arXiv AI August 5, 2026 By Kunkun Zhang, Qianwei Zhou

Cross-Anesthetic ECoG State Decoding Fails at the Decision Threshold, Not the Representation

arXiv:2608. 02646v1 Announce Type: cross Abstract: Decoders of anesthetic state from cortical activity fail across drug classes, most notoriously ketamine, but reported accuracy cannot say whether the neural representation or only the decision threshold has failed; we separate the two in a controlled preparation with ground-truth labels.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv AI.

fine-tuning

arXiv Machine Learning

Aug 4

What EEG Foundation Models Encode: Dataset Identity and a Negative-Control Suite for Clinical Benchmarks

arXiv:2607. 24519v2 Announce Type: replace Abstract: Pretrained EEG foundation models are proposed for clinical decoding, but whether reported gains transfer across populations or survive negative controls is unclear.

By Marzieh Zare

rag fine-tuning benchmarks

arXiv Machine Learning

Aug 4

Cross-Cohort Spectral-Temporal Dissociation in Frozen EEG Foundation-Model Representations

arXiv:2607. 24834v2 Announce Type: replace-cross Abstract: Objective.

By Marzieh Zare

rag

arXiv AI

2d ago

A Negative-Control Protocol for Clinical EEG Foundation-Model Benchmarks: Dataset Identity and External-Cohort Stress Testing

arXiv:2607. 24519v3 Announce Type: replace-cross Abstract: EEG foundation-model gains may depend on cohort, montage, or probe design.

By Marzieh Zare

fine-tuning benchmarks

arXiv Machine Learning

Jun 8

The Identity Trap in EEG Foundation Models: A Diagnostic Audit

arXiv:2606. 06647v1 Announce Type: new Abstract: Objective.

By Jun-You Lin, Ying Choon Wu, Tzyy-Ping Jung

fine-tuning benchmarks

arXiv AI

Jul 29

Beyond Predictive Accuracy: A Reliability-Aware Audit of Molecular Representations for Human Olfaction

arXiv:2607. 24848v1 Announce Type: cross Abstract: Pretrained molecular encoders are commonly evaluated through downstream prediction, but predictive accuracy alone does not establish that a learned representation captures reproducible scientific structure, adds information beyond strong conventional baselines, or transfers out of distribution.

By Kai Lun Huang (California State University, Fullerton), Wei Chieh Sun (University of Washington)

rag safety

arXiv AI

Jul 29

Foundation Models for EEG Are Blind to Long-Range Temporal Correlations: A Spectral-Temporal Dissociation Behind Their Cross-Population Fragility

arXiv:2607. 24834v1 Announce Type: cross Abstract: Objective.

By Marzieh Zare

rag

Related stories

What EEG Foundation Models Encode: Dataset Identity and a Negative-Control Suite for Clinical Benchmarks

Cross-Cohort Spectral-Temporal Dissociation in Frozen EEG Foundation-Model Representations

A Negative-Control Protocol for Clinical EEG Foundation-Model Benchmarks: Dataset Identity and External-Cohort Stress Testing

The Identity Trap in EEG Foundation Models: A Diagnostic Audit

Beyond Predictive Accuracy: A Reliability-Aware Audit of Molecular Representations for Human Olfaction

Foundation Models for EEG Are Blind to Long-Range Temporal Correlations: A Spectral-Temporal Dissociation Behind Their Cross-Population Fragility