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Pengcheng Wang

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8 papers

1 author row

AAAI Conference 2026 Conference Paper

ReCast: Reliability-aware Codebook-assisted Lightweight Time Series Forecasting

Xiang Ma
Taihua Chen
Pengcheng Wang
Xuemei Li
Caiming Zhang

Time series forecasting is crucial for applications in various domains. Conventional methods often rely on global decomposition into trend, seasonal, and residual components, which become ineffective for real-world series dominated by local, complex, and highly dynamic patterns. Moreover, the high model complexity of such approaches limits their applicability in real-time or resource-constrained environments. In this work, we propose a novel reliability-aware codebook-assisted time series forecasting framework (ReCast) that enables lightweight and robust prediction by exploiting recurring local shapes. ReCast encodes local patterns into discrete embeddings through patch-wise quantization using a learnable codebook, thereby compactly capturing stable regular structures. To compensate for residual variations not preserved by quantization, ReCast employs a dual-path architecture comprising a quantization path for efficient modeling of regular structures and a residual path for reconstructing irregular fluctuations. A central contribution of ReCast is a reliability-aware codebook update strategy, which incrementally refines the codebook via weighted corrections. These correction weights are derived by fusing multiple reliability factors from complementary perspectives by a distributionally robust optimization (DRO) scheme, ensuring adaptability to non-stationarity and robustness to distribution shifts. Extensive experiments demonstrate that ReCast outperforms state-of-the-art (SOTA) models in accuracy, efficiency, and adaptability to distribution shifts.

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YNIMG Journal 2025 Journal Article

Decoding cortical folding patterns in marmosets using machine learning and large language model

Yue Wu
Xuesong Gao
Zhengliang Liu
Pengcheng Wang
Zihao Wu
Yiwei Li
Tuo Zhang
Tianming Liu

Macroscale neuroimaging results have revealed significant differences in the structural and functional connectivity patterns of gyri and sulci in the primate cerebral cortex. Despite these findings, understanding these differences at the molecular level has remained challenging. This study leverages a comprehensive dataset of whole-brain in situ hybridization (ISH) data from marmosets, with updates continuing through 2024, to systematically analyze cortical folding patterns. Utilizing advanced machine learning algorithm and large language model (LLM), we identified genes with significant transcriptomic differences between concave (sulci) and convex (gyri) cortical patterns. Further, gene enrichment analysis, neural migration analysis, and axon guidance pathway analysis were employed to elucidate the molecular mechanisms underlying these structural and functional differences. Our findings provide new insights into the molecular basis of cortical folding, demonstrating the potential of LLM in enhancing our understanding of brain structural and functional connectivity.

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TMLR Journal 2025 Journal Article

Entropy-Regularized Process Reward Model

Hanning Zhang
Pengcheng Wang
Shizhe Diao
Yong Lin
Rui Pan
Hanze Dong
Dylan Zhang
Pavlo Molchanov

Large language models (LLMs) have shown promise in performing complex multi-step reasoning, yet they continue to struggle with mathematical reasoning, often making systematic errors. A promising solution is reinforcement learning (RL) guided by reward models, particularly those focusing on process rewards, which score each intermediate step rather than solely evaluating the final outcome. This approach is more effective at guiding policy models towards correct reasoning trajectories. In this work, we propose an entropy-regularized process reward model (ER-PRM) that integrates KL-regularized Markov Decision Processes (MDP) to balance policy optimization with the need to prevent the policy from shifting too far from its initial distribution. We derive a novel reward construction method based on the theoretical results. Our theoretical analysis shows that we could derive the optimal reward model from the initial policy sampling. Our empirical experiments on the MATH and GSM8K benchmarks demonstrate that ER-PRM consistently outperforms existing process reward models, achieving 1% improvement on GSM8K and 2-3% improvement on MATH under best-of-N evaluation, and more than 1% improvement under RLHF. These results highlight the efficacy of entropy-regularization in enhancing LLMs' reasoning capabilities.

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AAAI Conference 2024 Conference Paper

CK12: A Rounded K12 Knowledge Graph Based Benchmark for Chinese Holistic Cognition Evaluation

Weihao You
Pengcheng Wang
Changlong Li
Zhilong Ji
Jinfeng Bai

New NLP benchmarks are urgently needed to align with the rapid development of large language models (LLMs). We present a meticulously designed evaluation benchmark that leverages the knowledge graph. This evaluation comprises 584 level-1 knowledge points and 1,989 level-2 knowledge points, thereby encompassing a comprehensive spectrum of the K12 education domain knowledge. The primary objective is to comprehensively assess the high-level comprehension aptitude and reasoning capabilities of LLMs operating within the Chinese context. Our evaluation incorporates five distinct question types with 39,452 questions. We test the current mainstream LLMs by three distinct modes. Firstly, four prompt evaluation modes were employed to assess the fundamental capacity. Additionally, for choice questions, a result-oriented evaluation approach was designed through data augmentation to assess the model's proficiency in advanced knowledge and reasoning. Moreover, a subset with reasoning process is derived, and the process-oriented testing method is used to test the model's interpretability and higher-order reasoning capacity. We further show models' capability in our knowledge points, and anticipate the evaluation can assist in the assessment of the strengths and deficiencies of LLMs on knowledge points, thus fostering their development within the Chinese context. Our Dataset will be publicly available in https://github.com/tal-tech/chinese-k12-evaluation.

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EAAI Journal 2024 Journal Article

Incorporating syntax and semantics with dual graph neural networks for aspect-level sentiment analysis

Pengcheng Wang
Linping Tao
Mingwei Tang
Liuxuan Wang
Yangsheng Xu
Mingfeng Zhao

Aspect-level sentiment analysis is a more fine-grained task that aims to determine the sentiment polarity of specific aspects. Recent studies have employed graph attention networks and graph convolutional networks to model dependency trees, effectively establishing explicit associations between aspects and opinions, yielding promising performance. However, these methods have limitations in capturing complex linguistic features and the intricate dependencies between aspects and their contexts, resulting in suboptimal performance. In this paper, we propose a dual graph neural network that incorporates syntax and semantics, called IDGNN. Specifically, we utilize the relational graph attention network (RGAT) to encode the syntactic dependency tree and obtain syntactic information, while incorporating dependency labels to enhance aspect representation. Additionally, the semantic graph convolutional network (SemGCN) is employed to encode the self-attention matrix and capture semantic information, with the inclusion of orthogonal regularization to enhance semantic association. Furthermore, we introduce two fusion strategies based on gate mechanisms: the syntax fusion module (SYF) and the semantic fusion module (SEF). SYF combines contextual and syntactic representations to obtain global syntactic features, while SEF fuses semantic information with global syntactic features to obtain the final feature representation. Experimental results demonstrate that our proposed model achieves state-of-the-art performance on several benchmark datasets.

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YNICL Journal 2022 Journal Article

The effect of time delay for magnetic resonance contrast-enhanced scan on imaging for small-volume brain metastases

Mingming Chen
Pengcheng Wang
Yujie Guo
Yong Yin
Lizhen Wang
Ya Su
Guanzhong Gong

PURPOSE: To study the effect of different enhancement timings of magnetic resonance (MR) on small-volume brain metastases (BM) visualisation and provide a basis for the contour of tumour targets. METHOD: We prospectively enrolled 101 patients with BM who received radiotherapy. All patients underwent computed tomography (CT) and MR simulations. Contrast-enhanced MR scans at 1, 3, 5, 10, 18, and 20 min after injection of contrast medium were performed. The tumour target was determined on MR images at different enhancement times, and the differences of tumour target volume, maximum diameter, and MR signal intensity were compared. RESULTS: , respectively. Compared to 1 min, BM volume at other times increased by 13.1 %, 21.5 %, 31.6 %, 39.6 %, and 41.7 %, and the difference between the maximum and minimum volumes was statistically significant (p 10 min is essential.

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IJCAI Conference 2018 Conference Paper

High-Fidelity Simulated Players for Interactive Narrative Planning

Pengcheng Wang
Jonathan Rowe
Wookhee Min
Bradford Mott
James Lester

Interactive narrative planning offers significant potential for creating adaptive gameplay experiences. While data-driven techniques have been devised that utilize player interaction data to induce policies for interactive narrative planners, they require enormously large gameplay datasets. A promising approach to addressing this challenge is creating simulated players whose behaviors closely approximate those of human players. In this paper, we propose a novel approach to generating high-fidelity simulated players based on deep recurrent highway networks and deep convolutional networks. Empirical results demonstrate that the proposed models significantly outperform the prior state-of-the-art in generating high-fidelity simulated player models that accurately imitate human players’ narrative interactions. Using the high-fidelity simulated player models, we show the advantage of more exploratory reinforcement learning methods for deriving generalizable narrative adaptation policies.

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IJCAI Conference 2017 Conference Paper

Interactive Narrative Personalization with Deep Reinforcement Learning

Pengcheng Wang
Jonathan Rowe
Wookhee Min
Bradford Mott
James Lester

Data-driven techniques for interactive narrative generation are the subject of growing interest. Reinforcement learning (RL) offers significant potential for devising data-driven interactive narrative generators that tailor players’ story experiences by inducing policies from player interaction logs. A key open question in RL-based interactive narrative generation is how to model complex player interaction patterns to learn effective policies. In this paper we present a deep RL-based interactive narrative generation framework that leverages synthetic data produced by a bipartite simulated player model. Specifically, the framework involves training a set of Q-networks to control adaptable narrative event sequences with long short-term memory network-based simulated players. We investigate the deep RL framework’s performance with an educational interactive narrative, Crystal Island. Results suggest that the deep RL-based narrative generation framework yields effective personalized interactive narratives.

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