arXiv Machine Learning June 25, 2026 By Navin Bondade

Inverse Reinforcement Learning for Interpretable Keystroke Biomarkers in Parkinson's Disease

Read the original on arXiv Machine Learning →

arXiv:2606. 25270v1 Announce Type: new Abstract: Keystroke dynamics have been explored extensively as a passive digital biomarker for Parkinson's disease (PD), typically by extracting summary statistics from typing timing and training a classifier to discriminate PD from healthy controls.

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

arXiv AI

Jul 29

Selective Impairment of Motor Recovery from Typing Errors in Parkinson's Disease: A Survival Analysis

arXiv:2607. 24796v1 Announce Type: cross Abstract: Parkinson's disease (PD) affects multiple, dissociable stages of motor and cognitive control.

By Navin Bondade

arXiv AI

Jul 28

Stress-Testing EEG Foundation Models for Clinical Decoding: Dataset Identity and Targeted Negative Controls

arXiv:2607. 24519v1 Announce Type: cross Abstract: Pretrained EEG foundation models are increasingly proposed for clinical decoding, but their transfer across populations and robustness to negative controls remain unclear.

By Marzieh Zare

rag fine-tuning benchmarks

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

Jun 5

Policy-Conditioned Counterfactual Credit for Verifiable Reinforcement Learning of Long-Horizon Language Agents

arXiv:2606. 05263v1 Announce Type: new Abstract: Reinforcement learning with verifiable rewards improves reasoning and tool use, yet long-horizon language agents still learn unsupported evidence chains, belief drift, and shortcut actions that satisfy terminal checks.

By Renwei Meng

agents reinforcement-learning

arXiv AI

Aug 7

Is Self-Pretraining really useful to improve diagnosis in medical Time Series?

arXiv:2608. 06122v1 Announce Type: cross Abstract: Inspired by recent evidence that transformer architectures benefit from Self-PreTraining (SPT) on long-context benchmarks, we investigate whether similar gains extend to multimodal, multivariate, and even simple univariate medical time series.

By Omar Coser, Antonio Orvieto, Paolo Soda, Loredana Zollo

llms robotics multimodal benchmarks

arXiv AI

Jun 3

When RLHF Fails: A Mechanistic Taxonomy of Reward Hacking, Collapse, and Evaluator Gaming

arXiv:2606. 03238v1 Announce Type: cross Abstract: Reinforcement learning from human feedback (RLHF) makes large-scale post-training possible by replacing an underspecified human objective with learned and scalable proxies.

By Zelalem Abahana

llms reinforcement-learning safety

Related stories

Selective Impairment of Motor Recovery from Typing Errors in Parkinson's Disease: A Survival Analysis

Stress-Testing EEG Foundation Models for Clinical Decoding: Dataset Identity and Targeted Negative Controls

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

Policy-Conditioned Counterfactual Credit for Verifiable Reinforcement Learning of Long-Horizon Language Agents

Is Self-Pretraining really useful to improve diagnosis in medical Time Series?

When RLHF Fails: A Mechanistic Taxonomy of Reward Hacking, Collapse, and Evaluator Gaming