:04:19Z"},{"alias_kind":"arxiv_version","alias_value":"2512.19134v2","created_at":"2026-05-20T00:04:19Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2512.19134","created_at":"2026-05-20T00:04:19Z"},{"alias_kind":"pith_short_12","alias_value":"B45OJOSQOMBU","created_at":"2026-05-20T00:04:19Z"},{"alias_kind":"pith_short_16","alias_value":"B45OJOSQOMBUVYSU","created_at":"2026-05-20T00:04:19Z"},{"alias_kind":"pith_short_8","alias_value":"B45OJOSQ","created_at":"2026-05-20T00:04:19Z"}],"graph_snapshots":[{"event_id":"sha256:7bfa0fbbd222905f4bac1e627a092bdf06e7c4bc7004cd5c5a57762646115504","target":"graph","created_at":"2026-05-20T00:04:19Z","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/2512.19134/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Dynamic Retrieval-Augmented Generation adaptively determines when to retrieve during generation to mitigate hallucinations in large language models (LLMs). However, existing methods rely on model-internal signals (e.g., logits, entropy), which are fundamentally unreliable because LLMs are typically ill-calibrated and often exhibit high confidence in erroneous outputs. We propose QuCo-RAG, which shifts from subjective confidence to objective statistics computed from pre-training data. Our method quantifies uncertainty through two stages: (1) before generation, we identify low-frequency entities","authors_text":"Dehai Min, Kailin Zhang, Lu Cheng, Tongtong Wu","cross_cats":["cs.IR"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2025-12-22T08:28:05Z","title":"QuCo-RAG: Quantifying Uncertainty from the Pre-training Corpus for Dynamic Retrieval-Augmented Generation"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2512.19134","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":"sha256:75846c5aec50f683afadeab739ab97313370fd29a263ca409607f6e8d2dddde0","target":"record","created_at":"2026-05-20T00:04:19Z","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":"baa4ba33d3c4085ce67793cf3fdb61d915cac173491f454b1a1836ac3497910b","cross_cats_sorted":["cs.IR"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2025-12-22T08:28:05Z","title_canon_sha256":"40788fbbaa988fb862a3e85698ebedb0eb73c74e5afc3086eb4a025b6e3e673a"},"schema_version":"1.0","source":{"id":"2512.19134","kind":"arxiv","version":2}},"canonical_sha256":"0f3ae4ba5073034ae254ee52ab2094dab8f421f61ad05586cb353fa07bb2c16b","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"0f3ae4ba5073034ae254ee52ab2094dab8f421f61ad05586cb353fa07bb2c16b","first_computed_at":"2026-05-20T00:04:19.400104Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-20T00:04:19.400104Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"h6LtwkH8FPHGCQCRfjOE1fR1S6WXMrE8dj14Z/7XlGtyW1DrtD2LgpBFL5VFppw2aF4uHxmtqd+BdgzwY/lBDQ==","signature_status":"signed_v1","signed_at":"2026-05-20T00:04:19.400938Z","signed_message":"canonical_sha256_bytes"},"source_id":"2512.19134","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:75846c5aec50f683afadeab739ab97313370fd29a263ca409607f6e8d2dddde0","sha256:7bfa0fbbd222905f4bac1e627a092bdf06e7c4bc7004cd5c5a57762646115504"],"state_sha256":"10ff843bec2b3e8bf191f7e225cfc13fb3a1e78a7b2057484416a6d033168c8b"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Fe8Uc8VL5mgselelxaPwh/7of7HVGNwdEvS3LFJxObbouD+KaOHB0uN67DSFXKTOgcOGN5j+7dRAJMYeeiBwBA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-30T13:54:37.537733Z","bundle_sha256":"80e650fe47fa94d91d2b355f0c723035b1e20319e07b62a94685a0ef61bf1c29"}}