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Tao-Yong Cui

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Open access Sep 2026

InstructPLM-mu: protein fitness prediction from structure instructions

Abstract Multimodal protein language models (PLMs) deliver strong performance on mutation-effect prediction, but training such models from scratch demands substantial computational resources. In this paper, we propose a fine-tuning framework called InstructPLM-mu and investigate the question: Can a pretrained sequence-...

Jun-De Xu, Ya-Pin Shi, Li-Jun Lang et al. · 0 citations
Preprint Aug 2026

Support Operation Factorization: Compositional Readout of Frozen Vision Encoders under Controlled Interventions

An injectively aligned leave-one-cell-out protocol over support x operation grids and SO-OPF, a readout that factors cell energy into support salience and a competitive operation posterior, and a readout that factors cell energy into support salience and a competitive operation posterior are introduced.

Zhong-Yao Wang, Wan-Li Ouyang, Tao-Yong Cui et al. · 0 citations
#natural language process... Preprint Sep 2026

JEPA-Anything: Learning Predictive Models across Different Worlds

JEPA-Anything, a domain-agnostic framework based on orthogonal predictive factorization (OPF), achieves the lowest one-step and 100-step molecular errors among compared methods in all four systems and supports a common factorized predictive principle across heterogeneous worlds.

Tao-Yong Cui, Zhong-Yao Wang, Xin-Yue Xu et al. · 2 citations
#machine learning Preprint Sep 2026

An immune world model for multiscale forecasting and therapeutic hypothesis generation

Immune therapies act across cell-intrinsic programs, tissue ecosystems, and patient-specific immune states, yet most predictors address these scales separately. We used a governed evolutionary AI Scientist to construct the Immune World Model, an action-conditioned model that learns how interventions move immune states...

Tao-Yong Cui, Xi Wang, Zong-Hang Li et al. · 0 citations
#machine learning Preprint Aug 2026

Orthogonal JEPA: Factorized Predictive States for Latent World Models

Method, a latent world-modeling framework based on orthogonal predictive factorization, is introduced, a latent world-modeling framework based on orthogonal predictive factorization that can be used by a readout, decoder, planner, or autoregressive rollout of an underlying system.

Tao-Yong Cui, Pheng-Ann Heng, Wan-Li Ouyang · 0 citations

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