{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2023:7NNBGV6XDD7BKQ6M2JEPJYV5DC","short_pith_number":"pith:7NNBGV6X","canonical_record":{"source":{"id":"2311.12233","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2023-11-20T23:17:20Z","cross_cats_sorted":[],"title_canon_sha256":"bba6ca2f83763f692811a4a1eb7ba526a015cc79c7c1bf3cfed2491db1dda0bf","abstract_canon_sha256":"07905f0bef59d3628ee064f863f6ba2ad13f5879432f008b312a4d81d65e5926"},"schema_version":"1.0"},"canonical_sha256":"fb5a1357d718fe1543ccd248f4e2bd188ce3cfa224a28f1ef748e28bb0634de8","source":{"kind":"arxiv","id":"2311.12233","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2311.12233","created_at":"2026-07-05T07:15:02Z"},{"alias_kind":"arxiv_version","alias_value":"2311.12233v1","created_at":"2026-07-05T07:15:02Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2311.12233","created_at":"2026-07-05T07:15:02Z"},{"alias_kind":"pith_short_12","alias_value":"7NNBGV6XDD7B","created_at":"2026-07-05T07:15:02Z"},{"alias_kind":"pith_short_16","alias_value":"7NNBGV6XDD7BKQ6M","created_at":"2026-07-05T07:15:02Z"},{"alias_kind":"pith_short_8","alias_value":"7NNBGV6X","created_at":"2026-07-05T07:15:02Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2023:7NNBGV6XDD7BKQ6M2JEPJYV5DC","target":"record","payload":{"canonical_record":{"source":{"id":"2311.12233","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2023-11-20T23:17:20Z","cross_cats_sorted":[],"title_canon_sha256":"bba6ca2f83763f692811a4a1eb7ba526a015cc79c7c1bf3cfed2491db1dda0bf","abstract_canon_sha256":"07905f0bef59d3628ee064f863f6ba2ad13f5879432f008b312a4d81d65e5926"},"schema_version":"1.0"},"canonical_sha256":"fb5a1357d718fe1543ccd248f4e2bd188ce3cfa224a28f1ef748e28bb0634de8","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:15:02.362483Z","signature_b64":"YY46xEkrn2PQILA35SZlpoZqO6JVOyf/UL/CFG4LNldSQloKa9YyFwNWtfEUCmnVZSiCrDCaBov3OF5cZ1VjBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"fb5a1357d718fe1543ccd248f4e2bd188ce3cfa224a28f1ef748e28bb0634de8","last_reissued_at":"2026-07-05T07:15:02.362075Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:15:02.362075Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2311.12233","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T07:15:02Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"YviCdUHy//BMWP7L4jzHVlvFXcI+wE4TUGBhA54y/r0j0PXteTzuql1d5PhySt2YrX97nd1xK+bcGYOT0F5lAg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-03T20:13:26.300635Z"},"content_sha256":"ce928f06f3d1c0963df1b27ed3b86a9714bea21006a51320c9f3b4f4195ad6a5","schema_version":"1.0","event_id":"sha256:ce928f06f3d1c0963df1b27ed3b86a9714bea21006a51320c9f3b4f4195ad6a5"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2023:7NNBGV6XDD7BKQ6M2JEPJYV5DC","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Unifying Corroborative and Contributive Attributions in Large Language Models","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Caleb Winston, Carlos Guestrin, Judy Hanwen Shen, Nicole Meister, Theodora Worledge","submitted_at":"2023-11-20T23:17:20Z","abstract_excerpt":"As businesses, products, and services spring up around large language models, the trustworthiness of these models hinges on the verifiability of their outputs. However, methods for explaining language model outputs largely fall across two distinct fields of study which both use the term \"attribution\" to refer to entirely separate techniques: citation generation and training data attribution. In many modern applications, such as legal document generation and medical question answering, both types of attributions are important. In this work, we argue for and present a unified framework of large "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2311.12233","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/2311.12233/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":"2026-07-05T07:15:02Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"LdYdDPHVsaBBAuCZ++QPxDUeHM/AtJ9z5hP0g3CBIJ3659yTndzqMXMxvMHBSobVNr+UKkxy/l6wEQ1GYzlKAA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-03T20:13:26.301349Z"},"content_sha256":"e99c17c0341ec66a270c6554964193fbf3a4e368f436b0d8ea3297e3a327b4bc","schema_version":"1.0","event_id":"sha256:e99c17c0341ec66a270c6554964193fbf3a4e368f436b0d8ea3297e3a327b4bc"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/7NNBGV6XDD7BKQ6M2JEPJYV5DC/bundle.json","state_url":"https://pith.science/pith/7NNBGV6XDD7BKQ6M2JEPJYV5DC/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/7NNBGV6XDD7BKQ6M2JEPJYV5DC/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":"2026-08-03T20:13:26Z","links":{"resolver":"https://pith.science/pith/7NNBGV6XDD7BKQ6M2JEPJYV5DC","bundle":"https://pith.science/pith/7NNBGV6XDD7BKQ6M2JEPJYV5DC/bundle.json","state":"https://pith.science/pith/7NNBGV6XDD7BKQ6M2JEPJYV5DC/state.json","well_known_bundle":"https://pith.science/.well-known/pith/7NNBGV6XDD7BKQ6M2JEPJYV5DC/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2023:7NNBGV6XDD7BKQ6M2JEPJYV5DC","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":"07905f0bef59d3628ee064f863f6ba2ad13f5879432f008b312a4d81d65e5926","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2023-11-20T23:17:20Z","title_canon_sha256":"bba6ca2f83763f692811a4a1eb7ba526a015cc79c7c1bf3cfed2491db1dda0bf"},"schema_version":"1.0","source":{"id":"2311.12233","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2311.12233","created_at":"2026-07-05T07:15:02Z"},{"alias_kind":"arxiv_version","alias_value":"2311.12233v1","created_at":"2026-07-05T07:15:02Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2311.12233","created_at":"2026-07-05T07:15:02Z"},{"alias_kind":"pith_short_12","alias_value":"7NNBGV6XDD7B","created_at":"2026-07-05T07:15:02Z"},{"alias_kind":"pith_short_16","alias_value":"7NNBGV6XDD7BKQ6M","created_at":"2026-07-05T07:15:02Z"},{"alias_kind":"pith_short_8","alias_value":"7NNBGV6X","created_at":"2026-07-05T07:15:02Z"}],"graph_snapshots":[{"event_id":"sha256:e99c17c0341ec66a270c6554964193fbf3a4e368f436b0d8ea3297e3a327b4bc","target":"graph","created_at":"2026-07-05T07:15:02Z","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/2311.12233/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"As businesses, products, and services spring up around large language models, the trustworthiness of these models hinges on the verifiability of their outputs. However, methods for explaining language model outputs largely fall across two distinct fields of study which both use the term \"attribution\" to refer to entirely separate techniques: citation generation and training data attribution. In many modern applications, such as legal document generation and medical question answering, both types of attributions are important. In this work, we argue for and present a unified framework of large ","authors_text":"Caleb Winston, Carlos Guestrin, Judy Hanwen Shen, Nicole Meister, Theodora Worledge","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2023-11-20T23:17:20Z","title":"Unifying Corroborative and Contributive Attributions in Large Language Models"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2311.12233","kind":"arxiv","version":1},"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:ce928f06f3d1c0963df1b27ed3b86a9714bea21006a51320c9f3b4f4195ad6a5","target":"record","created_at":"2026-07-05T07:15:02Z","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":"07905f0bef59d3628ee064f863f6ba2ad13f5879432f008b312a4d81d65e5926","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2023-11-20T23:17:20Z","title_canon_sha256":"bba6ca2f83763f692811a4a1eb7ba526a015cc79c7c1bf3cfed2491db1dda0bf"},"schema_version":"1.0","source":{"id":"2311.12233","kind":"arxiv","version":1}},"canonical_sha256":"fb5a1357d718fe1543ccd248f4e2bd188ce3cfa224a28f1ef748e28bb0634de8","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"fb5a1357d718fe1543ccd248f4e2bd188ce3cfa224a28f1ef748e28bb0634de8","first_computed_at":"2026-07-05T07:15:02.362075Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T07:15:02.362075Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"YY46xEkrn2PQILA35SZlpoZqO6JVOyf/UL/CFG4LNldSQloKa9YyFwNWtfEUCmnVZSiCrDCaBov3OF5cZ1VjBg==","signature_status":"signed_v1","signed_at":"2026-07-05T07:15:02.362483Z","signed_message":"canonical_sha256_bytes"},"source_id":"2311.12233","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:ce928f06f3d1c0963df1b27ed3b86a9714bea21006a51320c9f3b4f4195ad6a5","sha256:e99c17c0341ec66a270c6554964193fbf3a4e368f436b0d8ea3297e3a327b4bc"],"state_sha256":"1a6293debc7f0e9066e97638be2766fe9c383c4fc564b7508f469a8c3849f372"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"mcF8qB8ghP6uDa/rP/ktpqgRtmpjOsYvKKNb0PxSeg9EX3hhQ9Z+Wy431MNnrKIFm5zYm1JHLy+x86bqb+SvCQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-03T20:13:26.306655Z","bundle_sha256":"7afa1c4ec58aed21af6bbbb0e9ab3fa38c133655ddb95e8e50d227c83d8f16ac"}}