{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:EL2B5C2KDV4ALIZXBNDXPR66LM","short_pith_number":"pith:EL2B5C2K","schema_version":"1.0","canonical_sha256":"22f41e8b4a1d7805a3370b4777c7de5b10921da2487bd1eb8a6775a018856de0","source":{"kind":"arxiv","id":"2509.00058","version":1},"attestation_state":"computed","paper":{"title":"A Comparative Study of Controllability, Explainability, and Performance in Dysfluency Detection Models","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.AI","authors_text":"Eric Zhang, Li Wei, Michael Wang, Sarah Chen","submitted_at":"2025-08-25T14:23:09Z","abstract_excerpt":"Recent advances in dysfluency detection have introduced a variety of modeling paradigms, ranging from lightweight object-detection inspired networks (YOLOStutter) to modular interpretable frameworks (UDM). While performance on benchmark datasets continues to improve, clinical adoption requires more than accuracy: models must be controllable and explainable. In this paper, we present a systematic comparative analysis of four representative approaches--YOLO-Stutter, FluentNet, UDM, and SSDM--along three dimensions: performance, controllability, and explainability. Through comprehensive evaluatio"},"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":"2509.00058","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.AI","submitted_at":"2025-08-25T14:23:09Z","cross_cats_sorted":[],"title_canon_sha256":"b0c8b772c84fa926c94ec62b81799d8b9388950786c5a1a6900a94b21d7a8534","abstract_canon_sha256":"80d8a07e700a04d7c53a073997811f28e55922d5d14f0719300f8f20d63f9584"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T12:02:08.799349Z","signature_b64":"Ev8q8Br+Wq4D6llaGr0YvyZICknLgsQ955CTScsx8k1P13d02InqNQlxUFFuiz8ng5cer4u1jZrQbY22Uz1ABw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"22f41e8b4a1d7805a3370b4777c7de5b10921da2487bd1eb8a6775a018856de0","last_reissued_at":"2026-07-05T12:02:08.798860Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T12:02:08.798860Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"A Comparative Study of Controllability, Explainability, and Performance in Dysfluency Detection Models","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.AI","authors_text":"Eric Zhang, Li Wei, Michael Wang, Sarah Chen","submitted_at":"2025-08-25T14:23:09Z","abstract_excerpt":"Recent advances in dysfluency detection have introduced a variety of modeling paradigms, ranging from lightweight object-detection inspired networks (YOLOStutter) to modular interpretable frameworks (UDM). While performance on benchmark datasets continues to improve, clinical adoption requires more than accuracy: models must be controllable and explainable. In this paper, we present a systematic comparative analysis of four representative approaches--YOLO-Stutter, FluentNet, UDM, and SSDM--along three dimensions: performance, controllability, and explainability. Through comprehensive evaluatio"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2509.00058","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/2509.00058/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":"2509.00058","created_at":"2026-07-05T12:02:08.798917+00:00"},{"alias_kind":"arxiv_version","alias_value":"2509.00058v1","created_at":"2026-07-05T12:02:08.798917+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2509.00058","created_at":"2026-07-05T12:02:08.798917+00:00"},{"alias_kind":"pith_short_12","alias_value":"EL2B5C2KDV4A","created_at":"2026-07-05T12:02:08.798917+00:00"},{"alias_kind":"pith_short_16","alias_value":"EL2B5C2KDV4ALIZX","created_at":"2026-07-05T12:02:08.798917+00:00"},{"alias_kind":"pith_short_8","alias_value":"EL2B5C2K","created_at":"2026-07-05T12:02:08.798917+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/EL2B5C2KDV4ALIZXBNDXPR66LM","json":"https://pith.science/pith/EL2B5C2KDV4ALIZXBNDXPR66LM.json","graph_json":"https://pith.science/api/pith-number/EL2B5C2KDV4ALIZXBNDXPR66LM/graph.json","events_json":"https://pith.science/api/pith-number/EL2B5C2KDV4ALIZXBNDXPR66LM/events.json","paper":"https://pith.science/paper/EL2B5C2K"},"agent_actions":{"view_html":"https://pith.science/pith/EL2B5C2KDV4ALIZXBNDXPR66LM","download_json":"https://pith.science/pith/EL2B5C2KDV4ALIZXBNDXPR66LM.json","view_paper":"https://pith.science/paper/EL2B5C2K","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2509.00058&json=true","fetch_graph":"https://pith.science/api/pith-number/EL2B5C2KDV4ALIZXBNDXPR66LM/graph.json","fetch_events":"https://pith.science/api/pith-number/EL2B5C2KDV4ALIZXBNDXPR66LM/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/EL2B5C2KDV4ALIZXBNDXPR66LM/action/timestamp_anchor","attest_storage":"https://pith.science/pith/EL2B5C2KDV4ALIZXBNDXPR66LM/action/storage_attestation","attest_author":"https://pith.science/pith/EL2B5C2KDV4ALIZXBNDXPR66LM/action/author_attestation","sign_citation":"https://pith.science/pith/EL2B5C2KDV4ALIZXBNDXPR66LM/action/citation_signature","submit_replication":"https://pith.science/pith/EL2B5C2KDV4ALIZXBNDXPR66LM/action/replication_record"}},"created_at":"2026-07-05T12:02:08.798917+00:00","updated_at":"2026-07-05T12:02:08.798917+00:00"}