{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:5AS7BL7WKIOSCBEBMWHM5I4ZVY","short_pith_number":"pith:5AS7BL7W","schema_version":"1.0","canonical_sha256":"e825f0aff6521d210481658ecea399ae23a38b3025be61f8b0fca84172b96f49","source":{"kind":"arxiv","id":"2506.21589","version":1},"attestation_state":"computed","paper":{"title":"A General Method for Detecting Information Generated by Large Language Models","license":"http://creativecommons.org/licenses/by-sa/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Dongjun Wei, Michael Chau, Minjia Mao, Xiao Fang","submitted_at":"2025-06-18T04:59:51Z","abstract_excerpt":"The proliferation of large language models (LLMs) has significantly transformed the digital information landscape, making it increasingly challenging to distinguish between human-written and LLM-generated content. Detecting LLM-generated information is essential for preserving trust on digital platforms (e.g., social media and e-commerce sites) and preventing the spread of misinformation, a topic that has garnered significant attention in IS research. However, current detection methods, which primarily focus on identifying content generated by specific LLMs in known domains, face challenges in"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2506.21589","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"cs.CL","submitted_at":"2025-06-18T04:59:51Z","cross_cats_sorted":[],"title_canon_sha256":"5d6ba642a2af307b701fd0b0d46ad7f83689779acbebdc74b5f60e4d2eb5ed01","abstract_canon_sha256":"9b5d79569cec6eb2248953bebed9c1b6fb12d2d3bcd561e0f69a792055921a19"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:27:56.845791Z","signature_b64":"k0IQ1/PdbYNRVywDTZfIJMEzDJbW7uquFH7S5FKpSAQZKrzDiDsoAO65cTQkuLEe2f0LxAhtJh0hayPZaKoUAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"e825f0aff6521d210481658ecea399ae23a38b3025be61f8b0fca84172b96f49","last_reissued_at":"2026-07-05T11:27:56.845240Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:27:56.845240Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"A General Method for Detecting Information Generated by Large Language Models","license":"http://creativecommons.org/licenses/by-sa/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Dongjun Wei, Michael Chau, Minjia Mao, Xiao Fang","submitted_at":"2025-06-18T04:59:51Z","abstract_excerpt":"The proliferation of large language models (LLMs) has significantly transformed the digital information landscape, making it increasingly challenging to distinguish between human-written and LLM-generated content. Detecting LLM-generated information is essential for preserving trust on digital platforms (e.g., social media and e-commerce sites) and preventing the spread of misinformation, a topic that has garnered significant attention in IS research. However, current detection methods, which primarily focus on identifying content generated by specific LLMs in known domains, face challenges in"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2506.21589","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/2506.21589/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"},"aliases":[{"alias_kind":"arxiv","alias_value":"2506.21589","created_at":"2026-07-05T11:27:56.845328+00:00"},{"alias_kind":"arxiv_version","alias_value":"2506.21589v1","created_at":"2026-07-05T11:27:56.845328+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2506.21589","created_at":"2026-07-05T11:27:56.845328+00:00"},{"alias_kind":"pith_short_12","alias_value":"5AS7BL7WKIOS","created_at":"2026-07-05T11:27:56.845328+00:00"},{"alias_kind":"pith_short_16","alias_value":"5AS7BL7WKIOSCBEB","created_at":"2026-07-05T11:27:56.845328+00:00"},{"alias_kind":"pith_short_8","alias_value":"5AS7BL7W","created_at":"2026-07-05T11:27:56.845328+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2605.12890","citing_title":"Steer-to-Detect: Probing Hidden Representations for Detection of LLM-Generated Texts","ref_index":58,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/5AS7BL7WKIOSCBEBMWHM5I4ZVY","json":"https://pith.science/pith/5AS7BL7WKIOSCBEBMWHM5I4ZVY.json","graph_json":"https://pith.science/api/pith-number/5AS7BL7WKIOSCBEBMWHM5I4ZVY/graph.json","events_json":"https://pith.science/api/pith-number/5AS7BL7WKIOSCBEBMWHM5I4ZVY/events.json","paper":"https://pith.science/paper/5AS7BL7W"},"agent_actions":{"view_html":"https://pith.science/pith/5AS7BL7WKIOSCBEBMWHM5I4ZVY","download_json":"https://pith.science/pith/5AS7BL7WKIOSCBEBMWHM5I4ZVY.json","view_paper":"https://pith.science/paper/5AS7BL7W","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2506.21589&json=true","fetch_graph":"https://pith.science/api/pith-number/5AS7BL7WKIOSCBEBMWHM5I4ZVY/graph.json","fetch_events":"https://pith.science/api/pith-number/5AS7BL7WKIOSCBEBMWHM5I4ZVY/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/5AS7BL7WKIOSCBEBMWHM5I4ZVY/action/timestamp_anchor","attest_storage":"https://pith.science/pith/5AS7BL7WKIOSCBEBMWHM5I4ZVY/action/storage_attestation","attest_author":"https://pith.science/pith/5AS7BL7WKIOSCBEBMWHM5I4ZVY/action/author_attestation","sign_citation":"https://pith.science/pith/5AS7BL7WKIOSCBEBMWHM5I4ZVY/action/citation_signature","submit_replication":"https://pith.science/pith/5AS7BL7WKIOSCBEBMWHM5I4ZVY/action/replication_record"}},"created_at":"2026-07-05T11:27:56.845328+00:00","updated_at":"2026-07-05T11:27:56.845328+00:00"}