{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:7QAEGZKL7HUE4USSTVAPUEXEJH","short_pith_number":"pith:7QAEGZKL","schema_version":"1.0","canonical_sha256":"fc0043654bf9e84e52529d40fa12e449dc8ea176734ced94e14eb7b0ec9df55a","source":{"kind":"arxiv","id":"2506.06886","version":1},"attestation_state":"computed","paper":{"title":"Hybrid Vision Transformer-Mamba Framework for Autism Diagnosis via Eye-Tracking Analysis","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Abigail Copiaco, Ammar Albanna, Valsamma Eapen, Wafaa Kasri, Wathiq Mansoor, Yassine Himeur","submitted_at":"2025-06-07T18:27:24Z","abstract_excerpt":"Accurate Autism Spectrum Disorder (ASD) diagnosis is vital for early intervention. This study presents a hybrid deep learning framework combining Vision Transformers (ViT) and Vision Mamba to detect ASD using eye-tracking data. The model uses attention-based fusion to integrate visual, speech, and facial cues, capturing both spatial and temporal dynamics. Unlike traditional handcrafted methods, it applies state-of-the-art deep learning and explainable AI techniques to enhance diagnostic accuracy and transparency. Tested on the Saliency4ASD dataset, the proposed ViT-Mamba model outperformed exi"},"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":"2506.06886","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2025-06-07T18:27:24Z","cross_cats_sorted":[],"title_canon_sha256":"b251632f2a0a08118edb00fb66108293ff521a5b8fb000287587977e8b095225","abstract_canon_sha256":"65cd74a728aaf34ab961a4e3afae808267dd0856fdc07c8bda14df467d3a19f9"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:17:57.551364Z","signature_b64":"hWnNiE32/kbbMjMrLxmJbtV0sBUHHKN93dPzLQ0snfcVmFjugb9pPlj2WgjpmPEjxbbgp8UNJsM9cbXqvkPRCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"fc0043654bf9e84e52529d40fa12e449dc8ea176734ced94e14eb7b0ec9df55a","last_reissued_at":"2026-07-05T11:17:57.550823Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:17:57.550823Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Hybrid Vision Transformer-Mamba Framework for Autism Diagnosis via Eye-Tracking Analysis","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Abigail Copiaco, Ammar Albanna, Valsamma Eapen, Wafaa Kasri, Wathiq Mansoor, Yassine Himeur","submitted_at":"2025-06-07T18:27:24Z","abstract_excerpt":"Accurate Autism Spectrum Disorder (ASD) diagnosis is vital for early intervention. This study presents a hybrid deep learning framework combining Vision Transformers (ViT) and Vision Mamba to detect ASD using eye-tracking data. The model uses attention-based fusion to integrate visual, speech, and facial cues, capturing both spatial and temporal dynamics. Unlike traditional handcrafted methods, it applies state-of-the-art deep learning and explainable AI techniques to enhance diagnostic accuracy and transparency. Tested on the Saliency4ASD dataset, the proposed ViT-Mamba model outperformed exi"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2506.06886","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/2506.06886/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":"2506.06886","created_at":"2026-07-05T11:17:57.550885+00:00"},{"alias_kind":"arxiv_version","alias_value":"2506.06886v1","created_at":"2026-07-05T11:17:57.550885+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2506.06886","created_at":"2026-07-05T11:17:57.550885+00:00"},{"alias_kind":"pith_short_12","alias_value":"7QAEGZKL7HUE","created_at":"2026-07-05T11:17:57.550885+00:00"},{"alias_kind":"pith_short_16","alias_value":"7QAEGZKL7HUE4USS","created_at":"2026-07-05T11:17:57.550885+00:00"},{"alias_kind":"pith_short_8","alias_value":"7QAEGZKL","created_at":"2026-07-05T11:17:57.550885+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/7QAEGZKL7HUE4USSTVAPUEXEJH","json":"https://pith.science/pith/7QAEGZKL7HUE4USSTVAPUEXEJH.json","graph_json":"https://pith.science/api/pith-number/7QAEGZKL7HUE4USSTVAPUEXEJH/graph.json","events_json":"https://pith.science/api/pith-number/7QAEGZKL7HUE4USSTVAPUEXEJH/events.json","paper":"https://pith.science/paper/7QAEGZKL"},"agent_actions":{"view_html":"https://pith.science/pith/7QAEGZKL7HUE4USSTVAPUEXEJH","download_json":"https://pith.science/pith/7QAEGZKL7HUE4USSTVAPUEXEJH.json","view_paper":"https://pith.science/paper/7QAEGZKL","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2506.06886&json=true","fetch_graph":"https://pith.science/api/pith-number/7QAEGZKL7HUE4USSTVAPUEXEJH/graph.json","fetch_events":"https://pith.science/api/pith-number/7QAEGZKL7HUE4USSTVAPUEXEJH/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/7QAEGZKL7HUE4USSTVAPUEXEJH/action/timestamp_anchor","attest_storage":"https://pith.science/pith/7QAEGZKL7HUE4USSTVAPUEXEJH/action/storage_attestation","attest_author":"https://pith.science/pith/7QAEGZKL7HUE4USSTVAPUEXEJH/action/author_attestation","sign_citation":"https://pith.science/pith/7QAEGZKL7HUE4USSTVAPUEXEJH/action/citation_signature","submit_replication":"https://pith.science/pith/7QAEGZKL7HUE4USSTVAPUEXEJH/action/replication_record"}},"created_at":"2026-07-05T11:17:57.550885+00:00","updated_at":"2026-07-05T11:17:57.550885+00:00"}