1 citations · 4 across the 12 of their papers we have counts for
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Think Twice to See More: Iterative Visual Reasoning in Medical VLMs
Kaitao Chen, Shaohao Rui, Yankai Jiang +6
Medical vision-language models (VLMs) excel at image-text understanding but typically rely on a single-pass reasoning that neglects localized visual cues. In clinical practice, how…
SciReasoner: Laying the Scientific Reasoning Ground Across Disciplines
Yizhou Wang, Chen Tang, Han Deng +29
We present a scientific reasoning foundation model that aligns natural language with heterogeneous scientific representations. The model is pretrained on a 206B-token corpus spanni…
AdaBrain-Bench: Benchmarking Brain Foundation Models for Brain-Computer Interface Applications
Jiamin Wu, Zichen Ren, Junyu Wang +7
Non-invasive Brain-Computer Interfaces (BCI) offer a safe and accessible means of connecting the human brain to external devices, with broad applications in home and clinical setti…
SynBrain: Enhancing Visual-to-fMRI Synthesis via Probabilistic Representation Learning
Weijian Mai, Jiamin Wu, Yu Zhu +6
Deciphering how visual stimuli are transformed into cortical responses is a fundamental challenge in computational neuroscience. This visual-to-neural mapping is inherently a one-t…
CSBrain: A Cross-scale Spatiotemporal Brain Foundation Model for EEG Decoding
Yuchen Zhou, Jiamin Wu, Zichen Ren +7
Understanding and decoding brain activity from electroencephalography (EEG) signals is a fundamental challenge in neuroscience and AI, with applications in cognition, emotion recog…
UniMind: Unleashing the Power of LLMs for Unified Multi-Task Brain Decoding
Weiheng Lu, Zhouheng Yao, Jiamin Wu +6
Decoding human brain activity from electroencephalography (EEG) signals is a central challenge at the intersection of neuroscience and artificial intelligence, enabling diverse app…
PaceLLM: Brain-Inspired Large Language Models for Long-Context Understanding
Kangcong Li, Peng Ye, Chongjun Tu +6
While Large Language Models (LLMs) demonstrate strong performance across domains, their long-context capabilities are limited by transient neural activations causing information de…
MindAligner: Explicit Brain Functional Alignment for Cross-Subject Visual Decoding from Limited fMRI Data
Yuqin Dai, Zhouheng Yao, Chunfeng Song +7
Brain decoding aims to reconstruct visual perception of human subject from fMRI signals, which is crucial for understanding brain's perception mechanisms. Existing methods are conf…