Skip to content

Similar papers

#artificial intelligence Preprint Aug 2026

ReWEIGH the Evidence: Calibrating Token-Level Ordinal Visual Evidence to Mitigate Hallucinations in Large Vision-Language Models

ReWEIGH is a training-free decoding intervention that aggregates vocabulary ranks across visual positions and compares each candidate with a token-specific reference estimated from unlabeled images and applies a bounded penalty only to candidates that fall below their reference.

Jihae Jeong, Jun-Ha Choi, Hwanjo Yu · 0 citations
#machine learning Preprint Sep 2026

Matched-Input Estimates Differ in Sign Across Architectures: Auditing EEG Foundation Models on Motor Imagery

Sign differences suggest that a single comparator may not provide an architecture-invariant decomposition of a pretrained-versus-supervised performance gap, and validation-fitted temperature scaling returns foundation-model calibration error to the supervised range despite substantially lower four-class accuracy.

Ke-Tian Zhou, Sparsh Roy · 0 citations
#machine learning Preprint Sep 2026

When Do Attention-Head Ablations Support Causal Claims? Projection-Level Confounds, Floor Effects, and Matched Controls

Attention-head ablation, zeroing a head and measuring the resulting change in task performance, is a common method for inferring which components of a language model are causally responsible for a behavior. We show using GPT-2 small that this inference can be fragile unless the intervention semantics, evaluation metric...

Ju-Li Huang · 0 citations
Open access Sep 2026

Decoding speech imagery or just noise?: a symptom of the replicability crisis

Objective. Speech imagery (SI) has emerged as a promising paradigm for brain-computer interface (BCI) control, attracting growing interest due to its intuitive nature–allowing users to interact with the system by internally saying a command. In this study, we investigate the replicability and reproducibility of SI deco...

A. Tates, Ana Matran-Fernandez, S. Halder et al. · 0 citations
Open access Sep 2026

Individual Differences in Neural Decoding Are Stable but Instrument Dependent

Some participants are consistently easier to decode than others. We asked whether this difference belongs to the person or to the conditions under which the brain is measured. We compared visual decoding in EEG, MEG, intracranial EEG and fMRI, testing classifiers on runs and stimulus identities excluded from training....

Mohammed Amin Miri, Sara Bonyadian, Rachid Hedjam et al. · 0 citations
#machine learning Preprint Oct 2026

Benchmarking Label-Revealed Online Updates for EEG BCI Decoding

Electroencephalography (EEG) signals drift over time, which can cause static brain-computer interface (BCI) models to degrade in practice. We present a benchmark for online adaptation and compare two widely used pipeline families, Common Spatial Patterns (CSP) and Riemannian covariance-based methods, under time-ordered...

B. Kozyrskiy, Artem M. Grachev, Abraham Camelo-Guerrero · 0 citations

We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.