{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2024:X63TVNMRK6JX7IKIO2RV4P7CSM","short_pith_number":"pith:X63TVNMR","canonical_record":{"source":{"id":"2410.23605","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2024-10-31T03:42:17Z","cross_cats_sorted":[],"title_canon_sha256":"0f76e07e940dfcc940945da2e8c2c6a3d5a3963497530c8447796985353557f4","abstract_canon_sha256":"078e5261b7e7a49d4477cd886a4a1962d2a1a9d2196c922bdcb26f9ab30e3af7"},"schema_version":"1.0"},"canonical_sha256":"bfb73ab59157937fa14876a35e3fe29339fc95302e883b1b9b7e41337c3d6e5c","source":{"kind":"arxiv","id":"2410.23605","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2410.23605","created_at":"2026-07-05T10:11:27Z"},{"alias_kind":"arxiv_version","alias_value":"2410.23605v2","created_at":"2026-07-05T10:11:27Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2410.23605","created_at":"2026-07-05T10:11:27Z"},{"alias_kind":"pith_short_12","alias_value":"X63TVNMRK6JX","created_at":"2026-07-05T10:11:27Z"},{"alias_kind":"pith_short_16","alias_value":"X63TVNMRK6JX7IKI","created_at":"2026-07-05T10:11:27Z"},{"alias_kind":"pith_short_8","alias_value":"X63TVNMR","created_at":"2026-07-05T10:11:27Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2024:X63TVNMRK6JX7IKIO2RV4P7CSM","target":"record","payload":{"canonical_record":{"source":{"id":"2410.23605","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2024-10-31T03:42:17Z","cross_cats_sorted":[],"title_canon_sha256":"0f76e07e940dfcc940945da2e8c2c6a3d5a3963497530c8447796985353557f4","abstract_canon_sha256":"078e5261b7e7a49d4477cd886a4a1962d2a1a9d2196c922bdcb26f9ab30e3af7"},"schema_version":"1.0"},"canonical_sha256":"bfb73ab59157937fa14876a35e3fe29339fc95302e883b1b9b7e41337c3d6e5c","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:11:27.974212Z","signature_b64":"+ZEjcfKwLUI9ng3Z5m6CrjMfBcyRBvvBzG84PB6bgXQqg1l65nK98EEQ8vVs9NfeS/h12fS2WLwzgQ15hSyZBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"bfb73ab59157937fa14876a35e3fe29339fc95302e883b1b9b7e41337c3d6e5c","last_reissued_at":"2026-07-05T10:11:27.973661Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:11:27.973661Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2410.23605","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":"2026-07-05T10:11:27Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"yKg5QrDiem1Rh3u1PoO8vImHOM7OHLJ47DCNcQKRhyAwOhyJVV7nLvCfgmodEXRqRSrY9UOsM161w13rAmOeAA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-09T05:11:27.594432Z"},"content_sha256":"06afd1679f0affaf823197d5fa2f6d9fb2fd1ec3eb71438672bb9be09303c251","schema_version":"1.0","event_id":"sha256:06afd1679f0affaf823197d5fa2f6d9fb2fd1ec3eb71438672bb9be09303c251"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2024:X63TVNMRK6JX7IKIO2RV4P7CSM","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Dynamic Uncertainty Ranking: Enhancing Retrieval-Augmented In-Context Learning for Long-Tail Knowledge in LLMs","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Cao Xiao, Jiayu Zhou, Parminder Bhatia, Runxue Bao, Shuyang Yu, Taha Kass-hout","submitted_at":"2024-10-31T03:42:17Z","abstract_excerpt":"Large language models (LLMs) can learn vast amounts of knowledge from diverse domains during pre-training. However, long-tail knowledge from specialized domains is often scarce and underrepresented, rarely appearing in the models' memorization. Prior work has shown that in-context learning (ICL) with retriever augmentation can help LLMs better capture long-tail knowledge, reducing their reliance on pre-trained data. Despite these advances, we observe that LLM predictions for long-tail questions remain uncertain to variations in retrieved samples. To take advantage of the uncertainty in ICL for"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2410.23605","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/2410.23605/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":"2026-07-05T10:11:27Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"BMgkVD1tNkKEDvwR1wLRX0f5/N4aJ8ZsHt9VaZUopFjksxcioQ+zcAi0nA37kYTuvde8tlwaeAjAQbPCOV+NBg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-09T05:11:27.595003Z"},"content_sha256":"61e624aea3b5e953dc24dee540c732854018ebd6941808d50da1fa6075b46acc","schema_version":"1.0","event_id":"sha256:61e624aea3b5e953dc24dee540c732854018ebd6941808d50da1fa6075b46acc"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/X63TVNMRK6JX7IKIO2RV4P7CSM/bundle.json","state_url":"https://pith.science/pith/X63TVNMRK6JX7IKIO2RV4P7CSM/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/X63TVNMRK6JX7IKIO2RV4P7CSM/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":"2026-08-09T05:11:27Z","links":{"resolver":"https://pith.science/pith/X63TVNMRK6JX7IKIO2RV4P7CSM","bundle":"https://pith.science/pith/X63TVNMRK6JX7IKIO2RV4P7CSM/bundle.json","state":"https://pith.science/pith/X63TVNMRK6JX7IKIO2RV4P7CSM/state.json","well_known_bundle":"https://pith.science/.well-known/pith/X63TVNMRK6JX7IKIO2RV4P7CSM/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:X63TVNMRK6JX7IKIO2RV4P7CSM","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":"078e5261b7e7a49d4477cd886a4a1962d2a1a9d2196c922bdcb26f9ab30e3af7","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2024-10-31T03:42:17Z","title_canon_sha256":"0f76e07e940dfcc940945da2e8c2c6a3d5a3963497530c8447796985353557f4"},"schema_version":"1.0","source":{"id":"2410.23605","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2410.23605","created_at":"2026-07-05T10:11:27Z"},{"alias_kind":"arxiv_version","alias_value":"2410.23605v2","created_at":"2026-07-05T10:11:27Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2410.23605","created_at":"2026-07-05T10:11:27Z"},{"alias_kind":"pith_short_12","alias_value":"X63TVNMRK6JX","created_at":"2026-07-05T10:11:27Z"},{"alias_kind":"pith_short_16","alias_value":"X63TVNMRK6JX7IKI","created_at":"2026-07-05T10:11:27Z"},{"alias_kind":"pith_short_8","alias_value":"X63TVNMR","created_at":"2026-07-05T10:11:27Z"}],"graph_snapshots":[{"event_id":"sha256:61e624aea3b5e953dc24dee540c732854018ebd6941808d50da1fa6075b46acc","target":"graph","created_at":"2026-07-05T10:11:27Z","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/2410.23605/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Large language models (LLMs) can learn vast amounts of knowledge from diverse domains during pre-training. However, long-tail knowledge from specialized domains is often scarce and underrepresented, rarely appearing in the models' memorization. Prior work has shown that in-context learning (ICL) with retriever augmentation can help LLMs better capture long-tail knowledge, reducing their reliance on pre-trained data. Despite these advances, we observe that LLM predictions for long-tail questions remain uncertain to variations in retrieved samples. To take advantage of the uncertainty in ICL for","authors_text":"Cao Xiao, Jiayu Zhou, Parminder Bhatia, Runxue Bao, Shuyang Yu, Taha Kass-hout","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2024-10-31T03:42:17Z","title":"Dynamic Uncertainty Ranking: Enhancing Retrieval-Augmented In-Context Learning for Long-Tail Knowledge in LLMs"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2410.23605","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":"sha256:06afd1679f0affaf823197d5fa2f6d9fb2fd1ec3eb71438672bb9be09303c251","target":"record","created_at":"2026-07-05T10:11:27Z","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":"078e5261b7e7a49d4477cd886a4a1962d2a1a9d2196c922bdcb26f9ab30e3af7","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2024-10-31T03:42:17Z","title_canon_sha256":"0f76e07e940dfcc940945da2e8c2c6a3d5a3963497530c8447796985353557f4"},"schema_version":"1.0","source":{"id":"2410.23605","kind":"arxiv","version":2}},"canonical_sha256":"bfb73ab59157937fa14876a35e3fe29339fc95302e883b1b9b7e41337c3d6e5c","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"bfb73ab59157937fa14876a35e3fe29339fc95302e883b1b9b7e41337c3d6e5c","first_computed_at":"2026-07-05T10:11:27.973661Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T10:11:27.973661Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"+ZEjcfKwLUI9ng3Z5m6CrjMfBcyRBvvBzG84PB6bgXQqg1l65nK98EEQ8vVs9NfeS/h12fS2WLwzgQ15hSyZBg==","signature_status":"signed_v1","signed_at":"2026-07-05T10:11:27.974212Z","signed_message":"canonical_sha256_bytes"},"source_id":"2410.23605","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:06afd1679f0affaf823197d5fa2f6d9fb2fd1ec3eb71438672bb9be09303c251","sha256:61e624aea3b5e953dc24dee540c732854018ebd6941808d50da1fa6075b46acc"],"state_sha256":"f85a2cfd4a72e1d9f468b21cdfdb3369a4f2ac4b14e7286002d1a3587d1dd610"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Az5uPstuyxDgX2ohIhCpWju0EPypKgpd801rdKyvpWbELkAc1r5xwMCrTpkfm+hr9R6M6y7tOp295289PQbeBw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-09T05:11:27.600116Z","bundle_sha256":"38d9748d6a2ede8671fca1ac9bdc555159d2e7f0cf50844915bdc26134f67f82"}}