9 papers
Control-Diverse Reinforcement Fine-Tuning: Decoupling the Shared Control Bottleneck of RL Post-Training
Binwen Tan, Jingchao Wang, Dengzhe Hou +6
Reinforcement learning post-training unlocks complex reasoning in LLMs. Yet benchmark scores reveal only whether a model improved, not what changed inside it, nor how it splits fin…
CogEEGAgent: Toward Autonomous Cognitive EEG Analysis with Grounded Execution and Selection-Aware Verification
Dengzhe Hou, Lingyu Jiang, Fangzhou Lin +1
Electroencephalography (EEG) analysis in cognitive studies requires specialized expertise and involves many defensible choices over contrasts, channels, time windows, and statistic…
CogArena: A Multimethod Evaluation of Cognitive Ability Structure in Large Language Models
Dengzhe Hou, Lingyu Jiang, Fangzhou Lin +1
LLM cognitive scores are increasingly summarized as per-ability profiles whose dimensions should converge across tasks, respond selectively to matched interventions, and generalize…
PathCal: State-Aware Reflection-Marker Calibration for Efficient Reasoning
Lingyu Jiang, Zirui Li, Shuo Xing +6
The emergence of Large Reasoning Language Models (LRMs) has paved the way for tackling complex reasoning tasks through test-time scaling by generating long-form Chain-of-Thought (C…
Subject-Specific Analysis of Self-Initiated Attention Shifts from EEG with Controlled Internal and External Attention Conditions
Yuwen Zeng, Dengzhe Hou, Zhang Zhang +4
Self-initiated attention shifts play a critical role in voluntary behavior but are difficult to study due to the absence of explicit temporal markers. While previous studies have e…
Same Brain, Different Prediction: How Preprocessing Choices Undermine EEG Decoding Reliability
Dengzhe Hou, Zihao Wu, Lingyu Jiang +3
Electroencephalography (EEG) is a cornerstone of brain-computer interfaces and clinical neuroscience, yet deep learning models are typically trained and evaluated under a single, u…