{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2024:RZANHGXRL2O4E6JTYPDVGLPU76","short_pith_number":"pith:RZANHGXR","canonical_record":{"source":{"id":"2407.01796","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.CL","submitted_at":"2024-07-01T20:47:47Z","cross_cats_sorted":[],"title_canon_sha256":"436ff8f0ac5e32a8616ea7e3253886fd26193f9b386c6e91adf41b18f455cbf7","abstract_canon_sha256":"17d1c337d4a3d1219689e74bcdafbeb01dd3bb00e68afd32d5fcb29ce0e15b6c"},"schema_version":"1.0"},"canonical_sha256":"8e40d39af15e9dc27933c3c7532df4ff9ae9fe7d311996ee4189281130861bb0","source":{"kind":"arxiv","id":"2407.01796","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2407.01796","created_at":"2026-07-05T11:07:58Z"},{"alias_kind":"arxiv_version","alias_value":"2407.01796v2","created_at":"2026-07-05T11:07:58Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2407.01796","created_at":"2026-07-05T11:07:58Z"},{"alias_kind":"pith_short_12","alias_value":"RZANHGXRL2O4","created_at":"2026-07-05T11:07:58Z"},{"alias_kind":"pith_short_16","alias_value":"RZANHGXRL2O4E6JT","created_at":"2026-07-05T11:07:58Z"},{"alias_kind":"pith_short_8","alias_value":"RZANHGXR","created_at":"2026-07-05T11:07:58Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2024:RZANHGXRL2O4E6JTYPDVGLPU76","target":"record","payload":{"canonical_record":{"source":{"id":"2407.01796","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.CL","submitted_at":"2024-07-01T20:47:47Z","cross_cats_sorted":[],"title_canon_sha256":"436ff8f0ac5e32a8616ea7e3253886fd26193f9b386c6e91adf41b18f455cbf7","abstract_canon_sha256":"17d1c337d4a3d1219689e74bcdafbeb01dd3bb00e68afd32d5fcb29ce0e15b6c"},"schema_version":"1.0"},"canonical_sha256":"8e40d39af15e9dc27933c3c7532df4ff9ae9fe7d311996ee4189281130861bb0","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:07:58.465887Z","signature_b64":"TNp3uGx/ozb71YoA5oAOFRUth2bnlJx5mXd6DwymIy7hIvyAr1Bib+aM4ZXBx8cd+lOUtnIc3XrdwWtb3Q7oAQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"8e40d39af15e9dc27933c3c7532df4ff9ae9fe7d311996ee4189281130861bb0","last_reissued_at":"2026-07-05T11:07:58.465339Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:07:58.465339Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2407.01796","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":"2026-07-05T11:07:58Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"FxLor6IYa//sgRBE8BGMooKEMloYOs5IxUvgaRHlHXhG+LCWOpaJg0TPj9BPckh3BDxtkhJsTNXktwRLCGjABg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-08T04:03:45.024063Z"},"content_sha256":"0958ed43009d2b8bb4ff8286c35a35385800380198d2c5359a5c2844f2d4c856","schema_version":"1.0","event_id":"sha256:0958ed43009d2b8bb4ff8286c35a35385800380198d2c5359a5c2844f2d4c856"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2024:RZANHGXRL2O4E6JTYPDVGLPU76","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Ground Every Sentence: Improving Retrieval-Augmented LLMs with Interleaved Reference-Claim Generation","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Fei Yu, Jiaji Deng, Jiaqing Liang, Sirui Xia, Weikang Zhou, Xintao Wang, Yanghua Xiao, Yifei Zhang","submitted_at":"2024-07-01T20:47:47Z","abstract_excerpt":"Retrieval-Augmented Generation (RAG) has been widely adopted to enhance Large Language Models (LLMs) in knowledge-intensive tasks. To enhance credibility and verifiability in RAG systems, Attributed Text Generation (ATG) is proposed, which provides citations to retrieval knowledge in LLM-generated responses. Prior methods mainly adopt coarse-grained attributions, with passage-level or paragraph-level references or citations, which fall short in verifiability. This paper proposes ReClaim (Refer & Claim), a fine-grained ATG method that alternates the generation of references and answers step by "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2407.01796","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/2407.01796/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":"2026-07-05T11:07:58Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"CiYfd+8lorfgxfM73H+O2GtZx6qtriJ8Oa8U9UCpT8dRLAp1TiowkSx7B9Vp2ySjViJt2YKBIa0ZsWAtjPUdBQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-08T04:03:45.024614Z"},"content_sha256":"f87545fbc20c611c4a2998bbb75b8810b48d0ffe936dd3ef79c0e6f194c5cc06","schema_version":"1.0","event_id":"sha256:f87545fbc20c611c4a2998bbb75b8810b48d0ffe936dd3ef79c0e6f194c5cc06"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/RZANHGXRL2O4E6JTYPDVGLPU76/bundle.json","state_url":"https://pith.science/pith/RZANHGXRL2O4E6JTYPDVGLPU76/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/RZANHGXRL2O4E6JTYPDVGLPU76/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":"2026-08-08T04:03:45Z","links":{"resolver":"https://pith.science/pith/RZANHGXRL2O4E6JTYPDVGLPU76","bundle":"https://pith.science/pith/RZANHGXRL2O4E6JTYPDVGLPU76/bundle.json","state":"https://pith.science/pith/RZANHGXRL2O4E6JTYPDVGLPU76/state.json","well_known_bundle":"https://pith.science/.well-known/pith/RZANHGXRL2O4E6JTYPDVGLPU76/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:RZANHGXRL2O4E6JTYPDVGLPU76","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":"17d1c337d4a3d1219689e74bcdafbeb01dd3bb00e68afd32d5fcb29ce0e15b6c","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.CL","submitted_at":"2024-07-01T20:47:47Z","title_canon_sha256":"436ff8f0ac5e32a8616ea7e3253886fd26193f9b386c6e91adf41b18f455cbf7"},"schema_version":"1.0","source":{"id":"2407.01796","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2407.01796","created_at":"2026-07-05T11:07:58Z"},{"alias_kind":"arxiv_version","alias_value":"2407.01796v2","created_at":"2026-07-05T11:07:58Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2407.01796","created_at":"2026-07-05T11:07:58Z"},{"alias_kind":"pith_short_12","alias_value":"RZANHGXRL2O4","created_at":"2026-07-05T11:07:58Z"},{"alias_kind":"pith_short_16","alias_value":"RZANHGXRL2O4E6JT","created_at":"2026-07-05T11:07:58Z"},{"alias_kind":"pith_short_8","alias_value":"RZANHGXR","created_at":"2026-07-05T11:07:58Z"}],"graph_snapshots":[{"event_id":"sha256:f87545fbc20c611c4a2998bbb75b8810b48d0ffe936dd3ef79c0e6f194c5cc06","target":"graph","created_at":"2026-07-05T11:07:58Z","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/2407.01796/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Retrieval-Augmented Generation (RAG) has been widely adopted to enhance Large Language Models (LLMs) in knowledge-intensive tasks. To enhance credibility and verifiability in RAG systems, Attributed Text Generation (ATG) is proposed, which provides citations to retrieval knowledge in LLM-generated responses. Prior methods mainly adopt coarse-grained attributions, with passage-level or paragraph-level references or citations, which fall short in verifiability. This paper proposes ReClaim (Refer & Claim), a fine-grained ATG method that alternates the generation of references and answers step by ","authors_text":"Fei Yu, Jiaji Deng, Jiaqing Liang, Sirui Xia, Weikang Zhou, Xintao Wang, Yanghua Xiao, Yifei Zhang","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.CL","submitted_at":"2024-07-01T20:47:47Z","title":"Ground Every Sentence: Improving Retrieval-Augmented LLMs with Interleaved Reference-Claim Generation"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2407.01796","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":"sha256:0958ed43009d2b8bb4ff8286c35a35385800380198d2c5359a5c2844f2d4c856","target":"record","created_at":"2026-07-05T11:07:58Z","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":"17d1c337d4a3d1219689e74bcdafbeb01dd3bb00e68afd32d5fcb29ce0e15b6c","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.CL","submitted_at":"2024-07-01T20:47:47Z","title_canon_sha256":"436ff8f0ac5e32a8616ea7e3253886fd26193f9b386c6e91adf41b18f455cbf7"},"schema_version":"1.0","source":{"id":"2407.01796","kind":"arxiv","version":2}},"canonical_sha256":"8e40d39af15e9dc27933c3c7532df4ff9ae9fe7d311996ee4189281130861bb0","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"8e40d39af15e9dc27933c3c7532df4ff9ae9fe7d311996ee4189281130861bb0","first_computed_at":"2026-07-05T11:07:58.465339Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T11:07:58.465339Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"TNp3uGx/ozb71YoA5oAOFRUth2bnlJx5mXd6DwymIy7hIvyAr1Bib+aM4ZXBx8cd+lOUtnIc3XrdwWtb3Q7oAQ==","signature_status":"signed_v1","signed_at":"2026-07-05T11:07:58.465887Z","signed_message":"canonical_sha256_bytes"},"source_id":"2407.01796","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:0958ed43009d2b8bb4ff8286c35a35385800380198d2c5359a5c2844f2d4c856","sha256:f87545fbc20c611c4a2998bbb75b8810b48d0ffe936dd3ef79c0e6f194c5cc06"],"state_sha256":"544b56f1258bececc29fad4474507f80d40b9b8cd239e5e0899054366eb1987d"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"igU2doEs8CSj51AtI/DfSNldQesuTjJrIeu6A4JH1xsOfaRSITrsgs4C2D69mD3Sm/jOQ7HBpRr0Dz1AkrpTDw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-08T04:03:45.029454Z","bundle_sha256":"299a2d5ad3eac781ace6c6f4ac9e1bbd2b9e5d5f500041fce8cf227ac1f97bcd"}}