Author name cluster

Man Luo

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11 papers

2 author rows

EAAI Journal 2026 Journal Article

Automatic zero-voltage-switching dynamic feature extraction and regression modeling for high-efficiency cascode gallium nitride/silicon carbide Class-E inverters

Jiachi Xian
Shijun Luo
Xiuzhang Shang
Man Luo
Chenhui Yu

Cascode gallium nitride/silicon carbide (GaN/SiC) switches have become a transformative technology for Class-E inverters due to their high-frequency and low-power characteristics, enabling improved transfer efficiency of the inverter via zero voltage switching (ZVS). However, quantifying the relationship between ZVS dynamics and transfer efficiency is challenging. This work proposes a deep learning-based method to automatically extract ZVS dynamic features from switching waveforms and integrate them with static parameters for high-precision efficiency prediction. The method employs statistical analysis and Fourier transform for initial feature extraction, followed by a random forest (RF) and Pearson correlation analysis to refine the feature set. Validated through multiple regression models, the framework achieves a mean absolute percentage error (MAPE) below 0. 098% and a coefficient of determination (R2) above 0. 95 in predicting the transfer efficiency of cascode GaN/SiC Class-E inverters, with a prediction latency as low as 2. 29 millisecond. Furthermore, SHapley Additive exPlanations is integrated to reveal the internal decision-making logic of the model, providing physically consistent interpretations of how ZVS dynamic features influence the transfer efficiency. Specifically, the proposed method effectively suppresses the static parameter drift seen in traditional models, reducing the absolute prediction deviation in high-efficiency (greater than 95%) regions and achieving a 42. 2% accuracy improvement. Strong validations with experimental dataset are finally demonstrated, with the hardware prototype reaching a maximum efficiency of 93. 77%. This work provides an interpretable deep learning paradigm for the automated design and analysis of high-efficiency Class-E inverters.

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IJCAI Conference 2025 Conference Paper

BGM: Demand Prediction for Expanding Bike-Sharing Systems with Dynamic Graph Modeling

Yixuan Zhao
Hongkai Wen
Xingchen Zhang
Man Luo

Accurate demand prediction is crucial for the equitable and sustainable expansion of bike-sharing systems, which help reduce urban congestion, promote low-carbon mobility, and improve transportation access in underserved areas. However, expanding these systems presents societal challenges, particularly in ensuring fair resource distribution and operational efficiency. A major hurdle is the difficulty of demand prediction at new stations, which lack historical usage data and are heavily influenced by the existing network. Additionally, new stations dynamically reshape demand patterns across time and space, complicating efforts to balance supply and accessibility in evolving urban environments. Existing methods model relationships between new and existing stations but often assume static patterns, overlooking how new stations reshape demand dynamics over time and space. To tackle these challenges, we propose a novel demand prediction framework for expanding bike-sharing systems, namely BGM, which leverages dynamic graph modeling to capture the evolving inter-station correlations while accounting for spatial and temporal heterogeneity. Specifically, we develop a knowledge transfer approach that studies the embeddings transformation across existing and new stations through a learnable orthogonal mapping matrix. We further design a gated selecting vector-based feature fusion mechanism to integrate the transferred embeddings and the intrinsic features of stations for precise predictions. Experiments on real-world bike-sharing data demonstrate that BGM outperforms existing methods.

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NeurIPS Conference 2025 Conference Paper

Evaluating the Inductive Abilities of Large Language Models: Why Chain-of-Thought Reasoning Sometimes Hurts More Than Helps

Haibo Jin
Peiyan Zhang
Man Luo
Haohan Wang

Large Language Models (LLMs) have shown remarkable progress across domains, yet their ability to perform inductive reasoning—inferring latent rules from sparse examples—remains limited. It is often assumed that chain-of-thought (CoT) prompting, as used in Large Reasoning Models (LRMs), enhances such reasoning. We investigate this assumption with creating four controlled, diagnostic game-based tasks—chess, Texas Hold’em, dice games, and blackjack—with hidden human-defined rules. We find that CoT reasoning can degrade inductive performance, with LRMs often underperforming their non-reasoning counterparts. To explain this, we present a theoretical framework that reveals how reasoning steps can amplify error through three failure modes: incorrect sub-task decomposition, incorrect sub-task solving, and incorrect final answer summarization. Based on our theoretical and empirical analysis, we introduce structured interventions that adapt CoT generation according to our identified failure types. These interventions improve inductive accuracy without retraining. Our findings suggest that effective (CoT) reasoning depends not only on taking more steps but also on ensuring those steps are well-structured.

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IJCAI Conference 2025 Conference Paper

High-Fidelity Road Network Generation with Latent Diffusion Models

Jinming Wang
Hongkai Wen
Geyong Min
Man Luo

Road networks are the vein of modern cities. Yet, maintaining up-to-date and accurate road network information is a persistent challenge, especially in areas with rapid urban changes or limited surveying resources. Crowdsourced trajectories, e. g. , from GPS records collected by mobile devices and vehicles, have emerged as a powerful data source for continuously mapping the urban areas. However, the inherent noise, irregular and often sparse sampling rates, and the vast variability in movement patterns make the problem of road network generation from trajectories a non-trivial task. Existing methods often approach this from an appearance-based perspective: they typically render trajectories as 2D density maps and then employ heuristic algorithms to extract road networks - leading to inevitable information loss and thus poor performance especially when trajectories are sparse or ambiguities present, e. g. flyovers. In this paper, we propose a novel approach, called GraphWalker, to generate high-fidelity road network graphs from raw trajectories in an end-to-end manner. We achieve this by designing a bespoke latent diffusion transformer T2W-DiT, which treats input trajectories as generation conditions, and gradually denoises samples from a latent space to obtain the corresponding walks on the underlying road network graph - then assemble them together as the final road network. Extensive experiments on multiple datasets demonstrate the proposed GraphWalker can effectively generate high quality road networks from noisy and sparse trajectories, showcasing significant improvements over state-of-the-art.

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ICML Conference 2025 Conference Paper

Revolve: Optimizing AI Systems by Tracking Response Evolution in Textual Optimization

Peiyan Zhang
Haibo Jin
Leyang Hu
Xinnuo Li
Liying Kang
Man Luo
Yangqiu Song
Haohan Wang

Recent advancements in large language models (LLMs) have significantly enhanced the ability of LLM-based systems to perform complex tasks through natural language processing and tool interaction. However, optimizing these LLM-based systems for specific tasks remains challenging, often requiring manual interventions like prompt engineering and hyperparameter tuning. Existing automatic optimization methods, such as textual feedback-based techniques ( e. g. , TextGrad), tend to focus on immediate feedback, analogous to using immediate derivatives in traditional numerical gradient descent. However, relying solely on such feedback can be limited when the adjustments made in response to this feedback are either too small or fluctuate irregularly, potentially slowing down or even stalling the optimization process. In this paper, we introduce $\textbf{REVOLVE}$, an optimization method that tracks how $\textbf{R}$esponses $\textbf{EVOLVE}$ across iterations in LLM systems. By focusing on the evolution of responses over time, REVOLVE enables more stable and effective optimization by making thoughtful, progressive adjustments at each step. Experiments across three tasks demonstrate the adaptability and efficiency of our proposal. Beyond its practical contributions, REVOLVE highlights a promising direction, where the rich knowledge from established optimization principles can be leveraged to enhance LLM systems, which paves the way for further advancements in this hybrid domain. Code is available at: https: //llm-revolve. netlify. app.

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ICML Conference 2025 Conference Paper

SK-VQA: Synthetic Knowledge Generation at Scale for Training Context-Augmented Multimodal LLMs

Xin Su 0008
Man Luo
Kris W. Pan
Tien Pei Chou
Vasudev Lal
Phillip Howard

Multimodal retrieval-augmented generation (RAG) plays a crucial role in domains such as knowledge-based visual question answering (KB-VQA), where models should effectively integrate additional knowledge to generate a response. However, existing vision and language models (VLMs) are not inherently designed for context-augmented generation, limiting their effectiveness in such tasks. While synthetic data generation has recently gained attention for training large VLMs, its application for context-augmented generation remains underexplored. To address this gap, we introduce SKVQA, a large-scale synthetic multimodal dataset containing over 2 million visual question-answer pairs, each associated with external knowledge sources to determine the final answer. Compared to previous datasets, SKVQA exhibits 11$\times$ more unique questions, greater domain diversity, and a broader spectrum of image sources. Through human evaluations, we confirm the high quality of the generated question-answer pairs and their contextual relevance. Extensive experiments show that SKVQA serves both as a challenging benchmark for knowledge-based VQA and as an effective training resource for adapting generative multimodal models to context-augmented generation. Our results further indicate that models trained on SKVQA demonstrate enhanced generalization in both context-aware VQA and multimodal RAG settings.

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TMLR Journal 2024 Journal Article

In-context Learning with Retrieved Demonstrations for Language Models: A Survey

Man Luo
Xin Xu
Yue Liu
Panupong Pasupat
Mehran Kazemi

Large language models have demonstrated remarkable few-shot in-context learning (ICL) capabilities, adapting to new tasks with few-shots demonstrations. However, the efficacy of ICL is highly dependent on the selection of these demonstrations. Recent developments have introduced retrieval-based in-context learning (RetICL), which dynamically retrieves demonstrations tailored to each input query. This approach leverages existing databases and retrieval systems, enhancing efficiency and scalability while mitigating biases inherent in manual example selection. Given the promising results and growing interest in RetICL, we present a comprehensive survey of this field. Our review encompasses: design choices for ICL demonstration retrieval models, retrieval training procedures, inference strategies and current applications of RetICL. In the end, we explore future directions for this emerging technology.

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AAAI Conference 2022 Conference Paper

Improving Biomedical Information Retrieval with Neural Retrievers

Man Luo
Arindam Mitra
Tejas Gokhale
Chitta Baral

Information retrieval (IR) is essential in search engines and dialogue systems as well as natural language processing tasks such as open-domain question answering. IR serve an important function in the biomedical domain, where content and sources of scientific knowledge may evolve rapidly. Although neural retrievers have surpassed traditional IR approaches such as TF-IDF and BM25 in standard open-domain question answering tasks, they are still found lacking in the biomedical domain. In this paper, we seek to improve information retrieval (IR) using neural retrievers (NR) in the biomedical domain, and achieve this goal using a three-pronged approach. First, to tackle the relative lack of data in the biomedical domain, we propose a template-based question generation method that can be leveraged to train neural retriever models. Second, we develop two novel pre-training tasks that are closely aligned to the downstream task of information retrieval. Third, we introduce the “Poly-DPR” model which encodes each context into multiple context vectors. Extensive experiments and analysis on the BioASQ challenge suggest that our proposed method leads to large gains over existing neural approaches and beats BM25 in the small-corpus setting. We show that BM25 and our method can complement each other, and a simple hybrid model leads to further gains in the large corpus setting.

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AAAI Conference 2022 Conference Paper

Privacy-Preserving Face Recognition in the Frequency Domain

Yinggui Wang
Jian Liu
Man Luo
Le Yang
Li Wang

Some applications require performing face recognition (FR) on third-party servers, which could be accessed by attackers with malicious intents to compromise the privacy of users’ face information. This paper advocates a practical privacypreserving frequency-domain FR scheme without key management. The new scheme first collects the components with the same frequency from different blocks of a face image to form component channels. Only part of the channels are retained and fed into the analysis network that performs an interpretable privacy-accuracy trade-off analysis to identify channels important for face image visualization but not crucial for maintaining high FR accuracy. For this purpose, the loss function of the analysis network consists of the empirical FR error loss and a face visualization penalty term, and the network is trained in an end-to-end manner. We find that with the developed analysis network, more than 94% of the image energy can be dropped while the face recognition accuracy stays almost undegraded. In order to further protect the remaining frequency components, we propose a fast masking method. Effectiveness of the new scheme in removing the visual information of face images while maintaining their distinguishability is validated over several large face datasets. Results show that the proposed scheme achieves a recognition performance and inference time comparable to ArcFace operating on original face images directly.

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IJCAI Conference 2020 Conference Paper

Rebalancing Expanding EV Sharing Systems with Deep Reinforcement Learning

Man Luo
Wenzhe Zhang
Tianyou Song
Kun Li
Hongming Zhu
Bowen Du
Hongkai Wen

Electric Vehicle (EV) sharing systems have recently experienced unprecedented growth across the world. One of the key challenges in their operation is vehicle rebalancing, i. e. , repositioning the EVs across stations to better satisfy future user demand. This is particularly challenging in the shared EV context, because i) the range of EVs is limited while charging time is substantial, which constrains the rebalancing options; and ii) as a new mobility trend, most of the current EV sharing systems are still continuously expanding their station networks, i. e. , the targets for rebalancing can change over time. To tackle these challenges, in this paper we model the rebalancing task as a Multi-Agent Reinforcement Learning (MARL) problem, which directly takes the range and charging properties of the EVs into account. We propose a novel approach of policy optimization with action cascading, which isolates the non-stationarity locally, and use two connected networks to solve the formulated MARL. We evaluate the proposed approach using a simulator calibrated with 1-year operation data from a real EV sharing system. Results show that our approach significantly outperforms the state-of-the-art, offering up to 14% gain in order satisfied rate and 12% increase in net revenue.

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YNICL Journal 2017 Journal Article

Early cortical biomarkers of longitudinal transcutaneous vagus nerve stimulation treatment success in depression

Jiliang Fang
Natalia Egorova
Peijing Rong
Jun Liu
Yang Hong
Yangyang Fan
Xiaoling Wang
Honghong Wang

Transcutaneous vagus nerve stimulation (tVNS), a non-invasive method of brain stimulation through the auricular branch of the vagus nerve, has shown promising results in treating major depressive disorder (MDD) in several pilot studies. However, the neural mechanism by which the effect on depression might be achieved has not been fully investigated, with only a few neuroimaging studies demonstrating tVNS-induced changes in the brains of healthy volunteers. Identifying specific neural pathways, which are influenced by tVNS compared with sham in depressed individuals, as well as determining neurobiomarkers of tVNS treatment success are needed to advance the application of tVNS for MDD. In order to address these questions, we measured fMRI brain activity of thirty-eight depressed patients assigned to undergo tVNS (n =17) or sham (n =21) treatment for 4weeks, during the first stimulation session. The results showed significant fMRI signal increases in the left anterior insula, revealed by a direct comparison of tVNS and sham stimulation. Importantly, the insula activation level during the first stimulation session in the tVNS group was significantly associated with the clinical improvement at the end of the four-week treatment, as indicated by the Hamilton Depression Rating Scale (HAM-D) score. Our findings suggest that anterior insula fMRI activity could serve as a potential cortical biomarker and an early predictor of tVNS longitudinal treatment success.

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