{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:5EB2NKTFZIHQMVYU6PTRBVIFZT","short_pith_number":"pith:5EB2NKTF","schema_version":"1.0","canonical_sha256":"e903a6aa65ca0f065714f3e710d505ccd8d7f10c6f84f11e17717b236e17ce7d","source":{"kind":"arxiv","id":"2406.19215","version":1},"attestation_state":"computed","paper":{"title":"SeaKR: Self-aware Knowledge Retrieval for Adaptive Retrieval Augmented Generation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Juanzi Li, Lei Hou, Liangming Pan, Linmei Hu, Shulin Cao, Weichuan Liu, Weijian Qi, Zijun Yao","submitted_at":"2024-06-27T14:38:33Z","abstract_excerpt":"This paper introduces Self-aware Knowledge Retrieval (SeaKR), a novel adaptive RAG model that extracts self-aware uncertainty of LLMs from their internal states. SeaKR activates retrieval when the LLMs present high self-aware uncertainty for generation. To effectively integrate retrieved knowledge snippets, SeaKR re-ranks them based on LLM's self-aware uncertainty to preserve the snippet that reduces their uncertainty to the utmost. To facilitate solving complex tasks that require multiple retrievals, SeaKR utilizes their self-aware uncertainty to choose among different reasoning strategies. O"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2406.19215","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2024-06-27T14:38:33Z","cross_cats_sorted":[],"title_canon_sha256":"cc5908e1ee1a36d2bfdf1ad380143daae5a71bd22e60cc5c3f3fd8cc175b74d0","abstract_canon_sha256":"ddbeca72bcfae0a7d8ac57b8f61f1ce4a63ca4f07126f8584574d10740cc2a39"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:37:31.914497Z","signature_b64":"9gd6FtKHTscPu6LHNI3g6EOLPKcmv4tSx3DW2DkGOwG1Vfs5nlrznrC/hmdUZPBxpGt8PA+XTekvrhEWglWvBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"e903a6aa65ca0f065714f3e710d505ccd8d7f10c6f84f11e17717b236e17ce7d","last_reissued_at":"2026-07-05T08:37:31.913993Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:37:31.913993Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"SeaKR: Self-aware Knowledge Retrieval for Adaptive Retrieval Augmented Generation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Juanzi Li, Lei Hou, Liangming Pan, Linmei Hu, Shulin Cao, Weichuan Liu, Weijian Qi, Zijun Yao","submitted_at":"2024-06-27T14:38:33Z","abstract_excerpt":"This paper introduces Self-aware Knowledge Retrieval (SeaKR), a novel adaptive RAG model that extracts self-aware uncertainty of LLMs from their internal states. SeaKR activates retrieval when the LLMs present high self-aware uncertainty for generation. To effectively integrate retrieved knowledge snippets, SeaKR re-ranks them based on LLM's self-aware uncertainty to preserve the snippet that reduces their uncertainty to the utmost. To facilitate solving complex tasks that require multiple retrievals, SeaKR utilizes their self-aware uncertainty to choose among different reasoning strategies. O"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2406.19215","kind":"arxiv","version":1},"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/2406.19215/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"},"aliases":[{"alias_kind":"arxiv","alias_value":"2406.19215","created_at":"2026-07-05T08:37:31.914046+00:00"},{"alias_kind":"arxiv_version","alias_value":"2406.19215v1","created_at":"2026-07-05T08:37:31.914046+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2406.19215","created_at":"2026-07-05T08:37:31.914046+00:00"},{"alias_kind":"pith_short_12","alias_value":"5EB2NKTFZIHQ","created_at":"2026-07-05T08:37:31.914046+00:00"},{"alias_kind":"pith_short_16","alias_value":"5EB2NKTFZIHQMVYU","created_at":"2026-07-05T08:37:31.914046+00:00"},{"alias_kind":"pith_short_8","alias_value":"5EB2NKTF","created_at":"2026-07-05T08:37:31.914046+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":6,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.07299","citing_title":"DuMate-DeepResearch: An Auditable Multi-Agent System with Recursive Search and Rubric-Grounded Reasoning","ref_index":22,"is_internal_anchor":false},{"citing_arxiv_id":"2606.05054","citing_title":"Boosting Self-Consistency with Ranking","ref_index":189,"is_internal_anchor":false},{"citing_arxiv_id":"2605.18767","citing_title":"DualView: Adaptive Local-Global Fusion for Multi-Hop Document Reranking","ref_index":38,"is_internal_anchor":false},{"citing_arxiv_id":"2605.18792","citing_title":"Trust or Abstain? A Self-Aware RAG Approach","ref_index":33,"is_internal_anchor":false},{"citing_arxiv_id":"2511.09803","citing_title":"Retrieval as a Decision: Training-Free Adaptive Gating for Efficient RAG","ref_index":17,"is_internal_anchor":false},{"citing_arxiv_id":"2604.01413","citing_title":"Adaptive Stopping for Multi-Turn LLM Reasoning","ref_index":33,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/5EB2NKTFZIHQMVYU6PTRBVIFZT","json":"https://pith.science/pith/5EB2NKTFZIHQMVYU6PTRBVIFZT.json","graph_json":"https://pith.science/api/pith-number/5EB2NKTFZIHQMVYU6PTRBVIFZT/graph.json","events_json":"https://pith.science/api/pith-number/5EB2NKTFZIHQMVYU6PTRBVIFZT/events.json","paper":"https://pith.science/paper/5EB2NKTF"},"agent_actions":{"view_html":"https://pith.science/pith/5EB2NKTFZIHQMVYU6PTRBVIFZT","download_json":"https://pith.science/pith/5EB2NKTFZIHQMVYU6PTRBVIFZT.json","view_paper":"https://pith.science/paper/5EB2NKTF","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2406.19215&json=true","fetch_graph":"https://pith.science/api/pith-number/5EB2NKTFZIHQMVYU6PTRBVIFZT/graph.json","fetch_events":"https://pith.science/api/pith-number/5EB2NKTFZIHQMVYU6PTRBVIFZT/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/5EB2NKTFZIHQMVYU6PTRBVIFZT/action/timestamp_anchor","attest_storage":"https://pith.science/pith/5EB2NKTFZIHQMVYU6PTRBVIFZT/action/storage_attestation","attest_author":"https://pith.science/pith/5EB2NKTFZIHQMVYU6PTRBVIFZT/action/author_attestation","sign_citation":"https://pith.science/pith/5EB2NKTFZIHQMVYU6PTRBVIFZT/action/citation_signature","submit_replication":"https://pith.science/pith/5EB2NKTFZIHQMVYU6PTRBVIFZT/action/replication_record"}},"created_at":"2026-07-05T08:37:31.914046+00:00","updated_at":"2026-07-05T08:37:31.914046+00:00"}