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Han Wu

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16 papers

2 author rows

YNICL Journal 2026 Journal Article

Alteration of fronto-thalamic-striatal and visual network activity to positive emotional stimuli in adolescent patients with bipolar disorder during a Go/No-Go task-based functional brain MRI

Xueying Wang
Jinfan Zhang
Feifei Wu
Liying Shen
Lin Wang
Han Wu
Qian Xiao
Xiaoping Yi

Background Adolescent patients with bipolar disorder (BD) tend to have abnormal neural activity to emotional stimuli. This study aimed to assess alterations of neural activity within the fronto-thalamic-striatal circuit, and the fusiform gyrus during positive stimuli processing in Go/No-Go task-based brain functional MRI (fMRI). Methods This prospective study enrolled 43 adolescent patients with BD and 18 age- sex-matched healthy controls. All study participants underwent a task-based brain fMRI using a happy versus neutral Go/No-Go paradigm. All study participants also completed multiple affective and cognitive assessment questionnaire. Results Enhanced activity was found in the fronto-thalamic-striatal circuit including the inferior frontal gyrus and the caudate, the fusiform gyrus, the left cerebellum crus I and the hippocampus in adolescent patients with BD, during response inhibition to happy versus neutral distractors in an emotional Go/No-Go fMRI task, compared with matched healthy controls (p<0. 05). Moreover, the left inferior frontal gyrus, the right fusiform gyrus and the left cerebellum crus I responses to happy versus neutral distractors were positively associated with the differences in false response errors in the patients with BD (all p<0. 05, FDR corrected). The enhanced activity of the caudate nucleus and that of the right hippocampus were negatively correlated with cognitive function (all p<0. 05, FDR corrected). Conclusion This study found significant brain functional alterations in the limbic system, the visual brain network and cerebellum, especially the fronto-thalamic-striatal track and fusiform gyrus, which was correlated with cognitive dysfunction in adolescent patients with BD. These changes may serve as potential neuroimaging correlates of BD in adolescent patients.

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AAAI Conference 2026 Conference Paper

DeepOR: A Deep Reasoning Foundation Model for Optimization Modeling

Ziyang Xiao
Yuan Jessica Wang
Xiongwei Han
Shisi Guan
Jingyan Zhu
Jingrong Xie
Lilin Xu
Han Wu

Optimization modeling plays a critical role in supporting optimal decision-making across various domains. Previous works have demonstrated that large language models (LLMs) tailored for optimization modeling have significantly automated and simplified this process. However, these models typically employ a straightforward input-output paradigm and struggle with challenging instances. In contrast, recent advances in general-purpose reasoning LLMs (RLLMs), such as DeepSeek-R1, have shown impressive capabilities in complex domains like mathematics and coding. In this paper, we introduce DeepOR, the first RLLM specifically designed for optimization modeling. Instead of directly outputting solutions, DeepOR explicitly performs multiple intermediate reasoning steps. To adapt a base LLM into an RLLM, we begin by synthesizing long chain-of-thought (CoT) data guided by a flowchart, which is automatically generated using a self-exploration algorithm. Once the training data are prepared, we employ supervised fine-tuning on the base LLM to endow it with reasoning capabilities tailored for optimization modeling. To fully leverage the model's reasoning potential, we further apply reinforcement learning with reward-shaping derived from solver feedback. Experimental results on benchmarks confirm that DeepOR consistently and significantly outperforms existing state-of-the-art approaches.

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AAAI Conference 2026 Conference Paper

Exposing the Cracks: Vulnerabilities of Retrieval-Augmented LLM-based Machine Translation

Yanming Sun
Runzhe Zhan
Chi Seng Cheang
Han Wu
Xuebo Liu
Yuyao Niu
Fengying Ye
Kaixin Lan

REtrieval-Augmented LLM-based Machine Translation (REAL-MT) shows promise for knowledge-intensive tasks like idiomatic translation, but its reliability under noisy retrieval, a common challenge in real-world deployment, remains poorly understood. To address this gap, we propose a noise synthesis framework and new metrics to systematically evaluate REAL-MT’s reliability across high-, medium-, and low-resource language pairs. Using both open- and closed-sourced models, including standard LLMs and large reasoning models (LRMs), we find that models heavily rely on retrieved context, and this dependence is significantly more detrimental in low-resource language pairs, producing nonsensical translations. Although LRMs possess enhanced reasoning capabilities, they show no improvement in error correction and are even more susceptible to noise, tending to rationalize incorrect contexts. Attention analysis reveals a shift from the source idiom to noisy content, while confidence increases despite declining accuracy, indicating poor self-monitoring. To mitigate these issues, we investigate training-free and fine-tuning strategies, which improve robustness at the cost of performance in clean contexts, revealing a fundamental trade-off. Our findings highlight the limitations of current approaches, underscoring the need for self-verifying integration mechanisms.

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AAAI Conference 2026 Conference Paper

FD-MAGRPO: Functionality-Driven Multi-Agent Group Relative Policy Optimization for Analog-LDO Sizing

Haoning Jiang
Han Wu
Zhuoli Ouyang
Ziheng Wang
Tinghuan Chen
Junmin Jiang

This paper introduces the Functionality-Driven Multi-Agent Group Relative Policy Optimization (FD-MAGRPO) algorithm, which is designed to enhance exploration efficiency in reinforcement learning (RL) for analog integrated circuit sizing. Our proposed method integrates two key innovations: (1) a critic-free multi-agent optimization framework based on Group Relative Policy Optimization (GRPO), that eliminates the critic network and achieves stable and efficient policy updates; and (2) a functionality-driven grouping strategy, that enables agents to coordinate exploration by functional roles instead of circuit blocks, thereby improving credit assignment and cooperation. Experimental results on practical low-dropout regulator (LDO) circuits with 65–179 design parameters show that the proposed method achieves rapid convergence with only 800–3000 simulations, yielding a 4.8×–13.0× speedup over state-of-the-art methods. Mathematical analysis and empirical studies validate that the combination of critic-free optimization and functionality-based grouping leads to higher exploration efficiency and faster convergence. The proposed method enables the discovery of higher circuit performances that are inaccessible to conventional approaches, establishing FD-MAGRPO as a robust and efficient solution for complex analog-LDO sizing tasks.

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IJCAI Conference 2025 Conference Paper

A Survey of Optimization Modeling Meets LLMs: Progress and Future Directions

Ziyang Xiao
Jingrong Xie
Lilin Xu
Shisi Guan
Jingyan Zhu
Xiongwei Han
Xiaojin Fu
WingYin Yu

By virtue of its great utility in solving real-world problems, optimization modeling has been widely employed for optimal decision-making across various sectors, but it requires substantial expertise from operations research professionals. With the advent of large language models (LLMs), new opportunities have emerged to automate the procedure of mathematical modeling. This survey presents a comprehensive and timely review of recent advancements that cover the entire technical stack, including data synthesis and fine-tuning for the base model, inference frameworks, benchmark datasets, and performance evaluation. In addition, we conducted an in-depth analysis on the quality of benchmark datasets, which was found to have a surprisingly high error rate. We cleaned the datasets and constructed a new leaderboard with fair performance evaluation in terms of base LLM model and datasets. We also build an online portal that integrates resources of cleaned datasets, code and paper repository to benefit the community. Finally, we identify limitations in current methodologies and outline future research opportunities.

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NeurIPS Conference 2025 Conference Paper

Activation-Guided Consensus Merging for Large Language Models

Yuxuan Yao
Shuqi LIU
Zehua Liu
Qintong Li
Mingyang Liu
Xiongwei Han
Zhijiang Guo
Han Wu

Recent research has increasingly focused on reconciling the reasoning capabilities of System 2 with the efficiency of System 1. While existing training-based and prompt-based approaches face significant challenges in terms of efficiency and stability, model merging emerges as a promising strategy to integrate the diverse capabilities of different Large Language Models (LLMs) into a unified model. However, conventional model merging methods often assume uniform importance across layers, overlooking the functional heterogeneity inherent in neural components. To address this limitation, we propose \textbf{A}ctivation-Guided \textbf{C}onsensus \textbf{M}erging (\textbf{ACM}), a plug-and-play merging framework that determines layer-specific merging coefficients based on mutual information between activations of pre-trained and fine-tuned models. ACM effectively preserves task-specific capabilities without requiring gradient computations or additional training. Extensive experiments on Long-to-Short (L2S) and general merging tasks demonstrate that ACM consistently outperforms all baseline methods. For instance, in the case of Qwen-7B models, TIES-Merging equipped with ACM achieves a \textbf{55. 3\%} reduction in response length while simultaneously improving reasoning accuracy by \textbf{1. 3} points. We submit the code with the paper for reproducibility, and it will be publicly available.

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YNICL Journal 2025 Journal Article

Effects of parietal iTBS on resting-state effective connectivity within the frontoparietal network in patients with schizophrenia: An fMRI study

Li Li
Lina Wang
Han Wu
Bing Li
Weigang Pan
Wenqing Jin
Wen Wang
Yanping Ren

BACKGROUND: Although intermittent theta burst stimulation (iTBS) has shown effectiveness in addressing working memory (WM) deficits in individuals with schizophrenia (SZ), the current body of evidence is limited and the specific mechanisms involved remain unclear. Therefore, this pilot fMRI study aimed to examine the efficacy of parietal iTBS in ameliorating WM impairments and explore its influence on the resting-state effective connectivity within the frontoparietal network in patients with SZ. METHOD: A total of 48 patients diagnosed with SZ were randomly assigned to an active or sham iTBS group and underwent 20 sessions of active or sham iTBS over 4 weeks. Subsequently, all patients underwent cognitive tests, clinical symptom assessments, and resting-state functional MRI (rs-fMRI) scans. The effective connectivity between the frontal and parietal brain regions during the rs-fMRI scans was analyzed using a spectral dynamic causal modeling approach. Additionally, this trial was registered at the Chinese Clinical Trial Registry in November 2022 (registry number: ChiCTR2200057286). RESULTS: iTBS treatment improved the positive symptoms, negative symptoms, general psychopathology, and WM deficits. Following the iTBS intervention, the active group demonstrated a significant increase in connectivity strengths from the right MFG to the right SPL (p = 0.031) and from the left SPL to the left MFG (p = 0.010) compared to the pre-treatment levels. Additionally, compared to the sham group, the active group displayed a significantly higher connectivity strength from the right MFG to the right SPL (p = 0.042) after iTBS treatment. CONCLUSION: All these findings suggest that iTBS targeting the parietal region may influence the resting-state effective connectivity within the frontoparietal network, thereby offering promising therapeutic implications for alleviating the cognitive deficits in SZ.

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NeurIPS Conference 2025 Conference Paper

MoEMeta: Mixture-of-Experts Meta Learning for Few-Shot Relational Learning

Han Wu
Jie Yin

Few-shot knowledge graph relational learning seeks to perform reasoning over relations given only a limited number of training examples. While existing approaches largely adopt a meta-learning framework for enabling fast adaptation to new relations, they suffer from two key pitfalls. First, they learn relation meta-knowledge in isolation, failing to capture common relational patterns shared across tasks. Second, they struggle to effectively incorporate local, task-specific contexts crucial for rapid adaptation. To address these limitations, we propose MoEMeta, a novel meta-learning framework that disentangles globally shared knowledge from task-specific contexts to enable both effective model generalization and rapid adaptation. MoEMeta introduces two key innovations: (i) a mixture-of-experts (MoE) model that learns globally shared relational prototypes to enhance generalization, and (ii) a task-tailored adaptation mechanism that captures local contexts for fast task-specific adaptation. By balancing global generalization with local adaptability, MoEMeta significantly advances few-shot relational learning. Extensive experiments and analyses on three KG benchmarks show that MoEMeta consistently outperforms existing baselines, achieving state-of-the-art performance.

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NeurIPS Conference 2025 Conference Paper

Preserving LLM Capabilities through Calibration Data Curation: From Analysis to Optimization

Bowei He
Lihao Yin
Hui-Ling Zhen
Shuqi LIU
Han Wu
Xiaokun Zhang
Mingxuan Yuan
Chen Ma

Post-training compression has been a widely employed approach to scale down large language model (LLM) and facilitate efficient inference. In various proposed compression methods, including pruning and quantization, calibration data plays a vital role by informing the weight importance and activation dynamic ranges. However, how calibration data impacts the LLM capability after compression is less explored. Few of the existing works, though recognizing the significance of this study, only investigate the language modeling or commonsense reasoning performance degradation from limited angles, like the data sources or sample amounts. More systematic research is still needed to examine the impacts on different LLM capabilities in terms of compositional properties and domain correspondence of calibration data. In this work, we aim at bridging this gap and further analyze underlying influencing mechanisms from the activation pattern perspective. Especially, we explore the calibration data's impacts on high-level complex reasoning capabilities, like math problem solving and code generation. Delving into the underlying mechanism, we find that the representativeness and diversity in activation space more fundamentally determine the quality of calibration data. Finally, we propose a calibration data curation framework based on such observations and analysis, enhancing the performance of existing post-training compression methods on preserving critical LLM capabilities. Our code is provided in Link.

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IROS Conference 2025 Conference Paper

Q-Learning-based Optimal Force-Tracking Control of Grinding Robots in Uncertain Environments

Rui Yang
Han Wu
Jianying Zheng
Xinyu Wang 0045
Qinglei Hu

This paper proposes a novel Q-learning-based dual-loop force tracking control framework for robot grinding tasks in uncertain environments. A complete system state-space model is established, incorporating interaction dynamics and the desired force. By augmenting the system state, a discount cost function is defined to quantify the tracking errors of the force and reference trajectory. The modified Q-learning method is systematically designed to iteratively compute the optimal control gain in a model-free manner. To mitigate force overshoot during the transition from free space to contact space, a force reference model and a transition mechanism for the control gain are designed. Simulations and experiments validate the method’s effectiveness in precise force tracking with minimal overshoot and robustness to environmental variations.

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EAAI Journal 2024 Journal Article

Methodology and application of digital twin-driven diesel engine fault diagnosis and virtual fault model acquisition

Yaqing Bo
Han Wu
Weifan Che
Zeyu Zhang
Xiangrong Li
Leonid Myagkov

Digital real-time fault diagnosis is an effective way to ensure the reliable long-term operation of the diesel engine, but there is still a lack of systematic methods with high integrity and practicability. Therefore, a digital twin-driven diesel engine fault diagnosis method based on the combination of the classification algorithm and the optimization algorithm is proposed and a case study of fuel injection system fault diagnosis is used to illustrate and verify the proposed method. This method closely links the physical system, virtual model, database, and diagnosis system through data transmission and the diagnostic process consists of three parts: classification, diagnosis, and decision. The fault classification part can preliminarily lock the possible types and degrees of faults, and point out the key classification features for each fault type by using classification algorithms such as Random Forest. The fault diagnosis part can diagnose and reproduce the diesel engine faults by using an optimization-simulation joint calculation model, where the virtual model variables and optimization algorithm are determined according to the possible fault types, and the optimization target depends on the importance of classification features. Then the maintenance decision can be made according to the fault detailed information. The proposed method reduces the requirement of covering the fault degree of the database, and the obtained fault model provides the possibility for subsequent online optimization and also facilitates the development of intelligent engine management.

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TIST Journal 2024 Journal Article

Model-Agnostic Adaptive Testing for Intelligent Education Systems via Meta-learned Gradient Embeddings

Haoyang Bi
Qi Liu
Han Wu
Weidong He
Zhenya Huang
Yu Yin
Haiping Ma
Yu Su

The field of education has undergone a significant revolution with the advent of intelligent systems and technology, which aim to personalize the learning experience, catering to the unique needs and abilities of individual learners. In this pursuit, a fundamental challenge is designing proper test for assessing the students’ cognitive status on knowledge and skills accurately and efficiently. One promising approach, referred to as Computerized Adaptive Testing (CAT), is to administrate computer-automated tests that alternately select the next item for each examinee and estimate their cognitive states given their responses to the selected items. Nevertheless, existing CAT systems suffer from inflexibility in item selection and ineffectiveness in cognitive state estimation, respectively. In this article, we propose a Model-Agnostic adaptive testing framework via Meta-leaned Gradient Embeddings, MAMGE for short, improving both item selection and cognitive state estimation simultaneously. For item selection, we design a Gradient Embedding-based Item Selector (GEIS) which incorporates the concept of gradient embeddings to represent items and selects the best ones that are both informative and representative. For cognitive state estimation, we propose a Meta-learned Cognitive State Estimator (MCSE) to automatically control the estimation process by learning to learn a proper initialization and dynamically inferred updates. Both MCSE and GEIS are inherently model-agnostic, and the two modules have an ingenious connection via meta-learned gradient embeddings. Finally, extensive experiments evaluate the effectiveness and flexibility of MAMGE.

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AAAI Conference 2023 Conference Paper

BETA-CD: A Bayesian Meta-Learned Cognitive Diagnosis Framework for Personalized Learning

Haoyang Bi
Enhong Chen
Weidong He
Han Wu
Weihao Zhao
Shijin Wang
Jinze Wu

Personalized learning is a promising educational approach that aims to provide high-quality personalized services for each student with minimum demands for practice data. The key to achieving that lies in the cognitive diagnosis task, which estimates the cognitive state of the student through his/her logged data of doing practice quizzes. Nevertheless, in the personalized learning scenario, existing cognitive diagnosis models suffer from the inability to (1) quickly adapt to new students using a small amount of data, and (2) measure the reliability of the diagnosis result to avoid improper services that mismatch the student's actual state. In this paper, we propose a general Bayesian mETA-learned Cognitive Diagnosis framework (BETA-CD), which addresses the two challenges by prior knowledge exploitation and model uncertainty quantification, respectively. Specifically, we firstly introduce Bayesian hierarchical modeling to associate each student's cognitive state with a shared prior distribution encoding prior knowledge and a personal posterior distribution indicating model uncertainty. Furthermore, we formulate a meta-learning objective to automatically exploit prior knowledge from historical students, and efficiently solve it with a gradient-based variational inference method. The code will be publicly available at https://github.com/AyiStar/pyat.

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UAI Conference 2022 Conference Paper

Partial likelihood Thompson sampling

Han Wu
Stefan Wager

We consider the problem of deciding how best to target and prioritize existing vaccines that may offer protection against new variants of an infectious disease. Sequential experiments are a promising approach; however, challenges due to delayed feedback and the overall ebb and flow of disease prevalence make available methods inapplicable for this task. We present a method, partial likelihood Thompson sampling, that can handle these challenges. Our method involves running Thompson sampling with belief updates determined by partial likelihood each time we observe an event. To test our approach, we ran a semi-synthetic experiment based on 200 days of COVID-19 infection data in the US.

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IJCAI Conference 2018 Conference Paper

Patent Litigation Prediction: A Convolutional Tensor Factorization Approach

Qi Liu
Han Wu
Yuyang Ye
Hongke Zhao
Chuanren Liu
Dongfang Du

Patent litigation is an expensive legal process faced by many companies. To reduce the cost of patent litigation, one effective approach is proactive management based on predictive analysis. However, automatic prediction of patent litigation is still an open problem due to the complexity of lawsuits. In this paper, we propose a data-driven framework, Convolutional Tensor Factorization (CTF), to identify the patents that may cause litigations between two companies. Specifically, CTF is a hybrid modeling approach, where the content features from the patents are represented by the Network embedding-combined Convolutional Neural Network (NCNN) and the lawsuit records of companies are summarized in a tensor, respectively. Then, CTF integrates NCNN and tensor factorization to systematically exploit both content information and collaborative information from large amount of data. Finally, the risky patents will be returned by a learning to rank strategy. Extensive experimental results on real-world data demonstrate the effectiveness of our framework.

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ICRA Conference 2017 Conference Paper

Three-dimensional robotic control of a 5-micrometer magnetic bead for intra-embryonic navigation and measurement

Xian Wang 0001
Mengxi Luo
Han Wu
Zhuoran Zhang 0001
Jun Liu 0007
Zhensong Xu
Wesley Johnson
Yu Sun 0001

Magnetic micromanipulation has the advantage of untethered control, high precision, and biocompatibility and has recently undergone great advances. The magnetic micromanipulation task to tackle in this work is to three-dimensionally navigate a 5-micrometer magnetic bead inside a mouse embryo and perform mechanical measurements at multiple locations. Existing technologies are not able to achieve these navigation and measurement goals because of poor magnetic force scaling and/or lacking the capability of applying an accurately controlled force. This paper reports a robotic magnetic tweezer system that enables, for the first time, intra- embryonic magnetic navigation and force application. A single magnetic bead was introduced into a mouse embryo via robotic microinjection. The robotic magnetic tweezer system accurately controls the position of the magnetic bead via visually servoed magnetic control. The system is also capable of applying forces up to 120 pN with a resolution of 1. 78 pN for performing mechanical measurements on the cellular structures inside the mouse embryo, revealing that the middle region is more deformable than the side regions of the inner cell mass.

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