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Lei Jiang

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13 papers

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AAAI Conference 2026 Conference Paper

CO2-Meter: A Comprehensive Carbon Footprint Estimator for LLMs on Edge Devices

Zhenxiao Fu
Fan Chen
Lei Jiang

LLMs have transformed NLP, yet deploying them on edge devices poses great carbon challenges. Prior estimators remain incomplete, neglecting peripheral energy use, distinct prefill/decode behaviors, and SoC design complexity. This paper presents CO2-Meter, a unified framework for estimating operational and embodied carbon in LLM edge inference. Contributions include: (1) equation-based peripheral energy models and datasets; (2) a GNN-based predictor with phase-specific LLM energy data; (3) a unit-level embodied carbon model for SoC bottleneck analysis; and (4) validation showing superior accuracy over prior methods. Case studies show CO2-Meter's effectiveness in identifying carbon hotspots and guiding sustainable LLM design on edge platforms.

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AAAI Conference 2026 Conference Paper

CRAF: A Clinical Reasoning-Adaptive Framework via Reinforcement Learning for Similar Case Retrieval

Jie Lin
Lei Jiang
Zongyi Chen
Liansheng Wang

With the advancement of information retrieval (IR) technologies toward deep semantic understanding, reasoning-based methods—featuring explicit chain-of-thought generation—have demonstrated significant advantages in multi-hop and causal reasoning tasks. However, in complex clinical case retrieval scenarios, implicit reasoning cues within clinical data often hinder current models from effectively capturing deep semantic associations between queries and cases. Query rewriting and expansion techniques based on reasoning offer a promising solution to this challenge by uncovering and completing the latent clinical intent behind user queries, thereby enhancing semantic coverage and reasoning sensitivity. In this paper, we propose CRAF, a clinically adaptive reasoning framework tailored for similar case retrieval. Our method generates clinical reasoning paths and incorporates a fine-grained semantic reward mechanism, enabling efficient query rewriting through reinforcement learning. Experimental results on the PMC-Patients benchmark demonstrate that CRAF consistently delivers robust improvements across multiple retrieval tasks, achieving reasoning performance comparable to that of commercial models.

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YNIMG Journal 2026 Journal Article

CT-free attenuation and scatter correction of [11C]CFT brain PET using a Bi-directional matching network

Wenxiang Ding
Xiaolin Sun
Qiaoqiao Ding
Xiaoyue Tan
Qing Zhang
Shanzhen He
Peiyong Li
Qiu Huang

C]CFT brain PET that achieves diagnostic-comparable dopamine transporter (DAT) quantification while avoiding CT-associated radiation. A Bi-directional Discrete Process Matching (Bi-DPM) network was adapted to establish reversible transformations between non-corrected (NASC-PET) and fully corrected (ASC-PET) images through discrete consistency constraints, eliminating the need for pseudo-CT generation or anatomical priors. Evaluated on 90 Parkinsonian syndrome patients, Bi-DPM demonstrated superior performance to Cycle-Consistent Generative Adversarial Networks (CycleGAN), Pix2Pix, and Rectified Flow (RF) across quantitative metrics (lower MAE, higher PSNR/SSIM). For standardized uptake value mean (SUVmean) measurements, Bi-DPM showed excellent agreement with CT-ASC reference (CCC > 0.98, PCC > 0.98). Voxel-wise analysis of DAT-positive/-negative (DAT+/DAT-) groups confirmed Bi-DPM's clinical validity, with statistical significance maps closely aligned to CT-ASC (Dice = 0.953 vs. 0.938 for RF, 0.948 for Pix2Pix and 0.618 for CycleGAN). This approach reduces unnecessary radiation exposure by omitting CT scans while maintaining PET quantification accuracy.

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AAMAS Conference 2026 Conference Paper

GroupDebate: Enhancing the Efficiency of Multi-Agent Debate Using Group Discussion

Tongxuan Liu
Xingyu Wang
Weizhe Huang
Wenjiang Xu
Yuting Zeng
Lei Jiang
Hailong Yang
Jing Li

In recent years, Large Language Models (LLMs) have demonstrated remarkablecapabilitiesacrossdiverseNLPtasks, includingcomplex logical reasoning, mathematical problem-solving, and multi-step decision-making. Extensive research has explored how to enhance the logical reasoning abilities such as Chain-of-Thought, Chain-of- Thought with Self-Consistency, Tree-Of-Thoughts, and multi-agent debates. In the context of multi-agent debates, significant performance improvements can be achieved with an increasing number of agents and debate rounds. However, the escalation in the number of agents and debate rounds can drastically raise the tokens cost of debates, thereby limiting the scalability of the multi-agent debate technique. To better harness the advantages of multi-agent debates in logical reasoning tasks, this paper proposes a method to significantly reduce token cost in multi-agent debates. This approach involves dividing all agents into multiple debate groups, with agents engaging in debates within their respective groups and sharing interim debate results between groups. Comparative experiments across multiple datasets have demonstrated that this method can reduce the total tokens by up to 46. 9% during debates and while potentially enhancing accuracy by as much as 21. 9%. Our method significantly enhances the performance and efficiency of interactions in the multi-agent debate. This work is licensed under a Creative Commons Attribution International 4. 0 License. Proc. of the 25th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2026), C. Amato, L. Dennis, V. Mascardi, J. Thangarajah (eds.), May 25 – 29, 2026, Paphos, Cyprus. © 2026 International Foundation for Autonomous Agents and Multiagent Systems (www. ifaamas. org). https: //doi. org/10. 65109/DSDX8860

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JBHI Journal 2026 Journal Article

PathFusion-Net: A Rough Path Theory-Based Deep Learning Model for ECG Arrhythmia Classification

Tianlong Feng
Qingchen Li
Yuanyuan Zhang
Yongzhi Liao
Di Lu
Liping Wang
Jianqin Zhao
Lei Jiang

This study introduces a novel electrocardiogram (ECG) arrhythmia classification model, PathFusion-Net, which integrates Rough Path Theory with deep learning technologies. The model combines Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM), Path Signatures, and Path Development to extract spatial morphological features from ECG images and multi-order temporal representations from ECG signals. By adopting an inter-patient split paradigm, our approach more closely reflects real-world clinical diagnostic settings compared to intra-patient methods. The model demonstrates state-of-the-art overall classification performance on both the MIT-BIH Arrhythmia Database and a private clinical dataset, achieving 94. 7% and 95. 1% accuracy, respectively, under the AAMI four-class standard with an inter-patient split paradigm. On the MIT-BIH dataset, the proposed method attains competitive precision and recall across multiple arrhythmia types, including 95. 2% /87. 9% for ventricular ectopic beats (V) and 75. 7% /92. 3% for supraventricular ectopic beats (S), indicating balanced performance across clinically diverse categories. This research highlights the potential of Rough Path Theory in time-series analysis and offers a novel deep learning framework for automated early detection and monitoring of ECG arrhythmias. The code used in this study is available at: https://github.com/Rand2AI/PathFusion-Net.

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AAAI Conference 2026 Conference Paper

Structural Entropy Guided Incremental Learning for Open-World Multimodal Social Event Detection

Zhiwei Yang
Haimei Qin
Xiaoyan Yu
Hao Peng
Lei Jiang
Li Sun
Zhiqin Yang

With the explosive growth of multimodal data streams on social media, the timely detection of emerging social events has become increasingly important. As a result, Multimodal Social Event Detection in open-world settings is receiving growing attention. However, most existing methods face two major limitations: (1) They overlook the dynamic nature of open-world social media data and fail to design dedicated incremental learning frameworks. (2) They ignore the impact of noise in streaming data, leading to performance degradation over long-term detection. To overcome these limitations, we propose SeInEvent (**S**tructural **E**ntropy Guided **In**cremental Learning for Open-World Multimodal Social **Event** Detection). Our innovations are as follows: **First**, considering data dynamics, we design a self-supervised alternating incremental contrastive learning mechanism. Through knowledge distillation, historical event clusters were reviewed and consolidated, and contrastive learning was combined to absorb knowledge of unknown events, ultimately achieving incremental learning without labels. **Second**, addressing the impact of noise, we propose a Pointwise Structural Entropy-based noise filter, which quantifies each sample’s informational contribution to the event clustering structure. It enables automatic removal of noisy data and supports robust long-term detection. Extensive experiments on two public datasets demonstrate that SeInEvent achieves superior performance.

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IJCAI Conference 2025 Conference Paper

T-T: Table Transformer for Tagging-based Aspect Sentiment Triplet Extraction

Kun Peng
Chaodong Tong
Cong Cao
Hao Peng
Qian Li
Guanlin Wu
Lei Jiang
Yanbing Liu

Aspect sentiment triplet extraction (ASTE) aims to extract triplets composed of aspect terms, opinion terms, and sentiment polarities from given sentences. The table tagging method is a popular approach to addressing this task, which encodes a sentence into a 2-dimensional table, allowing for the tagging of relations between any two words. Previous efforts have focused on designing various downstream relation learning modules to better capture interactions between tokens in the table, revealing that a stronger capability in relation capture can lead to greater improvements in the model. Motivated by this, we attempt to directly utilize transformer layers as downstream relation learning modules. Due to the powerful semantic modeling capability of transformers, it is foreseeable that this will lead to excellent improvement. However, owing to the quadratic relation between the length of the table and the length of the input sentence sequence, using transformers directly faces two challenges: overly long table sequences and unfair local attention interaction. To address these challenges, we propose a novel Table-Transformer (T-T) for the tagging-based ASTE method. Specifically, we introduce a stripe attention mechanism with a loop-shift strategy to tackle these challenges. The former modifies the global attention mechanism to only attend to a 2-dimensional local attention window, while the latter facilitates interaction between different attention windows. Extensive and comprehensive experiments demonstrate that the T-T, as a downstream relation learning module, achieves state-of-the-art performance with lower computational costs.

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JBHI Journal 2023 Journal Article

An Improved Combination of Faster R-CNN and U-Net Network for Accurate Multi-Modality Whole Heart Segmentation

Hengfei Cui
Yifan Wang
Yan Li
Di Xu
Lei Jiang
Yong Xia
Yanning Zhang

Detailed information of substructures of the whole heart is usually vital in the diagnosis of cardiovascular diseases and in 3D modeling of the heart. Deep convolutional neural networks have been demonstrated to achieve state-of-the-art performance in 3D cardiac structures segmentation. However, when dealing with high-resolution 3D data, current methods employing tiling strategies usually degrade segmentation performances due to GPU memory constraints. This work develops a two-stage multi-modality whole heart segmentation strategy, which adopts an improved Combination of Faster R-CNN and 3D U-Net (CFUN+). More specifically, the bounding box of the heart is first detected by Faster R-CNN, and then the original Computed Tomography (CT) and Magnetic Resonance Imaging (MRI) images of the heart aligned with the bounding box are input into 3D U-Net for segmentation. The proposed CFUN+ method redefines the bounding box loss function by replacing the previous Intersection over Union (IoU) loss with Complete Intersection over Union (CIoU) loss. Meanwhile, the integration of the edge loss makes the segmentation results more accurate, and also improves the convergence speed. The proposed method achieves an average Dice score of 91. 1% on the Multi-Modality Whole Heart Segmentation (MM-WHS) 2017 challenge CT dataset, which is 5. 2% higher than the baseline CFUN model, and achieves state-of-the-art segmentation results. In addition, the segmentation speed of a single heart has been dramatically improved from a few minutes to less than 6 seconds.

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NeurIPS Conference 2020 Conference Paper

AutoPrivacy: Automated Layer-wise Parameter Selection for Secure Neural Network Inference

Qian Lou
Song Bian
Lei Jiang

Hybrid Privacy-Preserving Neural Network (HPPNN) implementing linear layers by Homomorphic Encryption (HE) and nonlinear layers by Garbled Circuit (GC) is one of the most promising secure solutions to emerging Machine Learning as a Service (MLaaS). Unfortunately, a HPPNN suffers from long inference latency, e. g. , $\sim100$ seconds per image, which makes MLaaS unsatisfactory. Because HE-based linear layers of a HPPNN cost $93\%$ inference latency, it is critical to select a set of HE parameters to minimize computational overhead of linear layers. Prior HPPNNs over-pessimistically select huge HE parameters to maintain large noise budgets, since they use the same set of HE parameters for an entire network and ignore the error tolerance capability of a network. In this paper, for fast and accurate secure neural network inference, we propose an automated layer-wise parameter selector, AutoPrivacy, that leverages deep reinforcement learning to automatically determine a set of HE parameters for each linear layer in a HPPNN. The learning-based HE parameter selection policy outperforms conventional rule-based HE parameter selection policy. Compared to prior HPPNNs, AutoPrivacy-optimized HPPNNs reduce inference latency by $53\%\sim70\%$ with negligible loss of accuracy.

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NeurIPS Conference 2020 Conference Paper

Falcon: Fast Spectral Inference on Encrypted Data

Qian Lou
Wen-jie Lu
Cheng Hong
Lei Jiang

Homomorphic Encryption (HE) based secure Neural Networks(NNs) inference is one of the most promising security solutions to emerging Machine Learning as a Service (MLaaS). In the HE-based MLaaS setting, a client encrypts the sensitive data, and uploads the encrypted data to the server that directly processes the encrypted data without decryption, and returns the encrypted result to the client. The clients' data privacy is preserved since only the client has the private key. Existing HE-enabled Neural Networks (HENNs), however, suffer from heavy computational overheads. The state-of-the-art HENNs adopt ciphertext packing techniques to reduce homomorphic multiplications by packing multiple messages into one single ciphertext. Nevertheless, rotations are required in these HENNs to implement the sum of the elements within the same ciphertext. We observed that HENNs have to pay significant computing overhead on rotations, and each of rotations is $\sim 10\times$ more expensive than homomorphic multiplications between ciphertext and plaintext. So the massive rotations have become a primary obstacle of efficient HENNs. In this paper, we propose a fast, frequency-domain deep neural network called Falcon, for fast inferences on encrypted data. Falcon includes a fast Homomorphic Discrete Fourier Transform (HDFT) using block-circulant matrices to homomorphically support spectral operations. We also propose several efficient methods to reduce inference latency, including Homomorphic Spectral Convolution and Homomorphic Spectral Fully Connected operations by combing the batched HE and block-circulant matrices. Our experimental results show Falcon achieves the state-of-the-art inference accuracy and reduces the inference latency by $45. 45\%\sim 85. 34\%$ over prior HENNs on MNIST and CIFAR-10.

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NeurIPS Conference 2020 Conference Paper

Glyph: Fast and Accurately Training Deep Neural Networks on Encrypted Data

Qian Lou
Bo Feng
Geoffrey Charles Fox
Lei Jiang

Because of the lack of expertise, to gain benefits from their data, average users have to upload their private data to cloud servers they may not trust. Due to legal or privacy constraints, most users are willing to contribute only their encrypted data, and lack interests or resources to join deep neural network (DNN) training in cloud. To train a DNN on encrypted data in a completely non-interactive way, a recent work proposes a fully homomorphic encryption (FHE)-based technique implementing all activations by \textit{Brakerski-Gentry-Vaikuntanathan} (BGV)-based lookup tables. However, such inefficient lookup-table-based activations significantly prolong private training latency of DNNs. In this paper, we propose, Glyph, an FHE-based technique to fast and accurately train DNNs on encrypted data by switching between TFHE (Fast Fully Homomorphic Encryption over the Torus) and BGV cryptosystems. Glyph uses logic-operation-friendly TFHE to implement nonlinear activations, while adopts vectorial-arithmetic-friendly BGV to perform multiply-accumulations (MACs). Glyph further applies transfer learning on DNN training to improve test accuracy and reduce the number of MACs between ciphertext and ciphertext in convolutional layers. Our experimental results show Glyph obtains state-of-the-art accuracy, and reduces training latency by 69%~99% over prior FHE-based privacy-preserving techniques on encrypted datasets.

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IJCAI Conference 2019 Conference Paper

Feature Evolution Based Multi-Task Learning for Collaborative Filtering with Social Trust

Qitian Wu
Lei Jiang
Xiaofeng Gao
Xiaochun Yang
Guihai Chen

Social recommendation could address the data sparsity and cold-start problems for collaborative filtering by leveraging user trust relationships as auxiliary information for recommendation. However, most existing methods tend to consider the trust relationship as preference similarity in a static way and model the representations for user preference and social trust via a common feature space. In this paper, we propose TrustEV and take the view of multi-task learning to unite collaborative filtering for recommendation and network embedding for user trust. We design a special feature evolution unit that enables the embedding vectors for two tasks to exchange their features in a probabilistic manner, and further harness a meta-controller to globally explore proper settings for the feature evolution units. The training process contains two nested loops, where in the outer loop, we optimize the meta-controller by Bayesian optimization, and in the inner loop, we train the feedforward model with given feature evolution units. Experiment results show that TrustEV could make better use of social information and greatly improve recommendation MAE over state-of-the-art approaches.

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NeurIPS Conference 2019 Conference Paper

SHE: A Fast and Accurate Deep Neural Network for Encrypted Data

Qian Lou
Lei Jiang

Homomorphic Encryption (HE) is one of the most promising security solutions to emerging Machine Learning as a Service (MLaaS). Several Leveled-HE (LHE)-enabled Convolutional Neural Networks (LHECNNs) are proposed to implement MLaaS to avoid the large bootstrapping overhead. However, prior LHECNNs have to pay significant computational overhead but achieve only low inference accuracy, due to their polynomial approximation activations and poolings. Stacking many polynomial approximation activation layers in a network greatly reduces the inference accuracy, since the polynomial approximation activation errors lead to a low distortion of the output distribution of the next batch normalization layer. So the polynomial approximation activations and poolings have become the obstacle to a fast and accurate LHECNN model. In this paper, we propose a Shift-accumulation-based LHE-enabled deep neural network (SHE) for fast and accurate inferences on encrypted data. We use the binary-operation-friendly leveled-TFHE (LTFHE) encryption scheme to implement ReLU activations and max poolings. We also adopt the logarithmic quantization to accelerate inferences by replacing expensive LTFHE multiplications with cheap LTFHE shifts. We propose a mixed bitwidth accumulator to expedite accumulations. Since the LTFHE ReLU activations, max poolings, shifts and accumulations have small multiplicative depth, SHE can implement much deeper network architectures with more convolutional and activation layers. Our experimental results show SHE achieves the state-of-the-art inference accuracy and reduces the inference latency by 76. 21% ~ 94. 23% over prior LHECNNs on MNIST and CIFAR-10.

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