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Yufei Zhang

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6 papers

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TMLR Journal 2026 Journal Article

Bayesian Network Structure Discovery Using Large Language Models

Yinghuan Zhang
Yufei Zhang
Parisa Kordjamshidi
Zijun Cui

Understanding probabilistic dependencies among variables is central to analyzing complex systems. Traditional structure learning methods often require extensive observational data or are limited by manual, error-prone incorporation of expert knowledge. Recent studies have explored using large language models (LLMs) for structure learning, but most treat LLMs as auxiliary tools for pre-processing or post-processing, leaving the core learning process data-driven. In this work, we introduce a unified framework for Bayesian network structure discovery that places LLMs at the center, supporting both data-free and data-aware settings. In the data-free regime, we introduce \textbf{PromptBN}, which leverages LLM reasoning over variable metadata to generate a complete directed acyclic graph (DAG) in a single call. PromptBN effectively enforces global consistency and acyclicity through dual validation, achieving constant $\mathcal{O}(1)$ query complexity. When observational data are available, we introduce \textbf{ReActBN} to further refine the initial graph. ReActBN combines statistical evidence with LLM by integrating a novel ReAct-style reasoning with configurable structure scores (e.g., Bayesian Information Criterion). Experiments demonstrate that our method outperforms prior data-only, LLM-only, and hybrid baselines, particularly in low- or no-data regimes and on out-of-distribution datasets.

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NeurIPS Conference 2025 Conference Paper

PolarQuant: Leveraging Polar Transformation for Key Cache Quantization and Decoding Acceleration

Songhao Wu
Ang Lv
xiao feng
Yufei Zhang
Xun Zhang
Guojun Yin
Wei Lin
Rui Yan

The increasing demand for long-context generation has made the KV cache in large language models a bottleneck in memory consumption. Quantizing the cache to lower bit widths is an effective way to reduce memory costs; however, previous methods struggle with key cache quantization due to outliers, resulting in suboptimal performance. We propose a novel quantization approach PolarQuant, which provides a new perspective for key cache quantization and efficiently addresses the outlier dilemma. We observe that the distribution of the key states reveals well-structured patterns under polar transformation. Outliers generally appear in only one of the two dimensions, which are rotated together by a specific angle when rotary position embeddings are applied. When represented as two-dimensional vectors, these dimensions exhibit well-organized patterns, with radii and angles smoothly distributed in polar space. This alleviates the channel-wise outliers, making them well-suited for key cache quantization. PolarQuant divides key vectors into groups of two-dimensional sub-vectors, encoding them as the quantized radius and the polar angle, rather than quantizing original key vectors directly. PolarQuant achieves the superior efficiency in KV cache quantization and accelerates the decoding process by turning the query-key inner product into a table lookup, all while maintaining the downstream performance of full-precision models. Our code is available at https: //github. com/ericshwu/PolarQuant.

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AAAI Conference 2025 Conference Paper

Semantic Convergence: Harmonizing Recommender Systems via Two-Stage Alignment and Behavioral Semantic Tokenization

Guanghan Li
Xun Zhang
Yufei Zhang
Yifan Yin
Guojun Yin
Wei Lin

Large language models (LLMs), endowed with exceptional reasoning capabilities, are adept at discerning profound user interests from historical behaviors, thereby presenting a promising avenue for the advancement of recommendation systems. However, a notable discrepancy persists between the sparse collaborative semantics typically found in recommendation systems and the dense token representations within LLMs. In our study, we propose a novel framework that harmoniously merges traditional recommendation models with the prowess of LLMs. We initiate this integration by transforming ItemIDs into sequences that align semantically with the LLMs' space, through the proposed Alignment Tokenization module. Additionally, we design a series of specialized supervised learning tasks aimed at aligning collaborative signals with the subtleties of natural language semantics. To ensure practical applicability, we optimize online inference by pre-caching the top-K results for each user, reducing latency and improving efficiency. Extensive experimental evidence indicates that our model markedly improves recall metrics and displays remarkable scalability of recommendation systems.

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EAAI Journal 2023 Journal Article

A storage-efficient SNN–CNN hybrid network with RRAM-implemented weights for traffic signs recognition

Yufei Zhang
Hui Xu
Lixing Huang
Changlin Chen

Traffic Signs Recognition (TSR) is a key technology to implement Automatic Driving System (ADS) and Advanced Driver Assistant System (ADAS). Numerous efforts have been endeavored to improve the TSR accuracy and speed, and Convolution Neural Networks (CNN) are usually employed. However, most of the existing works require large network scale and thus suffer from the drawback of high computation and power consumption. To alleviate this issue, in this paper, we propose an SNN–CNN hybrid network with RRAM-implemented weights to improve the storage and computing efficiency of TSR. Specifically, we utilize SNN to quickly determine the superclass that a traffic sign belongs to. Then utilize CNNs to determine the subclass accurately, thus the complex TSR task is decomposed into multiple simple tasks. Finally, the network weights are binarized to alleviate the implementation of the hybrid network on RRAM-based accelerators, which have higher power efficiency than CMOS logic based ones. Experimental results indicate that, when compared with state of the art CNN method, the SNN–CNN hybrid network can achieve similar accuracy with less weight scale (−69. 21%) and lower power consumption (−81. 55%). Binarizing the hybrid network can further reduce the weights storage requirement by another 96. 875% with only 1. 518% accuracy loss. When the network weights are implemented with state of the art RRAM array (resistance variation is 5%), the proposed network can achieve a mean accuracy of 96. 47% with 95% ∼ 97. 5% confidence interval.

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JMLR Journal 2022 Journal Article

Logarithmic Regret for Episodic Continuous-Time Linear-Quadratic Reinforcement Learning over a Finite-Time Horizon

Matteo Basei
Xin Guo
Anran Hu
Yufei Zhang

We study finite-time horizon continuous-time linear-quadratic reinforcement learning problems in an episodic setting, where both the state and control coefficients are unknown to the controller. We first propose a least-squares algorithm based on continuous-time observations and controls, and establish a logarithmic regret bound of magnitude $\mathcal{O}((\ln M)(\ln\ln M) )$, with $M$ being the number of learning episodes. The analysis consists of two components: perturbation analysis, which exploits the regularity and robustness of the associated Riccati differential equation; and parameter estimation error, which relies on sub-exponential properties of continuous-time least-squares estimators. We further propose a practically implementable least-squares algorithm based on discrete-time observations and piecewise constant controls, which achieves similar logarithmic regret with an additional term depending explicitly on the time stepsizes used in the algorithm. [abs] [ pdf ][ bib ] &copy JMLR 2022. ( edit, beta )

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NeurIPS Conference 2020 Conference Paper

Understanding Deep Architecture with Reasoning Layer

Xinshi Chen
Yufei Zhang
Christoph Reisinger
Le Song

Recently, there is a surge of interest in combining deep learning models with reasoning in order to handle more sophisticated learning tasks. In many cases, a reasoning task can be solved by an iterative algorithm. This algorithm is often unrolled, truncated, and used as a specialized layer in the deep architecture, which can be trained end-to-end with other neural components. Although such hybrid deep architectures have led to many empirical successes, theoretical understandings of such architectures, especially the interplay between algorithm layers and other neural layers, remains largely unexplored. In this paper, we take an initial step toward an understanding of such hybrid deep architectures by showing that properties of the algorithm layers, such as convergence, stability and sensitivity, are intimately related to the approximation and generalization abilities of the end-to-end model. Furthermore, our analysis matches nicely with experimental observations under various conditions, suggesting that our theory can provide useful guidelines for designing deep architectures with reasoning layers.

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