{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:DKDTDX6WITJ3ENGVSGGUI3P233","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"50dcabc8d97ceddfd0de7cb691f60572e531e4e9d3ec090485d6d805122b64fa","cross_cats_sorted":["cs.AI"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2026-04-13T13:25:19Z","title_canon_sha256":"35c04e90079dd0e6b8d88c7a08db2bf51e2bc1403ab0f2f043046a376a13ad15"},"schema_version":"1.0","source":{"id":"2604.16491","kind":"arxiv","version":4}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2604.16491","created_at":"2026-05-20T00:03:11Z"},{"alias_kind":"arxiv_version","alias_value":"2604.16491v4","created_at":"2026-05-20T00:03:11Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2604.16491","created_at":"2026-05-20T00:03:11Z"},{"alias_kind":"pith_short_12","alias_value":"DKDTDX6WITJ3","created_at":"2026-05-20T00:03:11Z"},{"alias_kind":"pith_short_16","alias_value":"DKDTDX6WITJ3ENGV","created_at":"2026-05-20T00:03:11Z"},{"alias_kind":"pith_short_8","alias_value":"DKDTDX6W","created_at":"2026-05-20T00:03:11Z"}],"graph_snapshots":[{"event_id":"sha256:308089fa89f448dd5d8b2270d22a231ae5f31a7a1f2baee9b7a393d3b7f628f7","target":"graph","created_at":"2026-05-20T00:03:11Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":4,"items":[{"attestation":"unclaimed","claim_id":"C1","kind":"strongest_claim","source":"verdict.strongest_claim","status":"machine_extracted","text":"The proposed lightweight transformer fuses multiple fNIRS representations through a unified tokenization mechanism, enabling joint modeling of complementary signal views without requiring modality-specific adaptations or increasing architectural complexity, while achieving competitive pain recognition performance on the AI4Pain dataset."},{"attestation":"unclaimed","claim_id":"C2","kind":"weakest_assumption","source":"verdict.weakest_assumption","status":"machine_extracted","text":"That projecting heterogeneous fNIRS inputs (raw waveform and power spectral density) onto a shared latent representation via structured segmentation preserves all spatial, temporal, and time-frequency information necessary for accurate pain classification without significant loss."},{"attestation":"unclaimed","claim_id":"C3","kind":"one_line_summary","source":"verdict.one_line_summary","status":"machine_extracted","text":"A lightweight transformer fuses multiple fNIRS signal views through shared tokenization to achieve competitive pain recognition on the AI4Pain dataset while staying computationally compact."},{"attestation":"unclaimed","claim_id":"C4","kind":"headline","source":"verdict.pith_extraction.headline","status":"machine_extracted","text":"A lightweight transformer fuses raw and spectral fNIRS signals through unified tokenization to recognize pain states while remaining compact enough for real-time use."}],"snapshot_sha256":"3f747db88a0ea524082529aeae0b7a81113c7b8f4035b30fe9910aabef588d3a"},"formal_canon":{"evidence_count":2,"snapshot_sha256":"6fb8f68efa845abfd49139f899ef9a8364181dac3c529beb5bedd28a420b51d6"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2604.16491/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Pain is a multifaceted and widespread phenomenon with substantial clinical and societal burden, making reliable automated assessment a critical objective. This paper presents a lightweight transformer architecture that fuses multiple fNIRS representations through a unified tokenization mechanism, enabling joint modeling of complementary signal views without requiring modality-specific adaptations or increasing architectural complexity. The proposed token-mixing strategy preserves spatial, temporal, and time-frequency characteristics by projecting heterogeneous inputs onto a shared latent repre","authors_text":"Christian Arzate Cruz, Giorgos Giannakakis, Lu Cao, Muhammad Umar Khan, Randy Gomez, Raul Fernandez Rojas, Stefanos Gkikas, Thomas Kassiotis, Yu Fang","cross_cats":["cs.AI"],"headline":"A lightweight transformer fuses raw and spectral fNIRS signals through unified tokenization to recognize pain states while remaining compact enough for real-time use.","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2026-04-13T13:25:19Z","title":"A Lightweight Transformer for Pain Recognition from Brain Activity"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2604.16491","kind":"arxiv","version":4},"verdict":{"created_at":"2026-05-13T06:39:25.956084Z","id":"950b894c-a98c-474d-b9f8-aaf820bae837","model_set":{"reader":"grok-4.3"},"one_line_summary":"A lightweight transformer fuses multiple fNIRS signal views through shared tokenization to achieve competitive pain recognition on the AI4Pain dataset while staying computationally compact.","pipeline_version":"pith-pipeline@v0.9.0","pith_extraction_headline":"A lightweight transformer fuses raw and spectral fNIRS signals through unified tokenization to recognize pain states while remaining compact enough for real-time use.","strongest_claim":"The proposed lightweight transformer fuses multiple fNIRS representations through a unified tokenization mechanism, enabling joint modeling of complementary signal views without requiring modality-specific adaptations or increasing architectural complexity, while achieving competitive pain recognition performance on the AI4Pain dataset.","weakest_assumption":"That projecting heterogeneous fNIRS inputs (raw waveform and power spectral density) onto a shared latent representation via structured segmentation preserves all spatial, temporal, and time-frequency information necessary for accurate pain classification without significant loss."}},"verdict_id":"950b894c-a98c-474d-b9f8-aaf820bae837"}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:9addfb3ce6e428eb0edfc5824af39d70def6a2d299180eb69a7795352d50503d","target":"record","created_at":"2026-05-20T00:03:11Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"50dcabc8d97ceddfd0de7cb691f60572e531e4e9d3ec090485d6d805122b64fa","cross_cats_sorted":["cs.AI"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2026-04-13T13:25:19Z","title_canon_sha256":"35c04e90079dd0e6b8d88c7a08db2bf51e2bc1403ab0f2f043046a376a13ad15"},"schema_version":"1.0","source":{"id":"2604.16491","kind":"arxiv","version":4}},"canonical_sha256":"1a8731dfd644d3b234d5918d446dfadee2ceb00d04a8555514b39643ef09931a","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"1a8731dfd644d3b234d5918d446dfadee2ceb00d04a8555514b39643ef09931a","first_computed_at":"2026-05-20T00:03:11.603695Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-20T00:03:11.603695Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"FN4SaoihpHXrRDN1+64n4LxHuolbJ/qmwppaPyd5bmj3JTSmp8h6CEOJ7kAjatCywFHPik+ASjvZdzd6Cjw7Aw==","signature_status":"signed_v1","signed_at":"2026-05-20T00:03:11.604497Z","signed_message":"canonical_sha256_bytes"},"source_id":"2604.16491","source_kind":"arxiv","source_version":4}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:9addfb3ce6e428eb0edfc5824af39d70def6a2d299180eb69a7795352d50503d","sha256:308089fa89f448dd5d8b2270d22a231ae5f31a7a1f2baee9b7a393d3b7f628f7"],"state_sha256":"6a92af3374f1a1cd1d32ff844974c0701c857586506b4fe20e4324c257918680"}