{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2024:C5M7DAQH5JJWX4VEQ5JUZO6WNI","short_pith_number":"pith:C5M7DAQH","canonical_record":{"source":{"id":"2405.02732","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2024-05-04T18:32:08Z","cross_cats_sorted":["cs.IR"],"title_canon_sha256":"0caf258358912d72c5792610f9e16741b3d2ec5614974f89b3e96c20e40141a5","abstract_canon_sha256":"063cd0e3969d72cfe550b037c162bcc5257282175174fe502c080bd5f7cb0175"},"schema_version":"1.0"},"canonical_sha256":"1759f18207ea536bf2a487534cbbd66a33d942d1ad611dc321e763fe60d9ba23","source":{"kind":"arxiv","id":"2405.02732","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2405.02732","created_at":"2026-07-05T10:34:37Z"},{"alias_kind":"arxiv_version","alias_value":"2405.02732v2","created_at":"2026-07-05T10:34:37Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2405.02732","created_at":"2026-07-05T10:34:37Z"},{"alias_kind":"pith_short_12","alias_value":"C5M7DAQH5JJW","created_at":"2026-07-05T10:34:37Z"},{"alias_kind":"pith_short_16","alias_value":"C5M7DAQH5JJWX4VE","created_at":"2026-07-05T10:34:37Z"},{"alias_kind":"pith_short_8","alias_value":"C5M7DAQH","created_at":"2026-07-05T10:34:37Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2024:C5M7DAQH5JJWX4VEQ5JUZO6WNI","target":"record","payload":{"canonical_record":{"source":{"id":"2405.02732","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2024-05-04T18:32:08Z","cross_cats_sorted":["cs.IR"],"title_canon_sha256":"0caf258358912d72c5792610f9e16741b3d2ec5614974f89b3e96c20e40141a5","abstract_canon_sha256":"063cd0e3969d72cfe550b037c162bcc5257282175174fe502c080bd5f7cb0175"},"schema_version":"1.0"},"canonical_sha256":"1759f18207ea536bf2a487534cbbd66a33d942d1ad611dc321e763fe60d9ba23","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:34:37.762984Z","signature_b64":"dZaUvgEcdy4NbgN3kbh57FOttJ1dupDiLO8V25Q7DKurDmKqoZ2l4IHRLq9mqAPZYT5eniFDK+4A5jbgxEp1Ag==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"1759f18207ea536bf2a487534cbbd66a33d942d1ad611dc321e763fe60d9ba23","last_reissued_at":"2026-07-05T10:34:37.762370Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:34:37.762370Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2405.02732","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":"2026-07-05T10:34:37Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"CnmMeenPWOGQXhxxs/hr5RHp3fo3zO3rEaOKlJgMmnc0Bx1Kj6jD44ng37zZOikppvnpG9jfL5kYoTkdIPOqBQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-21T19:27:18.526195Z"},"content_sha256":"868bf20addc57a4f9a923c137a28582979596fbc6005aca0c97a854f1a204796","schema_version":"1.0","event_id":"sha256:868bf20addc57a4f9a923c137a28582979596fbc6005aca0c97a854f1a204796"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2024:C5M7DAQH5JJWX4VEQ5JUZO6WNI","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Recall Them All: Retrieval-Augmented Language Models for Long Object List Extraction from Long Documents","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.IR"],"primary_cat":"cs.CL","authors_text":"Gerhard Weikum, Simon Razniewski, Sneha Singhania","submitted_at":"2024-05-04T18:32:08Z","abstract_excerpt":"Methods for relation extraction from text mostly focus on high precision, at the cost of limited recall. High recall is crucial, though, to populate long lists of object entities that stand in a specific relation with a given subject. Cues for relevant objects can be spread across many passages in long texts. This poses the challenge of extracting long lists from long texts. We present the L3X method which tackles the problem in two stages: (1) recall-oriented generation using a large language model (LLM) with judicious techniques for retrieval augmentation, and (2) precision-oriented scrutini"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2405.02732","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/2405.02732/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":"2026-07-05T10:34:37Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"GftgAUwWejd/0HczeQgms+xF2eMfibsm/vWzQMSaqxwoZeC03vPDZNtiCODzJ1GPupb9QkeTP8RMPBJFgBVmCQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-21T19:27:18.526572Z"},"content_sha256":"4161ec3a1294759ec0ec22df9a484fa7ecf54c2addbbcf8c663e8f017a8c85f5","schema_version":"1.0","event_id":"sha256:4161ec3a1294759ec0ec22df9a484fa7ecf54c2addbbcf8c663e8f017a8c85f5"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/C5M7DAQH5JJWX4VEQ5JUZO6WNI/bundle.json","state_url":"https://pith.science/pith/C5M7DAQH5JJWX4VEQ5JUZO6WNI/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/C5M7DAQH5JJWX4VEQ5JUZO6WNI/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":"2026-08-21T19:27:18Z","links":{"resolver":"https://pith.science/pith/C5M7DAQH5JJWX4VEQ5JUZO6WNI","bundle":"https://pith.science/pith/C5M7DAQH5JJWX4VEQ5JUZO6WNI/bundle.json","state":"https://pith.science/pith/C5M7DAQH5JJWX4VEQ5JUZO6WNI/state.json","well_known_bundle":"https://pith.science/.well-known/pith/C5M7DAQH5JJWX4VEQ5JUZO6WNI/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:C5M7DAQH5JJWX4VEQ5JUZO6WNI","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":"063cd0e3969d72cfe550b037c162bcc5257282175174fe502c080bd5f7cb0175","cross_cats_sorted":["cs.IR"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2024-05-04T18:32:08Z","title_canon_sha256":"0caf258358912d72c5792610f9e16741b3d2ec5614974f89b3e96c20e40141a5"},"schema_version":"1.0","source":{"id":"2405.02732","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2405.02732","created_at":"2026-07-05T10:34:37Z"},{"alias_kind":"arxiv_version","alias_value":"2405.02732v2","created_at":"2026-07-05T10:34:37Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2405.02732","created_at":"2026-07-05T10:34:37Z"},{"alias_kind":"pith_short_12","alias_value":"C5M7DAQH5JJW","created_at":"2026-07-05T10:34:37Z"},{"alias_kind":"pith_short_16","alias_value":"C5M7DAQH5JJWX4VE","created_at":"2026-07-05T10:34:37Z"},{"alias_kind":"pith_short_8","alias_value":"C5M7DAQH","created_at":"2026-07-05T10:34:37Z"}],"graph_snapshots":[{"event_id":"sha256:4161ec3a1294759ec0ec22df9a484fa7ecf54c2addbbcf8c663e8f017a8c85f5","target":"graph","created_at":"2026-07-05T10:34:37Z","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/2405.02732/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Methods for relation extraction from text mostly focus on high precision, at the cost of limited recall. High recall is crucial, though, to populate long lists of object entities that stand in a specific relation with a given subject. Cues for relevant objects can be spread across many passages in long texts. This poses the challenge of extracting long lists from long texts. We present the L3X method which tackles the problem in two stages: (1) recall-oriented generation using a large language model (LLM) with judicious techniques for retrieval augmentation, and (2) precision-oriented scrutini","authors_text":"Gerhard Weikum, Simon Razniewski, Sneha Singhania","cross_cats":["cs.IR"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2024-05-04T18:32:08Z","title":"Recall Them All: Retrieval-Augmented Language Models for Long Object List Extraction from Long Documents"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2405.02732","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":"sha256:868bf20addc57a4f9a923c137a28582979596fbc6005aca0c97a854f1a204796","target":"record","created_at":"2026-07-05T10:34:37Z","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":"063cd0e3969d72cfe550b037c162bcc5257282175174fe502c080bd5f7cb0175","cross_cats_sorted":["cs.IR"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2024-05-04T18:32:08Z","title_canon_sha256":"0caf258358912d72c5792610f9e16741b3d2ec5614974f89b3e96c20e40141a5"},"schema_version":"1.0","source":{"id":"2405.02732","kind":"arxiv","version":2}},"canonical_sha256":"1759f18207ea536bf2a487534cbbd66a33d942d1ad611dc321e763fe60d9ba23","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"1759f18207ea536bf2a487534cbbd66a33d942d1ad611dc321e763fe60d9ba23","first_computed_at":"2026-07-05T10:34:37.762370Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T10:34:37.762370Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"dZaUvgEcdy4NbgN3kbh57FOttJ1dupDiLO8V25Q7DKurDmKqoZ2l4IHRLq9mqAPZYT5eniFDK+4A5jbgxEp1Ag==","signature_status":"signed_v1","signed_at":"2026-07-05T10:34:37.762984Z","signed_message":"canonical_sha256_bytes"},"source_id":"2405.02732","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:868bf20addc57a4f9a923c137a28582979596fbc6005aca0c97a854f1a204796","sha256:4161ec3a1294759ec0ec22df9a484fa7ecf54c2addbbcf8c663e8f017a8c85f5"],"state_sha256":"cbf4cdd3fb72a18e9c5d0b0f4b1ae00f641d7546aa19d607876bd55a445165e9"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"DeeISmsPQ2WDL64a6optIIAjLsnYAbbrbIBffSjGOGgaIei6JRJF9pc2Mig9pGMKeqp/LJBfq189EnnnfmjTDQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-21T19:27:18.529608Z","bundle_sha256":"ddbcca8fb0d137d3e3ea7ba87499f4edd6327eebd71b28ac890b8deea435c37e"}}