{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:VEF3OCPQDNOAR6TEJILOCQPRZS","short_pith_number":"pith:VEF3OCPQ","schema_version":"1.0","canonical_sha256":"a90bb709f01b5c08fa644a16e141f1cc876f13a8584c285df101be3f4b168ec7","source":{"kind":"arxiv","id":"2606.20696","version":1},"attestation_state":"computed","paper":{"title":"MindAlign: Decoding Inner Speech from fMRI Signals via Multimodal Embedding Alignment under Limited Data","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","eess.AS"],"primary_cat":"cs.CL","authors_text":"Ichiro Kobayashi, Muxuan Liu, Satoshi Nishida","submitted_at":"2026-06-15T08:30:03Z","abstract_excerpt":"Decoding inner speech from non-invasive brain signals remains a fundamental challenge due to the absence of overt linguistic output, limited training data, and large inter-subject variability. Existing brain-to-text approaches often rely on task-specific decoder fine-tuning, which restricts scalability and complicates adaptation to new participants. We propose MindAlign, a decoupled two-stage brain-to-language framework that enables open-ended text generation from fMRI signals without modifying the underlying language model. The first stage learns a subject-specific neural-semantic alignment t"},"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":"2606.20696","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2026-06-15T08:30:03Z","cross_cats_sorted":["cs.AI","eess.AS"],"title_canon_sha256":"05c7c4f2efbdeb1d3510e7bce712b094eecdbb6d63f250263080234ea0dafbf3","abstract_canon_sha256":"6a63195fa0f3ef9fa0f443b85a36346fc5c7ce8103f32140c588277475c85837"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-23T00:11:53.915653Z","signature_b64":"9818+IA/6ZlYZbVUV9IS8+9yByUKgxDLyxMt07N1SMve9FH5B8Xwngb4MAwYl304NGc/30q5/9CY3UbZg8sUBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"a90bb709f01b5c08fa644a16e141f1cc876f13a8584c285df101be3f4b168ec7","last_reissued_at":"2026-06-23T00:11:53.915241Z","signature_status":"signed_v1","first_computed_at":"2026-06-23T00:11:53.915241Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"MindAlign: Decoding Inner Speech from fMRI Signals via Multimodal Embedding Alignment under Limited Data","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","eess.AS"],"primary_cat":"cs.CL","authors_text":"Ichiro Kobayashi, Muxuan Liu, Satoshi Nishida","submitted_at":"2026-06-15T08:30:03Z","abstract_excerpt":"Decoding inner speech from non-invasive brain signals remains a fundamental challenge due to the absence of overt linguistic output, limited training data, and large inter-subject variability. Existing brain-to-text approaches often rely on task-specific decoder fine-tuning, which restricts scalability and complicates adaptation to new participants. We propose MindAlign, a decoupled two-stage brain-to-language framework that enables open-ended text generation from fMRI signals without modifying the underlying language model. The first stage learns a subject-specific neural-semantic alignment t"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2606.20696","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/2606.20696/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":"2606.20696","created_at":"2026-06-23T00:11:53.915304+00:00"},{"alias_kind":"arxiv_version","alias_value":"2606.20696v1","created_at":"2026-06-23T00:11:53.915304+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2606.20696","created_at":"2026-06-23T00:11:53.915304+00:00"},{"alias_kind":"pith_short_12","alias_value":"VEF3OCPQDNOA","created_at":"2026-06-23T00:11:53.915304+00:00"},{"alias_kind":"pith_short_16","alias_value":"VEF3OCPQDNOAR6TE","created_at":"2026-06-23T00:11:53.915304+00:00"},{"alias_kind":"pith_short_8","alias_value":"VEF3OCPQ","created_at":"2026-06-23T00:11:53.915304+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/VEF3OCPQDNOAR6TEJILOCQPRZS","json":"https://pith.science/pith/VEF3OCPQDNOAR6TEJILOCQPRZS.json","graph_json":"https://pith.science/api/pith-number/VEF3OCPQDNOAR6TEJILOCQPRZS/graph.json","events_json":"https://pith.science/api/pith-number/VEF3OCPQDNOAR6TEJILOCQPRZS/events.json","paper":"https://pith.science/paper/VEF3OCPQ"},"agent_actions":{"view_html":"https://pith.science/pith/VEF3OCPQDNOAR6TEJILOCQPRZS","download_json":"https://pith.science/pith/VEF3OCPQDNOAR6TEJILOCQPRZS.json","view_paper":"https://pith.science/paper/VEF3OCPQ","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2606.20696&json=true","fetch_graph":"https://pith.science/api/pith-number/VEF3OCPQDNOAR6TEJILOCQPRZS/graph.json","fetch_events":"https://pith.science/api/pith-number/VEF3OCPQDNOAR6TEJILOCQPRZS/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/VEF3OCPQDNOAR6TEJILOCQPRZS/action/timestamp_anchor","attest_storage":"https://pith.science/pith/VEF3OCPQDNOAR6TEJILOCQPRZS/action/storage_attestation","attest_author":"https://pith.science/pith/VEF3OCPQDNOAR6TEJILOCQPRZS/action/author_attestation","sign_citation":"https://pith.science/pith/VEF3OCPQDNOAR6TEJILOCQPRZS/action/citation_signature","submit_replication":"https://pith.science/pith/VEF3OCPQDNOAR6TEJILOCQPRZS/action/replication_record"}},"created_at":"2026-06-23T00:11:53.915304+00:00","updated_at":"2026-06-23T00:11:53.915304+00:00"}