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Yue Deng

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16 papers

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AAAI Conference 2026 Conference Paper

Bi-Spectrum Distillation: Addressing Spectral Mismatch in ANN-SNN Knowledge Transfer

Yuxuan Zhang
Yuhang Sun
Wen Yao
Yue Deng
Hongjue Li

Knowledge distillation from Artificial Neural Networks (ANNs) to Spiking Neural Networks (SNNs) is a prominent training paradigm. However, its efficacy is fundamentally limited by a spectral mismatch: SNNs, with their intrinsic low-pass filtering characteristics, struggle to learn high-frequency details from their ANN teachers, creating a bottleneck in knowledge transfer at both the feature and logit levels. To address this, we propose Bi-Spectrum Distillation (BSD), a novel framework that mitigates the mismatch from two complementary perspectives. First, at the feature level, our Spectral Residual Distillation (SRD) enhances the student SNN's features with a parameter-efficient, learnable filter that adaptively compensates for high-frequency information loss, which transforms the student's output to better match the teacher's rich spectral target. Second, at the logits level, our Spectral Semantic Distillation (SSD) enhances fine-grained classification by distilling high-frequency components from teacher-ordered logits. Extensive experiments on CIFAR-10/100, ImageNet, and CIFAR10-DVS demonstrate that BSD achieves new state-of-the-art performance across both CNN and Transformer-based SNNs, validating its effectiveness and broad applicability.

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AAAI Conference 2026 Conference Paper

Constrained Particle Seeking: Solving Diffusion Inverse Problems with Just Forward Passes

Hongkun Dou
Zike Chen
Zeyu Li
Hongjue Li
Lijun Yang
Yue Deng

Diffusion models have gained prominence as powerful generative tools for solving inverse problems due to their ability to model complex data distributions. However, existing methods typically rely on complete knowledge of the forward observation process to compute gradients for guided sampling, limiting their applicability in scenarios where such information is unavailable. In this work, we introduce *Constrained Particle Seeking (CPS)*, a novel gradient-free approach that leverages all candidate particle information to actively search for the optimal particle while incorporating constraints aligned with high-density regions of the unconditional prior. Unlike previous methods that passively select promising candidates, CPS reformulates the inverse problem as a constrained optimization task, enabling more flexible and efficient particle seeking. We demonstrate that CPS can effectively solve both image and scientific inverse problems, achieving results comparable to gradient-based methods while significantly outperforming gradient-free alternatives.

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AAMAS Conference 2026 Conference Paper

LLM-SMAC: Solving Multi-Agent Decision-Making Tasks via LLM Decision Tree Code Generation

Yue Deng
Weiyu Ma
Yuxin Fan
Ruyi Song
Yin Zhang
Haifeng Zhang
Jian Zhao

StarCraft Multi-Agent Challenge (SMAC) has become a widely used benchmark in multi-agent systems, where agents must control allied units to defeat enemy forces. Traditional MARL methods typically require millions of environment interactions to train parametric policies, which are often non-interpretable and exhibit limited transferability. In this paper, we introduce LLM-SMAC, a closedloop Planner–Coder–Critic framework. Given task descriptions, the LLM planner first generates a decision-tree strategy, which is translated into executable code by the coder. The generated scripts are executed in the environment, and reward signals and runtime feedback are fed back to a critic module for self-reflection and iterative refinement. Through this closed-loop process, this mechanism progressively improves both strategy design and code implementation without large-scale environment exploration. We evaluate our method on the original SMAC tasks and the results show that LLM- SMAC can produce high-quality, interpretable decision trees with minimal interaction, while demonstrating strong transferability across homogeneous SMAC environments without modification.

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AAAI Conference 2026 Conference Paper

Pseudo-Spiking Neurons: A Noise-Based Training Framework for Heterogeneous-Latency Spiking Neural Networks

Yuxuan Zhang
Yuhang Sun
Hongjue Li
Yue Deng
Wen Yao

Spiking Neural Networks (SNNs) promise significant energy efficiency by processing information via sparse, event-driven spikes. However, realizing this potential is hindered by the conventional use of a rigid, uniform timestep, T. This constraint imposes a challenging trade-off between accuracy and latency, while also incurring the prohibitive training costs of Backpropagation Through Time (BPTT). To overcome this limitation, we introduce the Pseudo-Spiking Neuron (PseudoSN), a novel training proxy that conceptualizes latency as an intrinsic, learnable parameter for each neuron. Building on the efficiency of rate-based methods, the PseudoSN models temporal dynamics in a single, BPTT-free pass. It employs a learnable probabilistic noise scheme to emulate the discretization effects of spike generation (e.g., clipping and quantization), making the neuron-specific timestep—and thus latency—directly optimizable via backpropagation. Integrated into a hardware-aware objective, our framework trains heterogeneous-latency SNNs that autonomously learn to optimize the trade-offs among accuracy, latency and energy, establishing a new state-of-the-art on major benchmarks.

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NeurIPS Conference 2025 Conference Paper

IPAD: Inverse Prompt for AI Detection - A Robust and Interpretable LLM-Generated Text Detector

Zheng Chen
Yushi Feng
Jisheng Dang
Changyang He
Yue Deng
Hongxi Pu
Haoxuan Li
Bo Li

Large Language Models (LLMs) have attained human-level fluency in text generation, which complicates the distinguishing between human-written and LLM generated texts. This increases the risk of misuse and highlights the need for reliable detectors. Yet, existing detectors exhibit poor robustness on out-of-distribution (OOD) data and attacked data, which is critical for real-world scenarios. Also, they struggle to provide interpretable evidence to support their decisions, thus undermining reliability. In light of these challenges, we propose IPAD (Inverse Prompt for AI Detection), a novel framework consisting of a Prompt Inverter that identifies predicted prompts that could have generated the input text, and two Distinguishers that examine the probability that the input texts align with the predicted prompts. Empirical evaluations demonstrate that IPAD outperforms the strongest baselines by 9. 05% (Average Recall) on in-distribution data, 12. 93% (AUROC) on out-of-distribution (OOD) data, and 5. 48% (AUROC) on attacked data. IPAD also performs robust on structured datasets. Furthermore, an interpretability assessment is conducted to illustrate that IPAD enhances the AI detection trustworthiness by allowing users to directly examine the decision-making evidence, which provides interpretable support for its state-of-the-art detection results.

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NeurIPS Conference 2025 Conference Paper

Progress Reward Model for Reinforcement Learning via Large Language Models

Xiuhui Zhang
Ning Gao
Xingyu Jiang
Yihui Chen
Yuheng Pan
Mohan Zhang
Yue Deng

Traditional reinforcement learning (RL) algorithms face significant limitations in handling long-term tasks with sparse rewards. Recent advancements have leveraged large language models (LLMs) to enhance RL by utilizing their world knowledge for task planning and reward generation. However, planning-based approaches often depend on pre-defined skill libraries and fail to optimize low-level control policies, while reward-based methods require extensive human feedback or exhaustive searching due to the complexity of tasks. In this paper, we propose the Progress Reward Model for RL (PRM4RL), a novel framework that integrates task planning and dense reward to enhance RL. For high-level planning, a complex task is decomposed into a series of simple manageable subtasks, with a subtask-oriented, fine-grained progress function designed to monitor task execution progress. For low-level reward generation, inspired by potential-based reward shaping, we use the progress function to construct a Progress Reward Model (PRM), providing theoretically grounded optimality and convergence guarantees, thereby enabling effective policy optimization. Experimental results on robotics control tasks demonstrate that our approach outperforms both LLM-based planning and reward methods, achieving state-of-the-art performance.

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NeurIPS Conference 2025 Conference Paper

RF-Agent: Automated Reward Function Design via Language Agent Tree Search

Ning Gao
Xiuhui Zhang
Xingyu Jiang
Mukang You
Mohan Zhang
Yue Deng

Designing efficient reward functions for low-level control tasks is a challenging problem. Recent research aims to reduce reliance on expert experience by using Large Language Models (LLMs) with task information to generate dense reward functions. These methods typically rely on training results as feedback, iteratively generating new reward functions with greedy or evolutionary algorithms. However, they suffer from poor utilization of historical feedback and inefficient search, resulting in limited improvements in complex control tasks. To address this challenge, we propose RF-Agent, a framework that treats LLMs as language agents and frames reward function design as a sequential decision-making process, enhancing optimization through better contextual reasoning. RF-Agent integrates Monte Carlo Tree Search (MCTS) to manage the reward design and optimization process, leveraging the multi-stage contextual reasoning ability of LLM. This approach better utilizes historical information and improves search efficiency to identify promising reward functions. Outstanding experimental results in 17 diverse low-level control tasks demonstrate the effectiveness of our method.

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IJCAI Conference 2024 Conference Paper

Improving Multi-agent Reinforcement Learning with Stable Prefix Policy

Yue Deng
Zirui Wang
Yin Zhang

In multi-agent reinforcement learning (MARL), the epsilon-greedy method plays an important role in balancing exploration and exploitation during the decision-making process in value-based algorithms. However, the epsilon-greedy exploration process will introduce conservativeness when calculating the expected state value when the agents are more in need of exploitation during the approximate policy convergence, which may result in a suboptimal policy convergence. Besides, eliminating the epsilon-greedy algorithm leaves no exploration and may lead to unacceptable local optimal policies. To address this dilemma, we use the previously collected trajectories to construct a Monte-Carlo Trajectory Tree, so that an existing optimal template, a sequence of state prototypes, can be planned out. The agents start by following the planned template and act according to the policy without exploration, Stable Prefix Policy. The agents will adaptively dropout and begin to explore by following the epsilon-greedy method when the policy still needs exploration. We scale our approach to various value-based MARL methods and empirically verify our method in a cooperative MARL task, SMAC benchmarks. Experimental results demonstrate that our method achieves not only better performance but also faster convergence speed than baseline algorithms within early time steps.

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NeurIPS Conference 2024 Conference Paper

Parallelizing Model-based Reinforcement Learning Over the Sequence Length

Zirui Wang
Yue Deng
Junfeng Long
Yin Zhang

Recently, Model-based Reinforcement Learning (MBRL) methods have demonstrated stunning sample efficiency in various RL domains. However, achieving this extraordinary sample efficiency comes with additional training costs in terms of computations, memory, and training time. To address these challenges, we propose the Pa rallelized Mo del-based R einforcement L earning ( PaMoRL ) framework. PaMoRL introduces two novel techniques: the P arallel W orld M odel ( PWM ) and the P arallelized E ligibility T race E stimation ( PETE ) to parallelize both model learning and policy learning stages of current MBRL methods over the sequence length. Our PaMoRL framework is hardware-efficient and stable, and it can be applied to various tasks with discrete or continuous action spaces using a single set of hyperparameters. The empirical results demonstrate that the PWM and PETE within PaMoRL significantly increase training speed without sacrificing inference efficiency. In terms of sample efficiency, PaMoRL maintains an MBRL-level sample efficiency that outperforms other no-look-ahead MBRL methods and model-free RL methods, and it even exceeds the performance of planning-based MBRL methods and methods with larger networks in certain tasks.

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JMLR Journal 2023 Journal Article

Boosting Multi-agent Reinforcement Learning via Contextual Prompting

Yue Deng
Zirui Wang
Xi Chen
Yin Zhang

Multi-agent reinforcement learning (MARL) has gained increasing attention due to its ability to enable multiple agents to learn policies simultaneously. However, the bootstrapping error arises from the difference between the estimated Q value and the real discounted return and accumulates backward through dynamic programming iterations. This error can become even larger as the number of agents increases, due to the exponential growth of agent interactions, resulting in infeasible learning time and incorrect actions during early training steps. To address this challenge, we observe that previously collected trajectories are useful contexts, model them using a contextual predictor to yield the next action and observation, and use the contextual predictor to replace the Q value function or utility function during the early training phase. Furthermore, we employ a joint-action sampling mechanism to restrict the action space and dynamically select policies from the vanilla utility network and those from the contextual trajectory predictor to perform rollout processes. By reasonably constraining the action space and rollout process, we can significantly accelerate the algorithm training process. Our framework applies to various value-based MARL methods in both centralized training decentralized execution (CTDE) and non-CTDE scenarios where agents are accessible (non-accessible) to global states during the training process. Experimental results on three tasks, Spread, Tag, and Reference, from the Particle World Environment (PWE) show that our framework significantly accelerates the training process of existing state-of-the-art CTDE and non-CTDE MARL methods, while also competing with or outperforming their original versions. [abs] [ pdf ][ bib ] &copy JMLR 2023. ( edit, beta )

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NeurIPS Conference 2023 Conference Paper

Task-aware world model learning with meta weighting via bi-level optimization

Huining Yuan
Hongkun Dou
Xingyu Jiang
Yue Deng

Aligning the world model with the environment for the agent’s specific task is crucial in model-based reinforcement learning. While value-equivalent models may achieve better task awareness than maximum-likelihood models, they sacrifice a large amount of semantic information and face implementation issues. To combine the benefits of both types of models, we propose Task-aware Environment Modeling Pipeline with bi-level Optimization (TEMPO), a bi-level model learning framework that introduces an additional level of optimization on top of a maximum-likelihood model by incorporating a meta weighter network that weights each training sample. The meta weighter in the upper level learns to generate novel sample weights by minimizing a proposed task-aware model loss. The model in the lower level focuses on important samples while maintaining rich semantic information in state representations. We evaluate TEMPO on a variety of continuous and discrete control tasks from the DeepMind Control Suite and Atari video games. Our results demonstrate that TEMPO achieves state-of-the-art performance regarding asymptotic performance, training stability, and convergence speed.

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JBHI Journal 2021 Journal Article

Medication Combination Prediction Using Temporal Attention Mechanism and Simple Graph Convolution

Haiqiang Wang
Yinying Wu
Chao Gao
Yue Deng
Fan Zhang
Jiajin Huang
Jiming Liu

Medication combination prediction can be applied to the clinical treatment for critical patients with multi-morbidity. The suitable medication combination can help cure patients and keep the treatment medication safe. However, the complexity and uncertainty of clinical circumstances limit the predictive accuracy of medication combination. Thus, this paper proposes a new medication combination prediction model based on the temporal attention mechanism (TAM) and the simple graph convolution (SGC), named as TAMSGC. More specifically, the TAM can capture the temporal sequence information in the medical records, and the SGC is implemented to acquire the medication knowledge from the complicated medication combination. Experiments in a real dataset show that TAMSGC surpasses the baseline models on the predictive accuracy of medication combination.

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AAMAS Conference 2019 Conference Paper

A New Concept of Convex based Multiple Neural Networks Structure

Yu Wang
Yue Deng
Yilin Shen
Hongxia Jin

In this paper, a new concept of convex based multiple neural networks structure is proposed. This new approach uses the collective information from multiple neural networks to train the model. From both theoretical and experimental analysis, it is going to demonstrate that the new approach gives a faster training speed of convergence with a similar or even better test accuracy, compared to a conventional neural network structure. Two experiments are conducted to demonstrate the performance of our new structure: the first one is a semantic frame parsing task for spoken language understanding (SLU) on ATIS dataset, and the other is a hand written digits recognition task on MNIST dataset. We test this new structure using both recurrent neural network and convolutional neural networks through these two tasks. The results of both experiments demonstrate a 4x-8x faster training speed with better or similar performance by using this new concept.

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IJCAI Conference 2019 Conference Paper

Learning Assistance from an Adversarial Critic for Multi-Outputs Prediction

Yue Deng
Yilin Shen
Hongxia Jin

We introduce an adversarial-critic-and-assistant (ACA) learning framework to improve the performance of existing supervised learning with multiple outputs. The core contribution of our ACA is the innovation of two novel modules, i. e. an `adversarial critic' and a `collaborative assistant', that are jointly designed to provide augmenting information for facilitating general learning tasks. Our approach is not intended to be regarded as an emerging competitor for tons of well-established algorithms in the field. In fact, most existing approaches, while implemented with different learning objectives, can all be adopted as building blocks seamlessly integrated in the ACA framework to accomplish various real-world tasks. We show the performance and generalization ability of ACA on diverse learning tasks including multi-label classification, attributes prediction and sequence-to-sequence generation.

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IJCAI Conference 2018 Conference Paper

Adversarial Active Learning for Sequences Labeling and Generation

Yue Deng
KaWai Chen
Yilin Shen
Hongxia Jin

We introduce an active learning framework for general sequence learning tasks including sequence labeling and generation. Most existing active learning algorithms mainly rely on an uncertainty measure derived from the probabilistic classifier for query sample selection. However, such approaches suffer from two shortcomings in the context of sequence learning including 1) cold start problem and 2) label sampling dilemma. To overcome these shortcomings, we propose a deep-learning-based active learning framework to directly identify query samples from the perspective of adversarial learning. Our approach intends to offer labeling priorities for sequences whose information content are least covered by existing labeled data. We verify our sequence-based active learning approach on two tasks including sequence labeling and sequence generation.

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IJCAI Conference 2017 Conference Paper

Disguise Adversarial Networks for Click-through Rate Prediction

Yue Deng
Yilin Shen
Hongxia Jin

We introduced an adversarial learning framework for improving CTR prediction in Ads recommendation. Our approach was motivated by observing the extremely low click-through rate and imbalanced label distribution in the historical Ads impressions. We hence proposed a Disguise-Adversarial-Networks (DAN) to improve the accuracy of supervised learning with limited positive-class information. In the context of CTR prediction, the rationality behind DAN could be intuitively understood as ``non-clicked Ads makeup''. DAN disguises the disliked Ads impressions (non-clicks) to be interesting ones and encourages a discriminator to classify these disguised Ads as positive recommendations. In an adversarial aspect, the discriminator should be sober-minded which is optimized to allocate these disguised Ads to their inherent classes according to an unsupervised information theoretic assignment strategy. We applied DAN to two Ads datasets including both mobile and display Ads for CTR prediction. The results showed that our DAN approach significantly outperformed other supervised learning and generative adversarial networks (GAN) in CTR prediction.

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