المنشورات العلمية

1. 2026

   VehAnchor: Metadata-Free Metric Scale Recovery from Vehicle Cues in Aerial Imagery

   Yifei Chen, Chenqian Le, Jiayi Cheng, Xupeng Chen

   المؤلف الأول: Yifei Chen (مهندس خوارزميات) · المؤلف المراسل: Xupeng Chen · انتماء كليهما: Dimension Gate

   IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)IROS 2026 · AcceptedarXiv:2603.04277

   A deterministic geometric perception skill for embodied agents: detect common vehicles in metadata-free aerial imagery, recover ground sample distance, and return a confidence score before metric decisions are made. Yifei Chen is first author; Xupeng Chen is corresponding author. Both are affiliated with Dimension Gate.

2. 2024Top 5% of all research outputs (Altmetric)

   A neural speech decoding framework leveraging deep learning and speech synthesis

   Xupeng Chen, Ran Wang, Amirhossein Khalilian-Gourtani, Leyao Yu, Patricia Dugan, Daniel Friedman, Werner Doyle, Orrin Devinsky, Yao Wang, Adeen Flinker

   Nature Machine IntelligenceVol. 6, No. 4, pp. 467–480doi:10.1038/s42256-024-00824-8

   An ECoG decoder that translates cortical signals into interpretable speech parameters, paired with a differentiable speech synthesizer. Reproducible across 48 participants; the 3D ResNet decoder reaches PCC 0.804 against the original spectrogram, and stays high under causal-only operation as required for real-time neural prostheses.

   

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3. 2026

   Why Does Grounding Hurt Medical VQA? Benchmarking, Diagnosis, and Fine-Tuning of Vision-Language Models

   Xupeng Chen, Binbin Shi, Chenqian Le, Qifu Yin, Lang Lin, Haowei Ni, Ran Gong, Panfeng Li

   arXiv preprintarXiv:2604.27720

   Asks why telling a vision-language model where to look often makes its answer worse. Benchmarks frontier VLMs on medical grounding, separates the two failure modes — the box lands in the wrong place, or the box is right but cropping to it throws away the context the answer needed — and shows what fine-tuning does and does not fix.

4. 2026

   Iterative Multimodal Retrieval-Augmented Generation for Medical Question Answering

   Xupeng Chen, Binbin Shi, Chenqian Le, Jiaqi Zhang, Kewen Wang, Ran Gong, Jinhan Zhang, Chihang Wang

   arXiv preprintarXiv:2604.27724

   MedVRAG retrieves over ~350K page images from the literature rather than over plain text, then decides for itself whether the evidence it pulled is enough — and goes back for more if it is not. Most questions settle in one round; the hard ones are exactly the ones that need a second and third.

5. 2026

   Comparison of sEMG Encoding Accuracy Across Speech Modes Using Articulatory and Phoneme Features

   Chenqian Le, Ruisi Li, Beatrice Fumagalli, Yasamin Esmaeili, Xupeng Chen, Amirhossein Khalilian-Gourtani, Tianyu He, Adeen Flinker, Yao Wang

   arXiv preprintarXiv:2604.18920

   Surface EMG from the face and neck, compared across spoken, mimed and silently imagined speech. Asks how much of the articulatory signal survives when no sound is produced — the question any silent-speech interface has to answer before it can work.

6. 2025

   VoxelFormer: Parameter-Efficient Multi-Subject Visual Decoding from fMRI

   Chenqian Le, Yilin Zhao, Nikasadat Emami, Kushagra Yadav, Xujin "Chris" Liu, Xupeng Chen, Yao Wang

   arXiv preprintarXiv:2509.09015

   Decodes what a person is looking at from fMRI, with one model shared across subjects instead of one model per brain. A token-merging encoder maps voxels into a CLIP embedding space, so adding a new subject costs a fraction of the parameters a per-subject model would.

7. 2025

   Machine Learning-Based Prediction of Speech Arrest During Direct Cortical Stimulation Mapping

   Nikasadat Emami, Amirhossein Khalilian-Gourtani, Jianghao Qian, Antoine Ratouchniak, Xupeng Chen, Yao Wang, Adeen Flinker

   arXiv preprintarXiv:2509.08703

   Predicts which cortical sites will arrest speech when stimulated — the mapping a surgeon currently has to establish by stimulating the awake patient site by site. Same signal, read ahead of time instead of found the hard way.

8. 2025

   When Thinking Fails: The Pitfalls of Reasoning for Instruction-Following in LLMs

   Xiaomin Li, Zhou Yu, Zhiwei Zhang, Xupeng Chen, Ziji Zhang, Yingying Zhuang, Narayanan Sadagopan, Anurag Beniwal

   arXiv preprintarXiv:2505.11423

   Chain-of-thought prompting is supposed to make models better. On instruction following it frequently makes them worse: the model reasons its way past a constraint it would have obeyed if asked directly. Measures where the trade-off bites and what mitigates it.

9. 2025

   Transformer-based neural speech decoding from surface and depth electrode signals

   Junbo Chen, Xupeng Chen, Ran Wang, Chenqian Le, Amirhossein Khalilian-Gourtani, Erika Jensen, Patricia Dugan, Werner Doyle, Orrin Devinsky, Daniel Friedman, Adeen Flinker, Yao Wang

   Journal of Neural EngineeringVol. 22, No. 1, 016017doi:10.1088/1741-2552/adab21

   SwinTW, a transformer that works with arbitrarily positioned electrodes by using their 3D cortical coordinates rather than a 2D grid. Subject-specific models on low-density 8×8 ECoG reach PCC 0.817 across 43 participants, freeing future speech prostheses from grid-only electrode layouts.

10. 2024

    R-LLaVA: Improving Med-VQA Understanding through Visual Region of Interest

    Xupeng Chen, Zhixin Lai, Kangrui Ruan, Shichu Chen, Jiaxiang Liu, Zuozhu Liu

    arXiv preprintarXiv:2410.20327

    Injects the region a clinician would actually look at straight into the visual token stream, so the model reasons over the lesion instead of over the whole slide. The line of work that the 2026 grounding benchmark above grew out of, and argued with.

11. 2024

    A corollary discharge circuit in human speech

    Amirhossein Khalilian-Gourtani, Ran Wang, Xupeng Chen, Leyao Yu, Patricia Dugan, Daniel Friedman, Werner Doyle, Orrin Devinsky, Yao Wang, Adeen Flinker

    Proceedings of the National Academy of Sciences (PNAS)Vol. 121, No. 50, e2404121121doi:10.1073/pnas.2404121121

    Identifies a reproducible corollary discharge source in ventral speech motor cortex that fires before articulation and predicts how strongly auditory cortex is suppressed during speech — how the brain tells its own voice apart from the world.

12. 2023

    Distributed feedforward and feedback cortical processing supports human speech production

    Ran Wang, Xupeng Chen, Amirhossein Khalilian-Gourtani, Leyao Yu, Patricia Dugan, Daniel Friedman, Werner Doyle, Orrin Devinsky, Yao Wang, Adeen Flinker

    Proceedings of the National Academy of Sciences (PNAS)Vol. 120, No. 42, e2300255120doi:10.1073/pnas.2300255120

    Uses causal, anticausal and noncausal convolutional architectures to disentangle motor control from sensory processing during speech, revealing a mixed feedforward/feedback recruitment across perisylvian cortex.

13. 2020Best Paper Award Finalist

    Stimulus Speech Decoding From Human Cortex with Generative Adversarial Network Transfer Learning

    Ran Wang, Xupeng Chen, Amirhossein Khalilian-Gourtani, Zhaoxi Chen, Leyao Yu, Adeen Flinker, Yao Wang

    IEEE International Symposium on Biomedical Imaging (ISBI)

    Applies GAN transfer learning to decode heard speech from cortical recordings, addressing the scarcity of paired neural-speech data.

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