7 papers
Matryoshka Concept Bottleneck Models
Ziye Chen, Hongbin Lin, Jie Li +1
Concept Bottleneck Models (CBMs) have emerged as a prominent paradigm for interpretable deep learning, learning by grounding predictions in human-understandable concepts. However,…
OEP: Poisoning Self-Evolving LLM Agents via Locally Correct but Non-Transferable Experiences
Kaixiang Wang, Jiong Lou, Zhaojiacheng Zhou +1
Memory-augmented large language model (LLM) agents use iterative reflection and self-evolution to solve complex tasks, but these mechanisms introduce security risks. Existing agent…
CodeBind: Decoupled Representation Learning for Multimodal Alignment with Unified Compositional Codebook
Zeyu Chen, Jie Li, Kai Han
Multimodal representation alignment is pivotal for large language models and robotics. Traditional methods are often hindered by cross-modal information discrepancies and data scar…
PRiSE-EEG: A Prior-Guided Foundation Model with Depth-Stratified Experts for Cross-Paradigm EEG Representation Learning
Wei Xiong, Jiangtong Li, Kun Zhu +2
EEG foundation models aim to learn reusable representations across heterogeneous paradigms, yet existing approaches often use uniform adaptation mechanisms and are typically report…
DynaMind: Reconstructing Dynamic Visual Scenes from EEG by Aligning Temporal Dynamics and Multimodal Semantics to Guided Diffusion
Junxiang Liu, Junming Lin, Jiangtong Li +1
Reconstruction dynamic visual scenes from electroencephalography (EEG) signals remains a primary challenge in brain decoding, limited by the low spatial resolution of EEG, a tempor…
EEG-FM-Bench: A Comprehensive Benchmark for the Systematic Evaluation and Diagnostic Analyses of EEG Foundation Models
Wei Xiong, Jiangtong Li, Jie Li +2
Electroencephalography foundation models (EEG-FMs) have advanced brain signal analysis, but the lack of standardized evaluation benchmarks impedes model comparison and scientific p…