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Huiqun Yu

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5 papers

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EAAI Journal 2026 Journal Article

Trustworthy distributed mirror learning for secure and private multi-agent coordination

Suhang Wei
Jinfang Jia
Xiang Feng
Huiqun Yu

Real-world deployment of Multi-Agent Reinforcement Learning (MARL) in Internet of Things (IoT) systems requires convergence, verifiability, and privacy to be jointly guaranteed, a capability absent in current approaches. While Multi-Agent Trust Region Learning (MATRL) ensures Nash equilibrium convergence, it risks data exposure and malicious attacks. To address this, we propose Trustworthy Distributed Mirror Learning (TDML), the first method to unify convergence, verifiability, and privacy in MARL. TDML theoretically breaks MATRL’s centralized architecture into agent-local learning and inter-agent communication. This allows key data to be secured with advanced techniques without compromising the theoretical properties of trust-region learning. Specifically, TDML introduces three core innovations: (1) an information functional that unifies all communication behaviors in distributed MATRL and enables flexible integration of security mechanisms; (2) split advantage computation, which decouples raw inputs from global advantages via intermediate representations to protect local data privacy; and (3) a security scheme that ensures verifiable message exchange by attaching zero-knowledge proofs to inter-agent communications. We prove TDML converges to a Nash equilibrium while providing verifiability and privacy guarantees. More importantly, it constructs a mirror space for trustworthy MARL, where derivative algorithms inherit these theoretical guarantees. Experiments show TDML outperforms state-of-the-art methods, improving attack resilience by up to 76% (sign-flipping attack) and achieving a 90+% privacy reconstruction error, while reducing communication overhead by up to 99% compared to homomorphic encryption. TDML establishes a foundational framework for trustworthy MARL, from which derivative algorithms inherit core guarantees for secure, real-world IoT deployment.

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EAAI Journal 2025 Journal Article

A safe multi-agent reinforcement learning algorithm using constraint update projection approach

Yang Liu
Xiang Feng
Huiqun Yu

Traditional reinforcement learning has a major limitation, which is that they optimize agent’s policy purely for maximizing rewards. It completely ignore safety considerations. However, in certain critical engineering fields, ensuring safety is of utmost importance, otherwise it can cause incalculable losses. Therefore, this paper proposes a safe Multi-Agent Constrained Update Projection(MACUP) algorithm, which can safely control agents to complete tasks. We solve this problem from the perspective of policy constraint optimization. Firstly, we derive the new bounds of multi-agent policy performance difference based on a tighter general policy performance difference. It contains generalized advantage estimates, and we utilize these bounds as surrogate functions concerning the objective and constraints. Secondly, to address the coordination issue among multiple agents, we employ a multi-agent sequential policy update framework. Finally, we use a projection method to optimize policies, which has low computational complexity and does not require convex approximation of the surrogate function for solving. It can help us reduce errors. Finally, we have validated our algorithm in two different multi-agent safety environments, and the results show that it is able to satisfy safety constraints while achieving higher rewards.

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NeurIPS Conference 2025 Conference Paper

Venus-MAXWELL: Efficient Learning of Protein-Mutation Stability Landscapes using Protein Language Models

Yuanxi Yu
Fan Jiang
Xinzhu Ma
Liang Zhang
Bozitao Zhong
Wanli Ouyang
Guisheng Fan
Huiqun Yu

In-silico prediction of protein mutant stability, measured by the difference in Gibbs free energy change ($\Delta \Delta G$), is fundamental for protein engineering. Current sequence-to-label methods typically employ two-stage pipelines: (i) encoding mutant sequences using neural networks (e. g. , transformers), followed by (ii) the $\Delta \Delta G$ regression from the latent representations. Although these methods have demonstrated promising performance, their dependence on specialized neural network encoders significantly increases the complexity. Additionally, the requirement to compute latent representations individually for each mutant sequence negatively impacts computational efficiency and poses the risk of overfitting. This work proposes the Venus-MAXWELL framework, which reformulates mutation $\Delta \Delta G$ prediction as a sequence-to-landscape task. In Venus-MAXWELL, mutations of a protein and their corresponding $\Delta \Delta G$ values are organized into a landscape matrix, allowing our framework to learn the $\Delta \Delta G$ landscape of a protein with a single forward and backward pass during training. To this end, we curated a new $\Delta \Delta G$ benchmark dataset with strict controls on data leakage and redundancy to ensure robust evaluation. Leveraging the zero-shot scoring capability of protein language models (PLMs), Venus-MAXWELL effectively utilizes the evolutionary patterns learned by PLMs during pre-training. More importantly, Venus-MAXWELL is compatible with multiple protein language models. For example, when integrated with the ESM-IF, Venus-MAXWELL achieves higher accuracy than ThermoMPNN with 10$\times$ faster in inference speed (despite having 50$\times$ more parameters than ThermoMPNN). The training codes, model weights, and datasets are publicly available at https: //github. com/ai4protein/Venus-MAXWELL.

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EAAI Journal 2024 Journal Article

Few-shot classification via efficient meta-learning with hybrid optimization

Jinfang Jia
Xiang Feng
Huiqun Yu

Meta-learning is one of the important methods to solve the challenging few-shot learning setting by using previous knowledge and experience to guide the learning of new tasks. Model-agnostic meta-learning (MAML) is one of the most popular meta-learning algorithms, and many variants of MAML have appeared in recent years. However, the performance of this algorithm for few-shot classification falls behind some other algorithms working on this problem. Therefore, its generalization performance needs to be further explored and improved. In view of the generalization problem, we found that MAML always shares an initialization in the process of parameter update, ignoring the bias between different tasks, resulting in limited generalization performance. On the other hand, the sample diversity of meta-learning model is low, and shallow network training is generally used, so it is difficult to obtain good performance based on deep neural network models. Based on these problems, we propose a hybrid optimization meta-learning method based on data augmentation, initialization attenuation, and resolution increase, called Mix-MAML. Experimental results show that our method reaches 76. 93% classification accuracy on mini-ImageNet with 100 × 100 resolution, and 83. 62% classification accuracy on CIFAR-FS with 80 × 80 resolution in the 5-way 5-shot settings under ResNet12, which achieves comparable or even better performance than other algorithms in some standard few-shot learning benchmarks without changing MAML simplicity and model-agnostic.

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NeurIPS Conference 2024 Conference Paper

ProSST: Protein Language Modeling with Quantized Structure and Disentangled Attention

Mingchen Li
Yang Tan
Xinzhu Ma
Bozitao Zhong
Huiqun Yu
Ziyi Zhou
Wanli Ouyang
Bingxin Zhou

Protein language models (PLMs) have shown remarkable capabilities in various protein function prediction tasks. However, while protein function is intricately tied to structure, most existing PLMs do not incorporate protein structure information. To address this issue, we introduce ProSST, a Transformer-based protein language model that seamlessly integrates both protein sequences and structures. ProSST incorporates a structure quantization module and a Transformer architecture with disentangled attention. The structure quantization module translates a 3D protein structure into a sequence of discrete tokens by first serializing the protein structure into residue-level local structures and then embeds them into dense vector space. These vectors are then quantized into discrete structure tokens by a pre-trained clustering model. These tokens serve as an effective protein structure representation. Furthermore, ProSST explicitly learns the relationship between protein residue token sequences and structure token sequences through the sequence-structure disentangled attention. We pre-train ProSST on millions of protein structures using a masked language model objective, enabling it to learn comprehensive contextual representations of proteins. To evaluate the proposed ProSST, we conduct extensive experiments on the zero-shot mutation effect prediction and several supervised downstream tasks, where ProSST achieves the state-of-the-art performance among all baselines. Our code and pre-trained models are publicly available.

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