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Run-Ze Ma

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#artificial intelligence Preprint Oct 2026

La-Ribo: RNA Co-Design via Geometry-Latent Flow Matching

RNA function arises from the coupling of nucleotide sequence and three-dimensional structure, motivating their joint design. Coordinating global folding with nucleotide-level detail remains challenging under limited structural supervision. We introduce La-Ribo, a generative framework for RNA sequence-structure co-desig...

Run-Ze Ma, William Hua, Shuang-Jia Zheng · 0 citations
#machine learning Preprint Sep 2026

TRACE: Tractable Routing Autoencoder for Clinical ECG

TRACE is proposed, a Tractable Routing Autoencoder for Clinical ECG, whose 32-dimensional clinical latent space is specified in advance from domain knowledge rather than discovered by optimization, and reconstructs the ECG through a decoder that permits latent perturbation.

Shun-Bo Jia, Run-Ze Ma, Haonan Lyu et al. · 0 citations

LiteMedCoT-VL: Parameter-Efficient Adaptation for Medical Visual Question Answering

LiteMedCoT-VL is introduced, a pipeline that transfers chain-of-thought reasoning from a 235B teacher model to 2B student models through LoRA-based fine-tuning on explanation-enriched training data, and indicates that a 2B model with reasoning distillation can match or exceed models with twice the parameters.

Run-Ze Ma, Shun-Bo Jia, Haonan Lyu et al. · 0 citations
#artificial intelligence Preprint Jan 2026

BEAT-Net: Injecting Biomimetic Spatio-Temporal Priors for Interpretable ECG Diagnosis

BEAT-Net is presented, a supervised biomimetic framework that integrates QRS-centered biological tokenization with a hierarchical architecture mirroring the cardiologist's workflow, demonstrating that explicit physiological structure provides a more efficient and interpretable alternative to massive pre-training for cl...

Run-Ze Ma, Haonan Lyu, Shun-Bo Jia et al. · 0 citations
Jul 2026

Antigen-specific Antibody Multi-modal Foundation Model for Functional Antibody Design

AAMFM, an Antigen-specific Antibody Multimodal Foundation Model that learns unified representations of antibody sequences and structures conditioned on antigen context, achieves state-of-the-art performance in functional antibody design, revealing its potential for antigen-specific antibody engineering.

Xiaoliang Shi, Zichen Wang, Runze Ma et al. · 0 citations

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