{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:F6YTX6ALANPPHHLKHHSGWSJ464","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":"f0c486fcacaa1c5736423dcc847b4940031ad3a0cc328c0d02c64411e2398b58","cross_cats_sorted":["cs.AI","cs.HC"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2026-05-17T17:47:00Z","title_canon_sha256":"3007de5ed06b41d2e1c06827410fbf1dfc7b9383f6d5d0b596757831b392bbb5"},"schema_version":"1.0","source":{"id":"2605.17562","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2605.17562","created_at":"2026-05-20T00:04:46Z"},{"alias_kind":"arxiv_version","alias_value":"2605.17562v1","created_at":"2026-05-20T00:04:46Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.17562","created_at":"2026-05-20T00:04:46Z"},{"alias_kind":"pith_short_12","alias_value":"F6YTX6ALANPP","created_at":"2026-05-20T00:04:46Z"},{"alias_kind":"pith_short_16","alias_value":"F6YTX6ALANPPHHLK","created_at":"2026-05-20T00:04:46Z"},{"alias_kind":"pith_short_8","alias_value":"F6YTX6AL","created_at":"2026-05-20T00:04:46Z"}],"graph_snapshots":[{"event_id":"sha256:ac3e22022da1ebaaadad1a4fd0b169632a6be4b5f7122a88a485f76d46d20264","target":"graph","created_at":"2026-05-20T00:04:46Z","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":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[{"findings_count":0,"name":"ai_meta_artifact","ran_at":"2026-05-19T21:33:23.600499Z","status":"skipped","version":"1.0.0"},{"findings_count":0,"name":"claim_evidence","ran_at":"2026-05-19T21:21:57.532991Z","status":"completed","version":"1.0.0"}],"endpoint":"/pith/2605.17562/integrity.json","findings":[],"snapshot_sha256":"8a1002aa7b2a9b5b99311b3cd3d059f75f5916356f450c029a125bde8457077b","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"EEG foundation models (EEG-FMs) have been evaluated predominantly on clean, in-distribution accuracy, leaving their robustness, interpretability and representational quality largely unexamined. This study addresses these gaps by benchmarking six EEG-FMs against a baseline deep learning model across eight datasets. Beyond clean accuracy, we conduct three layers of analysis: (i) Robustness: we apply test-time perturbations including additive noise, random and region-based channel dropout and region-specific noise injection. Our analyses show that no single model dominates all failure modes. The ","authors_text":"Konstantinos Barmpas, Maryam Alimardani, Stefanos Zafeiriou, Urban \\v{S}irca","cross_cats":["cs.AI","cs.HC"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2026-05-17T17:47:00Z","title":"Beyond Accuracy: Robustness, Interpretability and Expressiveness of EEG Foundation Models"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2605.17562","kind":"arxiv","version":1},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:a0887ee189b6fa2dace0ba5786008453cf4ce8a88480ad558da74d15670753ef","target":"record","created_at":"2026-05-20T00:04:46Z","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":"f0c486fcacaa1c5736423dcc847b4940031ad3a0cc328c0d02c64411e2398b58","cross_cats_sorted":["cs.AI","cs.HC"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2026-05-17T17:47:00Z","title_canon_sha256":"3007de5ed06b41d2e1c06827410fbf1dfc7b9383f6d5d0b596757831b392bbb5"},"schema_version":"1.0","source":{"id":"2605.17562","kind":"arxiv","version":1}},"canonical_sha256":"2fb13bf80b035ef39d6a39e46b493cf70ad4f10eac8bd2899f1a4edd7b88cc4d","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"2fb13bf80b035ef39d6a39e46b493cf70ad4f10eac8bd2899f1a4edd7b88cc4d","first_computed_at":"2026-05-20T00:04:46.059159Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-20T00:04:46.059159Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"6myukCJMrnA0bUnX2dA01mCZJZ4x0Tr0clApdcJj4pEZjW6UQaPFsiHWZvcMNBuiKto01yBeG9KwF6xCGC/RAw==","signature_status":"signed_v1","signed_at":"2026-05-20T00:04:46.060031Z","signed_message":"canonical_sha256_bytes"},"source_id":"2605.17562","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:a0887ee189b6fa2dace0ba5786008453cf4ce8a88480ad558da74d15670753ef","sha256:ac3e22022da1ebaaadad1a4fd0b169632a6be4b5f7122a88a485f76d46d20264"],"state_sha256":"935ee8d7225f1253b622dafd1fadf3566d8dc9f4c79bf6db1d80faf2a267f901"}