{"_canonicalization":{"envelope_id":"axm_ + sha256(envelope minus {signature, axiom_id, anchors})","envelope_signature":"ed25519(envelope minus {signature, axiom_id})","json":"sort_keys=True, separators=(',',':'), ensure_ascii=False, allow_nan=False, utf-8","leaf_hash":"sha256(0x00 || canonical_json(envelope_full))","seal_signature":"ed25519(seal minus {signature, sig_algorithm})"},"axiom_id":"axm_31f3426ee760e7c3f0608375bf1ec078ea7d688127378c34a151acca5b184204","bitcoin_anchor":{"bitcoin_attestations":[],"calendar_attestations":[],"ots_url":"","stamped_at":"","status":"pending_next_stamp"},"envelope":{"anchors":[{"chain":"crovia.axiom_graph","height":0,"merkle_proof":"spider_vendor_press_v1","root_at_anchor":"spider_vendor_press_v1"}],"axiom_id":"axm_31f3426ee760e7c3f0608375bf1ec078ea7d688127378c34a151acca5b184204","axiom_type":"AX.OBS","body":{"axiom_subtype":"news.vendor_press.v1","category":"news","fingerprint":"5c837f36bc6f13f6a63a9bf2286d99d28a359b0026fdeb412d188cfaa7ec7ef3","published":"Tue, 28 Jul 2026 00:00:00 -0400","receipt_hash":"5c837f36bc6f13f6a63a9bf2286d99d28a359b0026fdeb412d188cfaa7ec7ef3","schema":"spider.news.vendor_press.v1","spider":"vendor_press","spider_record":{"axiom_subtype":"news.vendor_press.v1","category":"news","decision_hint":"POSITIVE","envelope_target":"AX.OBS","fingerprint":"5c837f36bc6f13f6a63a9bf2286d99d28a359b0026fdeb412d188cfaa7ec7ef3","observed_at":"2026-07-28T04:43:08.282317Z","parent_run_hash":"23a1ef85134515049ced29518443d084afc46fd7c967741e6c6acdbdbbf29939","published":"Tue, 28 Jul 2026 00:00:00 -0400","runtime_version":"0.1.0","schema":"spider.news.vendor_press.v1","source_status":200,"source_url":"https://export.arxiv.org/rss/cs.AI","spider":"vendor_press","summary_excerpt":"arXiv:2607.24519v1 Announce Type: cross \nAbstract: Pretrained EEG foundation models are increasingly proposed for clinical decoding, but their transfer across populations and robustness to negative controls remain unclear. We benchmark six models (LaBraM, EEGMamba, CBraMod, REVE, BENDR, and BIOT) on five clinical tasks across four datasets using frozen linear probes with leave-one-subject-out, subject-grouped, or explicitly identified recording-level splits. Selected REVE findings are tested against random initialisation, random features, label permutation, scrambled-label fine-tuning, and projection sensitivity. On Korean dementia (CAUEEG, three-way), frozen REVE reaches 0.568 AUROC versus 0.769 for classical features; the ordering persists on a patient-disjoint held-out split (0.565 versus 0.768). Dataset identity is readily decoded from frozen embeddings (AUROC 1.000 at PCA-50; 0.9998 after band restriction and per-epoch z-scoring), whereas the same PCA-50 pipeline decodes Korean di","title":"Stress-Testing EEG Foundation Models for Clinical Decoding: Dataset Identity and Targeted Negative Controls","url":"https://arxiv.org/abs/2607.24519","vendor":"arxiv_cs_ai"},"summary":"arXiv:2607.24519v1 Announce Type: cross \nAbstract: Pretrained EEG foundation models are increasingly proposed for clinical decoding, but their transfer across populations and robustness to negative controls remain unclear. We benchmark six models (LaBraM, EEGMamba, CBraMod, REVE, BENDR, and BIOT) on five clinical tasks across four datasets using frozen linear probes with leave-one-subject-out, subject-grouped, or explicitly identified recording-level splits. Selected REVE findings are tested against random initialisation, random features, label permutation, scrambled-label fine-tuning, and projection sensitivity. On Korean dementia (CAUEEG, three-way), frozen REVE reaches 0.568 AUROC versus 0.769 for classical features; the ordering persists on a patient-disjoint held-out split (0.565 versus 0.768). Dataset identity is readily decoded from frozen embeddings (AUROC 1.000 at PCA-50; 0.9998 after band restriction and per-epoch z-scoring), whereas the same PCA-50 pipeline decodes Korean di","title":"Stress-Testing EEG Foundation Models for Clinical Decoding: Dataset Identity and Targeted Negative Controls","vendor":"arxiv_cs_ai"},"confidence":{"method":"deterministic"},"decision":"POSITIVE","issued_at":"2026-07-28T04:43:08Z","notes":"Spider vendor_press (news) news.vendor_press.v1","object":{"captured_by":"crovia.spider.vendor_press","primary_source_url":"https://arxiv.org/abs/2607.24519"},"predecessors":[],"schema":"crovia.axiom.v1","signature":"ed25519:87765f50a7419b6242bd32ca9ba8c3aab1294df67d1501d06e7b4a88ff821e14d25a2eefae6262235aa03824cc02208999f45eb035a95a7254a45327931d2206","signer":"crovia.substrate","subject":{"observed_at":"2026-07-28T04:43:08Z","source_collector":"spider:vendor_press","target_id":"https://arxiv.org/abs/2607.24519"},"tsa":{"authority":"crovia.substrate.bootstrap","rfc3161_token":"{\"kind\":\"crovia.bootstrap.tsa\",\"source_jsonl\":\"/opt/crovia/spider/data/news/vendor_press_v1.jsonl\",\"source_seal_merkle_root\":\"spider_vendor_press_v1\",\"upgrade_path\":\"Sessione H \\u2014 OpenTimestamps weekly anchor\"}"},"zk_mode":"clear","zk_proof":null},"ledger":{"leaf_hash":"c0da51b64351686ea64051353c71b47e8317fc30184aea90d0cd5fd4b9c503d3","leaf_index":360692,"ledger_path":"/opt/crovia/substrate/axiom_ledger.jsonl"},"merkle_proof":{"hash_alg":"sha256","leaf_prefix":"0x00","node_prefix":"0x01","odd_leaf_rule":"duplicate_last","path":[{"sibling":"49abe5a7445ed273795b970ba37e02903d85a5f190639a8b1913151e1db10baa","side":"right"},{"sibling":"05236858e11334b57344f8a1e09d0f58620f643f611fd28f67faf683f3ba05bd","side":"right"},{"sibling":"94bda9b68b3405df7400defc540c3e093cb247038af5c7f979c10a563b687bd3","side":"left"},{"sibling":"a8212fa528f60db3b441bd6649407f7f754b90f1ca96ecc38edbc75f314239fe","side":"right"},{"sibling":"445bd19d6f857bb3182acfddf12e17fc3e71382fe17f192f65ac2e6e277b708a","side":"left"},{"sibling":"8126fca9465f40046c62a130e39d35f26b4a21b5d36db9adb24de692af1207b2","side":"left"},{"sibling":"9f375fc8a084716711a7be00248cf68ffa1f4a9755be4f482aa761dcf36b15f9","side":"left"},{"sibling":"43776f432cc587f28ede8eaf5e1d4a4c47951623abe35c898f8f1127bf5ff870","side":"left"},{"sibling":"eb79f1d599c07786d2268140481af8b617999ecc5688c6283fe58e5e6f3a2640","side":"right"},{"sibling":"fe03c6d0b083c4097049d7fc6d7d08dfdb05d9c198b2786336689712d66eabf0","side":"right"},{"sibling":"590ea76fdfc1b9e8072055c378be3182f916709e8d06bd955232e650a7188b86","side":"right"},{"sibling":"d92781c59301ffd5bfb0bad75d9fdf6d73879f715518149d383f7362213e0daf","side":"right"},{"sibling":"f315303d4402b57497416d48eb4c4bb50405b40862d41c7caf318cb3d29c5237","side":"right"},{"sibling":"36973eb5f586cd67e0c0dc055dd87e734aa544c35d4400d2c9f932f8ef8fb27f","side":"right"},{"sibling":"e39f7900355489c4718b21cc2d3d06382e1d2f22b864d10be9ebc9d498b279a1","side":"right"},{"sibling":"1f9a970b25dd938c98cabc9e0a55c5a6f46292b9fa37cc89d90ef0cbb1e05a8c","side":"left"},{"sibling":"77025bcb374a7ad74f520643e20a8ae1205a7ee78507b0beb93117760f1c29d3","side":"left"},{"sibling":"3b50864499c874394ea0928567747666eaf59b01380e46cd52164ec5acec0f71","side":"right"},{"sibling":"1cecb7f447febd025aac272837c80de218aecc6485d2395a509b2a1f1b9c746e","side":"left"}]},"schema":"crovia.axiom_proof.v1","seal":{"first_collector_run_id":"","first_receipt_hash":"","jsonl_path":"/opt/crovia/substrate/axiom_ledger.jsonl","key_id":"430895f101d38164","last_collector_run_id":"","last_receipt_hash":"","leaf_count":361008,"merkle_root":"3065e8369ea437c06beba806dc4e4bb159979adeb21fe632242c1906a7204647","public_key_hex":"cf742e26f75669dc673cb5c0786a1ae23ae8ca19c347317192ce40c28a7ff25c","run_id":"hourly_json_retrofit_20260728T053701Z","schema":"crovia.seal.v1","seal_family_version":"crovia-seal-family/1","seal_kind":"substrate_batch","sealed_at":"2026-07-28T05:38:48Z","sig_algorithm":"ed25519","signature":"9141644407577a82611c1579110f667de2d46dc6b93c6322edf26f4c3056ea99f0e56502853908e30d87c38bcf95eb6e0ab5130525aa51505bd6f61938120609","signer_version":"1.1.0"},"trust_root":{"key_id":"430895f101d38164","public_key_hex":"cf742e26f75669dc673cb5c0786a1ae23ae8ca19c347317192ce40c28a7ff25c","signature_algorithm":"ed25519","url":"/registry/canon/TRUST_ROOT.md"},"verifier":{"spec":"/registry/canon/AXIOM_RECEIPT_v1.md","url":"/v/axm_31f3426ee760e7c3f0608375bf1ec078ea7d688127378c34a151acca5b184204"}}