{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2026:MJFEB2OVNVW5WKSW72VUBJPBDO","short_pith_number":"pith:MJFEB2OV","canonical_record":{"source":{"id":"2605.16475","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.DL","submitted_at":"2026-05-15T15:42:54Z","cross_cats_sorted":["cs.CL"],"title_canon_sha256":"87a1644d66e346875a5cb189bd63dd66dd16e707c8c9e24aae4d74eb479cb24c","abstract_canon_sha256":"de45f858a5e532c87bbaf11b93e757604f24ef6c0f1fb0eb0fb0f68cfa7e71d3"},"schema_version":"1.0"},"canonical_sha256":"624a40e9d56d6ddb2a56feab40a5e11b9e0b60e8fba27666ba41029204833eab","source":{"kind":"arxiv","id":"2605.16475","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2605.16475","created_at":"2026-05-20T00:02:23Z"},{"alias_kind":"arxiv_version","alias_value":"2605.16475v1","created_at":"2026-05-20T00:02:23Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.16475","created_at":"2026-05-20T00:02:23Z"},{"alias_kind":"pith_short_12","alias_value":"MJFEB2OVNVW5","created_at":"2026-05-20T00:02:23Z"},{"alias_kind":"pith_short_16","alias_value":"MJFEB2OVNVW5WKSW","created_at":"2026-05-20T00:02:23Z"},{"alias_kind":"pith_short_8","alias_value":"MJFEB2OV","created_at":"2026-05-20T00:02:23Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2026:MJFEB2OVNVW5WKSW72VUBJPBDO","target":"record","payload":{"canonical_record":{"source":{"id":"2605.16475","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.DL","submitted_at":"2026-05-15T15:42:54Z","cross_cats_sorted":["cs.CL"],"title_canon_sha256":"87a1644d66e346875a5cb189bd63dd66dd16e707c8c9e24aae4d74eb479cb24c","abstract_canon_sha256":"de45f858a5e532c87bbaf11b93e757604f24ef6c0f1fb0eb0fb0f68cfa7e71d3"},"schema_version":"1.0"},"canonical_sha256":"624a40e9d56d6ddb2a56feab40a5e11b9e0b60e8fba27666ba41029204833eab","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-20T00:02:23.931371Z","signature_b64":"0XcFch0WrsX5pvwBfNhelm5Ut20GuEwq4kysFnSN8CHhpMWlNwpTzfiS1sKilHMhlPqRJbcnNBe2Wiljnu2CAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"624a40e9d56d6ddb2a56feab40a5e11b9e0b60e8fba27666ba41029204833eab","last_reissued_at":"2026-05-20T00:02:23.930755Z","signature_status":"signed_v1","first_computed_at":"2026-05-20T00:02:23.930755Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2605.16475","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":"2026-05-20T00:02:23Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"JG+4TLdZ+8ZFJSc3s2Ker4vdzPbZsIEh86vEfvBSP4Hl/Xe+EaOsuX4320F8cJ+mkT18PR92t7HPxg9iNHafCw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-11T12:53:56.428225Z"},"content_sha256":"0b6533e2da6001eef5158339174696f5a9aa82c80dad052fce083341db868b32","schema_version":"1.0","event_id":"sha256:0b6533e2da6001eef5158339174696f5a9aa82c80dad052fce083341db868b32"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2026:MJFEB2OVNVW5WKSW72VUBJPBDO","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Generative Artificial Intelligence for Literature Reviews","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"Generative AI can assist with literature reviews through summarization, question answering, and data extraction while requiring attention to risks.","cross_cats":["cs.CL"],"primary_cat":"cs.DL","authors_text":"Gerit Wagner, Guy Pare, Julian Prester, Reza Mousavi, Roman Lukyanenko","submitted_at":"2026-05-15T15:42:54Z","abstract_excerpt":"Generative artificial intelligence (GenAI), based on large-language models (LLMs), such as ChatGPT, has taken organizations, academia, and the public by storm. In particular, impressive GenAI capabilities such as summarization of large text corpora, question-answering, data extraction, and translation, carry profound implications for the conduct of literature reviews. This impacts science, organizations and the general public, as all can benefit from GenAI-supported literature reviews. Building on the technical foundations of GenAI and grounded in established methodological discourse, this wor"},"claims":{"count":4,"items":[{"kind":"strongest_claim","text":"Generative AI capabilities such as summarization of large text corpora, question-answering, data extraction, and translation carry profound implications for the conduct of literature reviews.","source":"verdict.strongest_claim","status":"machine_extracted","claim_id":"C1","attestation":"unclaimed"},{"kind":"weakest_assumption","text":"That illustrative prompts and suggested strategies remain methodologically sound when applied by typical users without introducing systematic biases, hallucinations, or incomplete coverage that would invalidate the resulting review.","source":"verdict.weakest_assumption","status":"machine_extracted","claim_id":"C2","attestation":"unclaimed"},{"kind":"one_line_summary","text":"Generative AI tools can assist literature reviews via summarization, extraction, and question-answering, with the paper providing prompt examples and balanced discussion of opportunities, risks, and open methodological issues.","source":"verdict.one_line_summary","status":"machine_extracted","claim_id":"C3","attestation":"unclaimed"},{"kind":"headline","text":"Generative AI can assist with literature reviews through summarization, question answering, and data extraction while requiring attention to risks.","source":"verdict.pith_extraction.headline","status":"machine_extracted","claim_id":"C4","attestation":"unclaimed"}],"snapshot_sha256":"62a8aff54b1933cfe41ef8d82bd10f14f3107b629881329ebcdfcb1d0be18597"},"source":{"id":"2605.16475","kind":"arxiv","version":1},"verdict":{"id":"527069e4-6acd-4ab9-8018-defbc4ddba94","model_set":{"reader":"grok-4.3"},"created_at":"2026-05-19T21:39:26.572967Z","strongest_claim":"Generative AI capabilities such as summarization of large text corpora, question-answering, data extraction, and translation carry profound implications for the conduct of literature reviews.","one_line_summary":"Generative AI tools can assist literature reviews via summarization, extraction, and question-answering, with the paper providing prompt examples and balanced discussion of opportunities, risks, and open methodological issues.","pipeline_version":"pith-pipeline@v0.9.0","weakest_assumption":"That illustrative prompts and suggested strategies remain methodologically sound when applied by typical users without introducing systematic biases, hallucinations, or incomplete coverage that would invalidate the resulting review.","pith_extraction_headline":"Generative AI can assist with literature reviews through summarization, question answering, and data extraction while requiring attention to risks."},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2605.16475/integrity.json","findings":[],"available":true,"detectors_run":[{"name":"doi_title_agreement","ran_at":"2026-05-19T22:01:23.265609Z","status":"completed","version":"1.0.0","findings_count":0},{"name":"doi_compliance","ran_at":"2026-05-19T21:52:01.939142Z","status":"completed","version":"1.0.0","findings_count":0},{"name":"ai_meta_artifact","ran_at":"2026-05-19T19:33:23.114242Z","status":"skipped","version":"1.0.0","findings_count":0},{"name":"claim_evidence","ran_at":"2026-05-19T19:21:57.044244Z","status":"completed","version":"1.0.0","findings_count":0}],"snapshot_sha256":"3c85f2fa503b402ffd9823aadf8ee96d5e30b7048b34ca64ce6fa19d6e48d0cf"},"references":{"count":2,"sample":[{"doi":"10.1109/weef-","year":2023,"title":"(2024) Unmasking bias in arti ficial intelligence: a systematic review of bias detection and miti- gation strategies in electronic health record-based models","work_id":"00795d58-a299-4c1c-bbe3-5b3c23cd9905","ref_index":1,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"10.1186/s13643-023-02243-z","year":2018,"title":"Systems Analysis and Design: An Object-Oriented Ap- proach with UML","work_id":"7ceb7e97-c322-4719-ab65-726ac21351eb","ref_index":2,"cited_arxiv_id":"","is_internal_anchor":false}],"resolved_work":2,"snapshot_sha256":"ee41de618c4d409cb666fe9cd741057c47ff3ccbe3bf41871f50307a22c87ffc","internal_anchors":0},"formal_canon":{"evidence_count":1,"snapshot_sha256":"53fd0eea2a26a5afb299aacbc7e866b63a1814d5f5f28c0b293883313d9d49eb"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"verdict_id":"527069e4-6acd-4ab9-8018-defbc4ddba94"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-20T00:02:23Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"fZ7OHM4pgC4WHZAQ3vQF4sdywNqPh6kGa1QmFyNsWdITgMZw3IJBQArGH9jOoaakjEuVWjHJKj5MItb3rxLyDQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-11T12:53:56.429066Z"},"content_sha256":"a09664ad9f0b39475e399a1f911086ec022b1fe34006a607eeb532f7c6d9a147","schema_version":"1.0","event_id":"sha256:a09664ad9f0b39475e399a1f911086ec022b1fe34006a607eeb532f7c6d9a147"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/MJFEB2OVNVW5WKSW72VUBJPBDO/bundle.json","state_url":"https://pith.science/pith/MJFEB2OVNVW5WKSW72VUBJPBDO/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/MJFEB2OVNVW5WKSW72VUBJPBDO/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":"2026-06-11T12:53:56Z","links":{"resolver":"https://pith.science/pith/MJFEB2OVNVW5WKSW72VUBJPBDO","bundle":"https://pith.science/pith/MJFEB2OVNVW5WKSW72VUBJPBDO/bundle.json","state":"https://pith.science/pith/MJFEB2OVNVW5WKSW72VUBJPBDO/state.json","well_known_bundle":"https://pith.science/.well-known/pith/MJFEB2OVNVW5WKSW72VUBJPBDO/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:MJFEB2OVNVW5WKSW72VUBJPBDO","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":"de45f858a5e532c87bbaf11b93e757604f24ef6c0f1fb0eb0fb0f68cfa7e71d3","cross_cats_sorted":["cs.CL"],"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.DL","submitted_at":"2026-05-15T15:42:54Z","title_canon_sha256":"87a1644d66e346875a5cb189bd63dd66dd16e707c8c9e24aae4d74eb479cb24c"},"schema_version":"1.0","source":{"id":"2605.16475","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2605.16475","created_at":"2026-05-20T00:02:23Z"},{"alias_kind":"arxiv_version","alias_value":"2605.16475v1","created_at":"2026-05-20T00:02:23Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.16475","created_at":"2026-05-20T00:02:23Z"},{"alias_kind":"pith_short_12","alias_value":"MJFEB2OVNVW5","created_at":"2026-05-20T00:02:23Z"},{"alias_kind":"pith_short_16","alias_value":"MJFEB2OVNVW5WKSW","created_at":"2026-05-20T00:02:23Z"},{"alias_kind":"pith_short_8","alias_value":"MJFEB2OV","created_at":"2026-05-20T00:02:23Z"}],"graph_snapshots":[{"event_id":"sha256:a09664ad9f0b39475e399a1f911086ec022b1fe34006a607eeb532f7c6d9a147","target":"graph","created_at":"2026-05-20T00:02:23Z","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":4,"items":[{"attestation":"unclaimed","claim_id":"C1","kind":"strongest_claim","source":"verdict.strongest_claim","status":"machine_extracted","text":"Generative AI capabilities such as summarization of large text corpora, question-answering, data extraction, and translation carry profound implications for the conduct of literature reviews."},{"attestation":"unclaimed","claim_id":"C2","kind":"weakest_assumption","source":"verdict.weakest_assumption","status":"machine_extracted","text":"That illustrative prompts and suggested strategies remain methodologically sound when applied by typical users without introducing systematic biases, hallucinations, or incomplete coverage that would invalidate the resulting review."},{"attestation":"unclaimed","claim_id":"C3","kind":"one_line_summary","source":"verdict.one_line_summary","status":"machine_extracted","text":"Generative AI tools can assist literature reviews via summarization, extraction, and question-answering, with the paper providing prompt examples and balanced discussion of opportunities, risks, and open methodological issues."},{"attestation":"unclaimed","claim_id":"C4","kind":"headline","source":"verdict.pith_extraction.headline","status":"machine_extracted","text":"Generative AI can assist with literature reviews through summarization, question answering, and data extraction while requiring attention to risks."}],"snapshot_sha256":"62a8aff54b1933cfe41ef8d82bd10f14f3107b629881329ebcdfcb1d0be18597"},"formal_canon":{"evidence_count":1,"snapshot_sha256":"53fd0eea2a26a5afb299aacbc7e866b63a1814d5f5f28c0b293883313d9d49eb"},"integrity":{"available":true,"clean":true,"detectors_run":[{"findings_count":0,"name":"doi_title_agreement","ran_at":"2026-05-19T22:01:23.265609Z","status":"completed","version":"1.0.0"},{"findings_count":0,"name":"doi_compliance","ran_at":"2026-05-19T21:52:01.939142Z","status":"completed","version":"1.0.0"},{"findings_count":0,"name":"ai_meta_artifact","ran_at":"2026-05-19T19:33:23.114242Z","status":"skipped","version":"1.0.0"},{"findings_count":0,"name":"claim_evidence","ran_at":"2026-05-19T19:21:57.044244Z","status":"completed","version":"1.0.0"}],"endpoint":"/pith/2605.16475/integrity.json","findings":[],"snapshot_sha256":"3c85f2fa503b402ffd9823aadf8ee96d5e30b7048b34ca64ce6fa19d6e48d0cf","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Generative artificial intelligence (GenAI), based on large-language models (LLMs), such as ChatGPT, has taken organizations, academia, and the public by storm. In particular, impressive GenAI capabilities such as summarization of large text corpora, question-answering, data extraction, and translation, carry profound implications for the conduct of literature reviews. This impacts science, organizations and the general public, as all can benefit from GenAI-supported literature reviews. Building on the technical foundations of GenAI and grounded in established methodological discourse, this wor","authors_text":"Gerit Wagner, Guy Pare, Julian Prester, Reza Mousavi, Roman Lukyanenko","cross_cats":["cs.CL"],"headline":"Generative AI can assist with literature reviews through summarization, question answering, and data extraction while requiring attention to risks.","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.DL","submitted_at":"2026-05-15T15:42:54Z","title":"Generative Artificial Intelligence for Literature Reviews"},"references":{"count":2,"internal_anchors":0,"resolved_work":2,"sample":[{"cited_arxiv_id":"","doi":"10.1109/weef-","is_internal_anchor":false,"ref_index":1,"title":"(2024) Unmasking bias in arti ficial intelligence: a systematic review of bias detection and miti- gation strategies in electronic health record-based models","work_id":"00795d58-a299-4c1c-bbe3-5b3c23cd9905","year":2023},{"cited_arxiv_id":"","doi":"10.1186/s13643-023-02243-z","is_internal_anchor":false,"ref_index":2,"title":"Systems Analysis and Design: An Object-Oriented Ap- proach with UML","work_id":"7ceb7e97-c322-4719-ab65-726ac21351eb","year":2018}],"snapshot_sha256":"ee41de618c4d409cb666fe9cd741057c47ff3ccbe3bf41871f50307a22c87ffc"},"source":{"id":"2605.16475","kind":"arxiv","version":1},"verdict":{"created_at":"2026-05-19T21:39:26.572967Z","id":"527069e4-6acd-4ab9-8018-defbc4ddba94","model_set":{"reader":"grok-4.3"},"one_line_summary":"Generative AI tools can assist literature reviews via summarization, extraction, and question-answering, with the paper providing prompt examples and balanced discussion of opportunities, risks, and open methodological issues.","pipeline_version":"pith-pipeline@v0.9.0","pith_extraction_headline":"Generative AI can assist with literature reviews through summarization, question answering, and data extraction while requiring attention to risks.","strongest_claim":"Generative AI capabilities such as summarization of large text corpora, question-answering, data extraction, and translation carry profound implications for the conduct of literature reviews.","weakest_assumption":"That illustrative prompts and suggested strategies remain methodologically sound when applied by typical users without introducing systematic biases, hallucinations, or incomplete coverage that would invalidate the resulting review."}},"verdict_id":"527069e4-6acd-4ab9-8018-defbc4ddba94"}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:0b6533e2da6001eef5158339174696f5a9aa82c80dad052fce083341db868b32","target":"record","created_at":"2026-05-20T00:02:23Z","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":"de45f858a5e532c87bbaf11b93e757604f24ef6c0f1fb0eb0fb0f68cfa7e71d3","cross_cats_sorted":["cs.CL"],"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.DL","submitted_at":"2026-05-15T15:42:54Z","title_canon_sha256":"87a1644d66e346875a5cb189bd63dd66dd16e707c8c9e24aae4d74eb479cb24c"},"schema_version":"1.0","source":{"id":"2605.16475","kind":"arxiv","version":1}},"canonical_sha256":"624a40e9d56d6ddb2a56feab40a5e11b9e0b60e8fba27666ba41029204833eab","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"624a40e9d56d6ddb2a56feab40a5e11b9e0b60e8fba27666ba41029204833eab","first_computed_at":"2026-05-20T00:02:23.930755Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-20T00:02:23.930755Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"0XcFch0WrsX5pvwBfNhelm5Ut20GuEwq4kysFnSN8CHhpMWlNwpTzfiS1sKilHMhlPqRJbcnNBe2Wiljnu2CAg==","signature_status":"signed_v1","signed_at":"2026-05-20T00:02:23.931371Z","signed_message":"canonical_sha256_bytes"},"source_id":"2605.16475","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:0b6533e2da6001eef5158339174696f5a9aa82c80dad052fce083341db868b32","sha256:a09664ad9f0b39475e399a1f911086ec022b1fe34006a607eeb532f7c6d9a147"],"state_sha256":"0713c1c9cec15c7e2d6e54ac0614bb65ee3b0ba43dc3bc1fe7bc3ad1672f7132"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Y9/eTFkw1aPCUlpEE+QoisvH9Cfdq8J6FdBetELvWQCphwEYSTV9HeLXliAAgv+YQ7+NpUAD87dBYrciYjn7Cg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-11T12:53:56.431822Z","bundle_sha256":"03c645404fc0701e0d5eadd7b243964ca864ce485c4eff67e19bc4243bfe54ab"}}