{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:XLEB2CK5FWXL7HS3RLLQDRDHSR","short_pith_number":"pith:XLEB2CK5","schema_version":"1.0","canonical_sha256":"bac81d095d2daebf9e5b8ad701c4679443099f055e4415b1741a7ef757e8fdf4","source":{"kind":"arxiv","id":"2606.24644","version":1},"attestation_state":"computed","paper":{"title":"Measuring User's Mental Models of Speech Translation in Human-AI Collaboration","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.HC"],"primary_cat":"cs.CL","authors_text":"HyoJung Han, Jordan Boyd-Graber, Marine Carpuat, Nishant Balepur","submitted_at":"2026-06-23T14:40:36Z","abstract_excerpt":"Millions of people use machine translation (MT) tools daily, yet little is known about their perception of what systems can and cannot do. This paper studies users' mental models of speech translation systems through a new framework based on cross-lingual question answering, where users either accept MT output or request professional re-translation to answer questions based on the information presented in a foreign language. By analyzing user behavior and accuracy trends across varying translation qualities, we examine to what extent they can predict where the system is likely to be wrong, and"},"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":"2606.24644","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2026-06-23T14:40:36Z","cross_cats_sorted":["cs.HC"],"title_canon_sha256":"9e520ec6cd2695347fb2b3cd5a5cbc2464cbfb04faedbcacc9875aff92dc0907","abstract_canon_sha256":"79fc8df20b862b5ea2d829b05298c280cf5f6ccfe9158dcec80b702979c4104e"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-24T01:15:37.976522Z","signature_b64":"/i1awKV8hqjanDRaWYOx5mEoWmJmFsNrpbejaFtz1yzmwVAngUve6t8VhMEi/gOxmee/H+g+6TUIpRON7zy4CQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"bac81d095d2daebf9e5b8ad701c4679443099f055e4415b1741a7ef757e8fdf4","last_reissued_at":"2026-06-24T01:15:37.976042Z","signature_status":"signed_v1","first_computed_at":"2026-06-24T01:15:37.976042Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Measuring User's Mental Models of Speech Translation in Human-AI Collaboration","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.HC"],"primary_cat":"cs.CL","authors_text":"HyoJung Han, Jordan Boyd-Graber, Marine Carpuat, Nishant Balepur","submitted_at":"2026-06-23T14:40:36Z","abstract_excerpt":"Millions of people use machine translation (MT) tools daily, yet little is known about their perception of what systems can and cannot do. This paper studies users' mental models of speech translation systems through a new framework based on cross-lingual question answering, where users either accept MT output or request professional re-translation to answer questions based on the information presented in a foreign language. By analyzing user behavior and accuracy trends across varying translation qualities, we examine to what extent they can predict where the system is likely to be wrong, and"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2606.24644","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/2606.24644/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":"2606.24644","created_at":"2026-06-24T01:15:37.976118+00:00"},{"alias_kind":"arxiv_version","alias_value":"2606.24644v1","created_at":"2026-06-24T01:15:37.976118+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2606.24644","created_at":"2026-06-24T01:15:37.976118+00:00"},{"alias_kind":"pith_short_12","alias_value":"XLEB2CK5FWXL","created_at":"2026-06-24T01:15:37.976118+00:00"},{"alias_kind":"pith_short_16","alias_value":"XLEB2CK5FWXL7HS3","created_at":"2026-06-24T01:15:37.976118+00:00"},{"alias_kind":"pith_short_8","alias_value":"XLEB2CK5","created_at":"2026-06-24T01:15:37.976118+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/XLEB2CK5FWXL7HS3RLLQDRDHSR","json":"https://pith.science/pith/XLEB2CK5FWXL7HS3RLLQDRDHSR.json","graph_json":"https://pith.science/api/pith-number/XLEB2CK5FWXL7HS3RLLQDRDHSR/graph.json","events_json":"https://pith.science/api/pith-number/XLEB2CK5FWXL7HS3RLLQDRDHSR/events.json","paper":"https://pith.science/paper/XLEB2CK5"},"agent_actions":{"view_html":"https://pith.science/pith/XLEB2CK5FWXL7HS3RLLQDRDHSR","download_json":"https://pith.science/pith/XLEB2CK5FWXL7HS3RLLQDRDHSR.json","view_paper":"https://pith.science/paper/XLEB2CK5","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2606.24644&json=true","fetch_graph":"https://pith.science/api/pith-number/XLEB2CK5FWXL7HS3RLLQDRDHSR/graph.json","fetch_events":"https://pith.science/api/pith-number/XLEB2CK5FWXL7HS3RLLQDRDHSR/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/XLEB2CK5FWXL7HS3RLLQDRDHSR/action/timestamp_anchor","attest_storage":"https://pith.science/pith/XLEB2CK5FWXL7HS3RLLQDRDHSR/action/storage_attestation","attest_author":"https://pith.science/pith/XLEB2CK5FWXL7HS3RLLQDRDHSR/action/author_attestation","sign_citation":"https://pith.science/pith/XLEB2CK5FWXL7HS3RLLQDRDHSR/action/citation_signature","submit_replication":"https://pith.science/pith/XLEB2CK5FWXL7HS3RLLQDRDHSR/action/replication_record"}},"created_at":"2026-06-24T01:15:37.976118+00:00","updated_at":"2026-06-24T01:15:37.976118+00:00"}