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Tianxiang Chen

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8 papers

1 author row

AAAI Conference 2026 Conference Paper

Flora: Effortless Context Construction to Arbitrary Length and Scale

Tianxiang Chen
Zhentao Tan
Xiaofan Bo
Yue Wu
Tao Gong
Qi Chu
Jieping Ye

Effectively handling long contexts is challenging for Large Language Models (LLMs) due to the rarity of long texts, high computational demands, and substantial forgetting of short-context abilities. Recent approaches have attempted to construct long contexts for instruction tuning, but these methods often require LLMs or human interventions, which are both costly and limited in length and diversity. Also, the drop in short-context performances of present long-context LLMs remains significant. In this paper, we introduce Flora, an effortless (human/LLM-free) long-context construction strategy. Flora can markedly enhance the long-context performance of LLMs by arbitrarily assembling short instructions based on categories and instructing LLMs to generate responses based on long-context meta-instructions. This enables Flora to produce contexts of arbitrary length and scale with rich diversity, while only slightly compromising short-context performance. Experiments on Llama3-8B-Instruct and QwQ-32B show that LLMs enhanced by Flora excel in three long-context benchmarks while maintaining strong performances in short-context tasks.

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AAAI Conference 2026 Conference Paper

Oblivionis: A Lightweight Learning and Unlearning Framework for Federated Large Language Models

Fuyao Zhang
Xinyu Yan
Tiantong Wu
Wenjie Li
Tianxiang Chen
Yang Cao
Ran Yan
Longtao Huang

Large Language Models (LLMs) increasingly leverage Federated Learning (FL) to utilize private, task-specific datasets for fine-tuning while preserving data privacy. However, while federated LLM frameworks effectively enable collaborative training without raw data sharing, they critically lack built-in mechanisms for regulatory compliance like GDPR’s right to be forgotten. Integrating private data heightens concerns over data quality and long-term governance, yet existing distributed training frameworks offer no principled way to selectively remove specific client contributions post-training. Due to distributed data silos, stringent privacy constraints, and the intricacies of interdependent model aggregation, federated LLM unlearning is significantly more complex than centralized LLM unlearning. To address this gap, we introduce Oblivionis, a lightweight learning and unlearning framework that enables clients to selectively remove specific private data during federated LLM training, enhancing trustworthiness and regulatory compliance. By unifying FL and unlearning as a dual optimization objective, we incorporate 6 FL and 5 unlearning algorithms for comprehensive evaluation and comparative analysis, establishing a robust pipeline for federated LLM unlearning. Extensive experiments demonstrate that Oblivionis outperforms local training, achieving a robust balance between forgetting efficacy and model utility, with cross-algorithm comparisons providing clear directions for future LLM development.

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EAAI Journal 2026 Journal Article

Shape-aware medical image segmentation via frequency domain partitioning

Ke Zhou
Tianxiang Chen
Jiayuan Huang
Dongmei Fu
Chuanjiang Qi

Precise medical image segmentation is vital for computer-aided diagnosis, yet current methods struggle with subtle endoscopic areas where lesions and normal tissue appear similar. To address this, we propose a shape-aware partitioning model with a dual-branch architecture. Its high-frequency branch captures edges and fine details, while the low-frequency branch focuses on overall shape and color distribution. The proposed model integrates these features via a hybrid decoder and a chimeric wavelet block, facilitating continuous bilateral information interaction. We also introduce a dual-domain loss function to comprehensively evaluate model output against ground truth, especially when pixel value differences are small but frequency domain differences are significant. The proposed method markedly enhances the accuracy and efficiency of computer-aided diagnosis, particularly in polyp and skin lesion segmentation. By accurately capturing lesion shape and volume, it provides a robust tool crucial for disease grading and treatment planning. Moreover, it outperforms comparable hybrid architectures integrating convolutional neural networks and transformers on public endoscopic polyp segmentation benchmarks. Quantitatively, it achieves a 15. 29% higher intersection over union than conventional hybrid networks with a 33. 75 giga floating-point operations reduction. Furthermore, it shows a 5. 93% improvement over hierarchical hybrid models with an 11. 74 giga floating-point operations decrease.

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AAAI Conference 2026 Conference Paper

Unlocking Dynamic Inter-Client Spatial Dependencies: A Federated Spatio-temporal Graph Learning Method for Traffic Flow Forecasting

Feng Wang
Tianxiang Chen
Shuyue Wei
Qian Chu
Yi Zhang
Yifan Sun
Zhiming Zheng

Spatio-temporal graphs are powerful tools for modeling complex dependencies in traffic time series. However, the distributed nature of real-world traffic data across multiple stakeholders poses significant challenges in modeling and reconstructing inter-client spatial dependencies while adhering to data locality constraints. Existing methods primarily address static dependencies, overlooking their dynamic nature and resulting in suboptimal performance. In response, we propose Federated Spatio-Temporal Graph with Dynamic Inter-Client Dependencies (FedSTGD), a framework designed to model and reconstruct dynamic inter-client spatial dependencies in federated learning. FedSTGD incorporates a federated nonlinear computation decomposition module to approximate complex graph operations. This is complemented by a graph node embedding augmentation module, which alleviates performance degradation arising from the decomposition. These modules are coordinated through a client-server collective learning protocol, which decomposes dynamic inter-client spatial dependency learning tasks into lightweight, parallelizable subtasks. Extensive experiments on four real-world datasets demonstrate that FedSTGD achieves superior performance over state-of-the-art baselines in terms of RMSE, MAE, and MAPE, approaching that of centralized baselines. Ablation studies confirm the contribution of each module in addressing dynamic inter-client spatial dependencies, while sensitivity analysis highlights the robustness of FedSTGD to variations in hyperparameters.

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EAAI Journal 2025 Journal Article

Lightweight deep learning based solar cells defect detection using electroluminescence images

Zhicong Chen
Tianxiang Chen
Haoxin Zheng
Lijun Wu
Shuying Cheng
Peijie Lin

Solar cells are the fundamental core energy harvesting components in photovoltaic (PV) power generation stations. In view of the capability of detecting the invisible defects, the electroluminescence (EL) imaging is broadly used in the production lines of solar cells, based on which the deep learning technique is introduced to implement automatic defect detection and classification. However, the current deep learning models feature high complexity and require much computation resources, which are difficult to deploy in edge devices for real time applications. To tackle this issue, we proposed a novel lightweight and high-precision deep learning model named Cross Stage Partial Photovoltaic-You Only Look Once (CSPV-YOLO) based on the deep learning framework You Only Look Once v5 (YOLOv5) to enable the real-time solar cell defect detection. Firstly, a new module Cross Stage Partial C5 (CSPC5) is proposed to replace the initial C3 module in the YOLOv5 network to enhance the network accuracy in recognizing different types of defects. Secondly, a novel Spatial Pyramid Pooling with Cross Stage Partial (SPPFCSP) module is designed to replace the original Spatial Pyramid Pooling Fast (SPPF), which boosts the network feature extraction capabilities from defect targets at multiple scales and facilitates a more efficient integration of multiscale features. Finally, the original loss function of YOLOv5 is replaced by the Scylla intersection over union (SIoU) function to optimize the training model. The proposed models have been validated and intensively compared with many other state-of-the-art models on two public datasets. Firstly, results of experiments on the public Pascal Visual Object Classes (PASCAL VOC) 2007 datasets demonstrate that the proposed SPPFCSP block is obviously superior to other Spatial Pyramid Pooling blocks for the most state-of-the-art YOLO detectors, which can significantly improve the detection accuracy. The comparison results of experiments on the public Photovoltaic Electroluminescence Anomaly Detection Dataset (PVEL-AD) that includes 12-class defects obviously indicate that the proposed CSPV-YOLO model is better than many state-of-the-art models and achieves 91. 5 % average precision (AP) and frames per second (FPS) of 177. 8 on with only 2. 2 million (M) parameters. Hence, it is suitable for the deployment on edge devices for real-time applications.

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EAAI Journal 2025 Journal Article

Research, application, and challenges of causal inference in industrial fault diagnosis: A survey

Bo Li
Qiang Li
Tingfeng Du
Dong Liu
Qiang Yang
Tianxiang Chen
Jing Xiong
Bo Peng

Industrial fault diagnosis technologies leveraging convolutional neural networks and other advanced neural network architectures are pivotal for ensuring stable equipment operation, enhancing production efficiency, and minimizing maintenance costs. Nevertheless, these methods encounter inherent challenges due to data constraints and the complexity of production environments, particularly in identifying fault root causes and ensuring the interpretability of models. The integration of causal inference into industrial fault diagnosis offers significant promise for elucidating fault propagation pathways, revealing causal interrelations within complex systems, and advancing model interpretability. This survey presents a holistic review of research trajectories, pivotal technologies, and methodological advancements in causal inference for industrial fault diagnosis while systematically delineating the advantages and prospective challenges in this domain. First, this paper examines the limitations of conventional machine-learning approaches in fault diagnosis and traces the evolutionary trajectory of causal inference development in this context. Subsequently, the core theories and foundational technologies underpinning causal inference in industrial fault diagnosis are comprehensively discussed. Following this, the survey categorizes the existing literature according to different causal inferences to solve specific problems in industrial fault diagnosis and delves into detailed case studies, underscoring their utility in addressing distinct challenges. Finally, this survey synthesizes insights from existing literature to encapsulate the merits of causal inference in industrial fault diagnosis and to elucidate the prospective challenges it may encounter.

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AAAI Conference 2024 Conference Paper

TCI-Former: Thermal Conduction-Inspired Transformer for Infrared Small Target Detection

Tianxiang Chen
Zhentao Tan
Qi Chu
Yue Wu
Bin Liu
Nenghai Yu

Infrared small target detection (ISTD) is critical to national security and has been extensively applied in military areas. ISTD aims to segment small target pixels from background. Most ISTD networks focus on designing feature extraction blocks or feature fusion modules, but rarely describe the ISTD process from the feature map evolution perspective. In the ISTD process, the network attention gradually shifts towards target areas. We abstract this process as the directional movement of feature map pixels to target areas through convolution, pooling and interactions with surrounding pixels, which can be analogous to the movement of thermal particles constrained by surrounding variables and particles. In light of this analogy, we propose Thermal Conduction-Inspired Transformer (TCI-Former) based on the theoretical principles of thermal conduction. According to thermal conduction differential equation in heat dynamics, we derive the pixel movement differential equation (PMDE) in the image domain and further develop two modules: Thermal Conduction-Inspired Attention (TCIA) and Thermal Conduction Boundary Module (TCBM). TCIA incorporates finite difference method with PMDE to reach a numerical approximation so that target body features can be extracted. To further remove errors in boundary areas, TCBM is designed and supervised by boundary masks to refine target body features with fine boundary details. Experiments on IRSTD-1k and NUAA-SIRST demonstrate the superiority of our method.

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IJCAI Conference 2023 Conference Paper

Fluid Dynamics-Inspired Network for Infrared Small Target Detection

Tianxiang Chen
Qi Chu
Bin Liu
Nenghai Yu

Most infrared small target detection (ISTD) networks focus on building effective neural blocks or feature fusion modules but none describes the ISTD process from the image evolution perspective. The directional evolution of image pixels influenced by convolution, pooling and surrounding pixels is analogous to the movement of fluid elements constrained by surrounding variables ang particles. Inspired by this, we explore a novel research routine by abstracting the movement of pixels in the ISTD process as the flow of fluid in fluid dynamics (FD). Specifically, a new Fluid Dynamics-Inspired Network (FDI-Net) is devised for ISTD. Based on Taylor Central Difference (TCD) method, the TCD feature extraction block is designed, where convolution and Transformer structures are combined for local and global information. The pixel motion equation during the ISTD process is derived from the Navier–Stokes (N-S) equation, constructing a N-S Refinement Module that refines extracted features with edge details. Thus, the TCD feature extraction block determines the primary movement direction of pixels during detection, while the N-S Refinement Module corrects some skewed directions of the pixel stream to supplement the edge details. Experiments on IRSTD-1k and SIRST demonstrate that our method achieves SOTA performance in terms of evaluation metrics.

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