{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:54TAWUDKDCENIU6HKVLSBO6YRG","short_pith_number":"pith:54TAWUDK","schema_version":"1.0","canonical_sha256":"ef260b506a1888d453c7555720bbd889995562de1c6c573ef082fda3b67bf113","source":{"kind":"arxiv","id":"2505.16576","version":2},"attestation_state":"computed","paper":{"title":"EMULATE: A Multi-Agent Framework for Determining the Veracity of Atomic Claims by Emulating Human Actions","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Meng Luo, Spencer Hong, Xinyi Wan","submitted_at":"2025-05-22T12:08:08Z","abstract_excerpt":"Determining the veracity of atomic claims is an imperative component of many recently proposed fact-checking systems. Many approaches tackle this problem by first retrieving evidence by querying a search engine and then performing classification by providing the evidence set and atomic claim to a large language model, but this process deviates from what a human would do in order to perform the task. Recent work attempted to address this issue by proposing iterative evidence retrieval, allowing for evidence to be collected several times and only when necessary. Continuing along this line of res"},"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":"2505.16576","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2025-05-22T12:08:08Z","cross_cats_sorted":[],"title_canon_sha256":"63ac93a37ba2f82f4a6a098cc1fe78bfb72d6538ec66ab8ac8e1956a91ebc956","abstract_canon_sha256":"ec1fe604a97719fa0e219938a697686acce00c156bcf6d5e160afad08ef218c8"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:25:45.119112Z","signature_b64":"PFk93WrelcMx2wfO86QVemXT1WHmA+pgj9PeJ4CN9k1BTzyAkPRcYGGcLSb1IY1+EsL165+n4QuyGdaojOIEDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"ef260b506a1888d453c7555720bbd889995562de1c6c573ef082fda3b67bf113","last_reissued_at":"2026-07-05T11:25:45.118665Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:25:45.118665Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"EMULATE: A Multi-Agent Framework for Determining the Veracity of Atomic Claims by Emulating Human Actions","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Meng Luo, Spencer Hong, Xinyi Wan","submitted_at":"2025-05-22T12:08:08Z","abstract_excerpt":"Determining the veracity of atomic claims is an imperative component of many recently proposed fact-checking systems. Many approaches tackle this problem by first retrieving evidence by querying a search engine and then performing classification by providing the evidence set and atomic claim to a large language model, but this process deviates from what a human would do in order to perform the task. Recent work attempted to address this issue by proposing iterative evidence retrieval, allowing for evidence to be collected several times and only when necessary. Continuing along this line of res"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2505.16576","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/2505.16576/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":"2505.16576","created_at":"2026-07-05T11:25:45.118722+00:00"},{"alias_kind":"arxiv_version","alias_value":"2505.16576v2","created_at":"2026-07-05T11:25:45.118722+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2505.16576","created_at":"2026-07-05T11:25:45.118722+00:00"},{"alias_kind":"pith_short_12","alias_value":"54TAWUDKDCEN","created_at":"2026-07-05T11:25:45.118722+00:00"},{"alias_kind":"pith_short_16","alias_value":"54TAWUDKDCENIU6H","created_at":"2026-07-05T11:25:45.118722+00:00"},{"alias_kind":"pith_short_8","alias_value":"54TAWUDK","created_at":"2026-07-05T11:25:45.118722+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2604.12184","citing_title":"TRUST Agents: A Collaborative Multi-Agent Framework for Fake News Detection, Explainable Verification, and Logic-Aware Claim Reasoning","ref_index":6,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/54TAWUDKDCENIU6HKVLSBO6YRG","json":"https://pith.science/pith/54TAWUDKDCENIU6HKVLSBO6YRG.json","graph_json":"https://pith.science/api/pith-number/54TAWUDKDCENIU6HKVLSBO6YRG/graph.json","events_json":"https://pith.science/api/pith-number/54TAWUDKDCENIU6HKVLSBO6YRG/events.json","paper":"https://pith.science/paper/54TAWUDK"},"agent_actions":{"view_html":"https://pith.science/pith/54TAWUDKDCENIU6HKVLSBO6YRG","download_json":"https://pith.science/pith/54TAWUDKDCENIU6HKVLSBO6YRG.json","view_paper":"https://pith.science/paper/54TAWUDK","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2505.16576&json=true","fetch_graph":"https://pith.science/api/pith-number/54TAWUDKDCENIU6HKVLSBO6YRG/graph.json","fetch_events":"https://pith.science/api/pith-number/54TAWUDKDCENIU6HKVLSBO6YRG/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/54TAWUDKDCENIU6HKVLSBO6YRG/action/timestamp_anchor","attest_storage":"https://pith.science/pith/54TAWUDKDCENIU6HKVLSBO6YRG/action/storage_attestation","attest_author":"https://pith.science/pith/54TAWUDKDCENIU6HKVLSBO6YRG/action/author_attestation","sign_citation":"https://pith.science/pith/54TAWUDKDCENIU6HKVLSBO6YRG/action/citation_signature","submit_replication":"https://pith.science/pith/54TAWUDKDCENIU6HKVLSBO6YRG/action/replication_record"}},"created_at":"2026-07-05T11:25:45.118722+00:00","updated_at":"2026-07-05T11:25:45.118722+00:00"}