{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2023:PJGDHFNRIQISL5YI7QYV5VZI6T","short_pith_number":"pith:PJGDHFNR","canonical_record":{"source":{"id":"2312.07559","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2023-12-08T18:50:20Z","cross_cats_sorted":["cs.AI","cs.LG"],"title_canon_sha256":"45d07f674e15f65b5c44cbaa12ff9a6048b7369a641cd2c63a878a3af446b72b","abstract_canon_sha256":"cea0c9e1473eeee1342045196780aba1451259254ccae7950651216bf2969029"},"schema_version":"1.0"},"canonical_sha256":"7a4c3395b1441125f708fc315ed728f4e94e50cfd48268c0d567b8fd0a29058a","source":{"kind":"arxiv","id":"2312.07559","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2312.07559","created_at":"2026-07-05T07:24:21Z"},{"alias_kind":"arxiv_version","alias_value":"2312.07559v2","created_at":"2026-07-05T07:24:21Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2312.07559","created_at":"2026-07-05T07:24:21Z"},{"alias_kind":"pith_short_12","alias_value":"PJGDHFNRIQIS","created_at":"2026-07-05T07:24:21Z"},{"alias_kind":"pith_short_16","alias_value":"PJGDHFNRIQISL5YI","created_at":"2026-07-05T07:24:21Z"},{"alias_kind":"pith_short_8","alias_value":"PJGDHFNR","created_at":"2026-07-05T07:24:21Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2023:PJGDHFNRIQISL5YI7QYV5VZI6T","target":"record","payload":{"canonical_record":{"source":{"id":"2312.07559","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2023-12-08T18:50:20Z","cross_cats_sorted":["cs.AI","cs.LG"],"title_canon_sha256":"45d07f674e15f65b5c44cbaa12ff9a6048b7369a641cd2c63a878a3af446b72b","abstract_canon_sha256":"cea0c9e1473eeee1342045196780aba1451259254ccae7950651216bf2969029"},"schema_version":"1.0"},"canonical_sha256":"7a4c3395b1441125f708fc315ed728f4e94e50cfd48268c0d567b8fd0a29058a","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:24:21.970089Z","signature_b64":"ClK3Vub/F09dUXWLOXP7tSEUk/hbf2Nmn4k4jsDj0miiLPHVMoTKfKb3jf4jwDCt4yjLOHEaMriFK+TV71xYCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"7a4c3395b1441125f708fc315ed728f4e94e50cfd48268c0d567b8fd0a29058a","last_reissued_at":"2026-07-05T07:24:21.969606Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:24:21.969606Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2312.07559","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":"2026-07-05T07:24:21Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"XXbY/qjIJ7sKdlwd1ACdM9s8CByLc8wmCjB1CGDJ/ME8iaJsS+6tnYNQbnbN+AjTS6+lNL6FnYbXytrCDd7UAg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-09T13:55:36.788110Z"},"content_sha256":"67bed6b4255a047dbde5e4a6998d59d5575c6d4634187de6e83f6fb677a965eb","schema_version":"1.0","event_id":"sha256:67bed6b4255a047dbde5e4a6998d59d5575c6d4634187de6e83f6fb677a965eb"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2023:PJGDHFNRIQISL5YI7QYV5VZI6T","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"PaperQA: Retrieval-Augmented Generative Agent for Scientific Research","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.LG"],"primary_cat":"cs.CL","authors_text":"Aleksandar Shtedritski, Andrew D. White, Jakub L\\'ala, Odhran O'Donoghue, Sam Cox, Samuel G. Rodriques","submitted_at":"2023-12-08T18:50:20Z","abstract_excerpt":"Large Language Models (LLMs) generalize well across language tasks, but suffer from hallucinations and uninterpretability, making it difficult to assess their accuracy without ground-truth. Retrieval-Augmented Generation (RAG) models have been proposed to reduce hallucinations and provide provenance for how an answer was generated. Applying such models to the scientific literature may enable large-scale, systematic processing of scientific knowledge. We present PaperQA, a RAG agent for answering questions over the scientific literature. PaperQA is an agent that performs information retrieval a"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2312.07559","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/2312.07559/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":"2026-07-05T07:24:21Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Vq4XwI/tnjHFHKa0rbSyqHS+Y/BPf7sxNSbf6nH2kxVQzxTEFBVI5XMfwmDW80QF405R/2clqp8mSL2hDQtQBw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-09T13:55:36.788704Z"},"content_sha256":"a4a7520c83d43a2956b1867d93d966f5d48b1a97cd4cb559e4e2fca52cd3c5db","schema_version":"1.0","event_id":"sha256:a4a7520c83d43a2956b1867d93d966f5d48b1a97cd4cb559e4e2fca52cd3c5db"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/PJGDHFNRIQISL5YI7QYV5VZI6T/bundle.json","state_url":"https://pith.science/pith/PJGDHFNRIQISL5YI7QYV5VZI6T/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/PJGDHFNRIQISL5YI7QYV5VZI6T/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":"2026-08-09T13:55:36Z","links":{"resolver":"https://pith.science/pith/PJGDHFNRIQISL5YI7QYV5VZI6T","bundle":"https://pith.science/pith/PJGDHFNRIQISL5YI7QYV5VZI6T/bundle.json","state":"https://pith.science/pith/PJGDHFNRIQISL5YI7QYV5VZI6T/state.json","well_known_bundle":"https://pith.science/.well-known/pith/PJGDHFNRIQISL5YI7QYV5VZI6T/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2023:PJGDHFNRIQISL5YI7QYV5VZI6T","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":"cea0c9e1473eeee1342045196780aba1451259254ccae7950651216bf2969029","cross_cats_sorted":["cs.AI","cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2023-12-08T18:50:20Z","title_canon_sha256":"45d07f674e15f65b5c44cbaa12ff9a6048b7369a641cd2c63a878a3af446b72b"},"schema_version":"1.0","source":{"id":"2312.07559","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2312.07559","created_at":"2026-07-05T07:24:21Z"},{"alias_kind":"arxiv_version","alias_value":"2312.07559v2","created_at":"2026-07-05T07:24:21Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2312.07559","created_at":"2026-07-05T07:24:21Z"},{"alias_kind":"pith_short_12","alias_value":"PJGDHFNRIQIS","created_at":"2026-07-05T07:24:21Z"},{"alias_kind":"pith_short_16","alias_value":"PJGDHFNRIQISL5YI","created_at":"2026-07-05T07:24:21Z"},{"alias_kind":"pith_short_8","alias_value":"PJGDHFNR","created_at":"2026-07-05T07:24:21Z"}],"graph_snapshots":[{"event_id":"sha256:a4a7520c83d43a2956b1867d93d966f5d48b1a97cd4cb559e4e2fca52cd3c5db","target":"graph","created_at":"2026-07-05T07:24:21Z","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/2312.07559/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Large Language Models (LLMs) generalize well across language tasks, but suffer from hallucinations and uninterpretability, making it difficult to assess their accuracy without ground-truth. Retrieval-Augmented Generation (RAG) models have been proposed to reduce hallucinations and provide provenance for how an answer was generated. Applying such models to the scientific literature may enable large-scale, systematic processing of scientific knowledge. We present PaperQA, a RAG agent for answering questions over the scientific literature. PaperQA is an agent that performs information retrieval a","authors_text":"Aleksandar Shtedritski, Andrew D. White, Jakub L\\'ala, Odhran O'Donoghue, Sam Cox, Samuel G. Rodriques","cross_cats":["cs.AI","cs.LG"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2023-12-08T18:50:20Z","title":"PaperQA: Retrieval-Augmented Generative Agent for Scientific Research"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2312.07559","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":"sha256:67bed6b4255a047dbde5e4a6998d59d5575c6d4634187de6e83f6fb677a965eb","target":"record","created_at":"2026-07-05T07:24:21Z","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":"cea0c9e1473eeee1342045196780aba1451259254ccae7950651216bf2969029","cross_cats_sorted":["cs.AI","cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2023-12-08T18:50:20Z","title_canon_sha256":"45d07f674e15f65b5c44cbaa12ff9a6048b7369a641cd2c63a878a3af446b72b"},"schema_version":"1.0","source":{"id":"2312.07559","kind":"arxiv","version":2}},"canonical_sha256":"7a4c3395b1441125f708fc315ed728f4e94e50cfd48268c0d567b8fd0a29058a","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"7a4c3395b1441125f708fc315ed728f4e94e50cfd48268c0d567b8fd0a29058a","first_computed_at":"2026-07-05T07:24:21.969606Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T07:24:21.969606Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"ClK3Vub/F09dUXWLOXP7tSEUk/hbf2Nmn4k4jsDj0miiLPHVMoTKfKb3jf4jwDCt4yjLOHEaMriFK+TV71xYCg==","signature_status":"signed_v1","signed_at":"2026-07-05T07:24:21.970089Z","signed_message":"canonical_sha256_bytes"},"source_id":"2312.07559","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:67bed6b4255a047dbde5e4a6998d59d5575c6d4634187de6e83f6fb677a965eb","sha256:a4a7520c83d43a2956b1867d93d966f5d48b1a97cd4cb559e4e2fca52cd3c5db"],"state_sha256":"540d37ad9cd5649143a698d480ee0fe4419d2ba84e8489ae253076623b7e0e3d"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"DF4cxq5XCPvrLVJ6W4EE9exccjEfXktPIV8U2jJSWeKkrDuHvXcziHF+u68pXBOHRjLRWPg+T7nMRnsT7wieBw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-09T13:55:36.793783Z","bundle_sha256":"5d2f2136f8db548afb0dd9c02335137836aff5757bc8fd78a5313df89a32523d"}}