Hui Li

Yunsheng Tian, Karl D. D. Willis, Bassel Al Omari et al.

The automated assembly of complex products requires a system that can automatically plan a physically feasible sequence of actions for assembling many parts together. In this paper, we present ASAP, a physics-based planning approach for automatically generating such a sequence for general-shaped assemblies. ASAP accounts for gravity to design a sequence where each sub-assembly is physically stable with a limited number of parts being held and a support surface. We apply efficient tree search algorithms to reduce the combinatorial complexity of determining such an assembly sequence. The search can be guided by either geometric heuristics or graph neural networks trained on data with simulation labels. Finally, we show the superior performance of ASAP at generating physically realistic assembly sequence plans on a large dataset of hundreds of complex product assemblies. We further demonstrate the applicability of ASAP on both simulation and real-world robotic setups. Project website: asap.csail.mit.edu

Qi Li, Yong Huang, Hui Li

Data quality assessment is an essential step that ensures the reliability of the subsequent structural health monitoring (SHM) tasks. This study proposes a prediction deviation-based SHM data quality assessment method using a univariate implicit auto-regressive model, enabling outlier diagnosis and data cleaning. The proposed conditional diffusion model (CDM) augments the standard diffusion model with a conditional embedding module to incorporate temporal context, quartile normalization to mitigate distribution skew, and a Huber loss to enhance robustness against outliers. Within this univariate implicit autoregressive framework, each data point is assigned an outlier probability, quantifying its degree of "outlier-ness", and a global quality evaluation score is computed to characterize the overall dataset quality. Extensive case studies utilizing operational data from real-world structures demonstrate that the proposed framework significantly improves the accuracy of data quality assessment, outperforming other strong baselines representative of clustering, isolation-based, and deep reconstruction methods. The effectiveness and robustness of the proposed framework are further demonstrated by the findings of ablation experiments and hyperparameter analysis.

Dawei Huang, Hui Li, Haonan Feng et al.

Formal verification provides rigorous guarantees for cryptographic security, yet automating the extraction and formalization of security goals from natural language protocol documents remains a major bottleneck, compounded by the scarcity of expert-annotated resources and integrated frameworks bridging unstructured text and symbolic logic. We introduce SecGoal, the first expert-annotated benchmark covering 15 widely deployed protocol documents, including 5G-AKA and TLS 1.3, and AIFG, an AI-assisted framework that decomposes the task into context-aware goal extraction and retrieval-augmented formalization. We conduct a comprehensive evaluation to assess whether contemporary LLMs are ready to automate this pipeline. Our results reveal a pronounced precision-recall imbalance: frontier models, such as Gemini 2.5-Pro, achieve high recall but precision below 15%, frequently misclassifying operational text as security goals. In contrast, instruction tuning on SecGoal enables compact models with 7B/9B parameters to achieve F1-scores above 80%, substantially outperforming larger general-purpose models. Our work establishes a foundational dataset and reproducible baseline for automated formal protocol analysis.

Xiongtao Sun, Gan Liu, Zhipeng He et al.

Prompt serves as a crucial link in interacting with large language models (LLMs), widely impacting the accuracy and interpretability of model outputs. However, acquiring accurate and high-quality responses necessitates precise prompts, which inevitably pose significant risks of personal identifiable information (PII) leakage. Therefore, this paper proposes DePrompt, a desensitization protection and effectiveness evaluation framework for prompt, enabling users to safely and transparently utilize LLMs. Specifically, by leveraging large model fine-tuning techniques as the underlying privacy protection method, we integrate contextual attributes to define privacy types, achieving high-precision PII entity identification. Additionally, through the analysis of key features in prompt desensitization scenarios, we devise adversarial generative desensitization methods that retain important semantic content while disrupting the link between identifiers and privacy attributes. Furthermore, we present utility evaluation metrics for prompt to better gauge and balance privacy and usability. Our framework is adaptable to prompts and can be extended to text usability-dependent scenarios. Through comparison with benchmarks and other model methods, experimental evaluations demonstrate that our desensitized prompt exhibit superior privacy protection utility and model inference results.

Yunkai Zhang, Shiyin Wei, Yong Huang et al.

Existing computer vision(CV)-based structural damage identification models demonstrate notable accuracy in categorizing and localizing damage. However, these models present several critical limitations that hinder their practical application in civil engineering(CE). Primarily, their ability to recognize damage types remains constrained, preventing comprehensive analysis of the highly varied and complex conditions encountered in real-world CE structures. Second, these models lack linguistic capabilities, rendering them unable to articulate structural damage characteristics through natural language descriptions. With the continuous advancement of artificial intelligence(AI), large multi-modal models(LMMs) have emerged as a transformative solution, enabling the unified encoding and alignment of textual and visual data. These models can autonomously generate detailed descriptive narratives of structural damage while demonstrating robust generalization across diverse scenarios and tasks. This study introduces SDIGLM, an innovative LMM for structural damage identification, developed based on the open-source VisualGLM-6B architecture. To address the challenge of adapting LMMs to the intricate and varied operating conditions in CE, this work integrates a U-Net-based semantic segmentation module to generate defect segmentation maps as visual Chain of Thought(CoT). Additionally, a multi-round dialogue fine-tuning dataset is constructed to enhance logical reasoning, complemented by a language CoT formed through prompt engineering. By leveraging this multi-modal CoT, SDIGLM surpasses general-purpose LMMs in structural damage identification, achieving an accuracy of 95.24% across various infrastructure types. Moreover, the model effectively describes damage characteristics such as hole size, crack direction, and corrosion severity.

Sheng Zhang, Yifan Ding, Shuquan Lian et al.

Repository-level code completion automatically predicts the unfinished code based on the broader information from the repository. Recent strides in Code Large Language Models (code LLMs) have spurred the development of repository-level code completion methods, yielding promising results. Nevertheless, they suffer from issues such as inappropriate query construction, single-path code retrieval, and misalignment between code retriever and code LLM. To address these problems, we introduce CodeRAG, a framework tailored to identify relevant and necessary knowledge for retrieval-augmented repository-level code completion. Its core components include log probability guided query construction, multi-path code retrieval, and preference-aligned BestFit reranking. Extensive experiments on benchmarks ReccEval and CCEval demonstrate that CodeRAG significantly and consistently outperforms state-of-the-art methods. The implementation of CodeRAG is available at https://github.com/KDEGroup/CodeRAG.

Hequn Li, Zhongwei Deng, Chunlin Jiang et al.

Accurate prediction of lithium-ion battery capacity and its associated uncertainty is essential for reliable battery management but remains challenging due to the stochastic nature of aging. This paper presents a novel method, termed the Condition Diffusion U-Net with Attention (CDUA), which integrates feature engineering and deep learning to address this challenge. The proposed approach employs a diffusion-based generative model for time-series forecasting and incorporates attention mechanisms to enhance predictive performance. Battery capacity is first derived from real-world vehicle operation data. The most relevant features are then identified using the Pearson correlation coefficient and the XGBoost algorithm. These features are used to train the CDUA model, which comprises two core components: (1) a contextual U-Net with self-attention to capture complex temporal dependencies, and (2) a denoising network to reconstruct accurate capacity values from noisy observations. Experimental validation on the real-world vehicle data demonstrates that the proposed CDUA model achieves a relative Mean Absolute Error (MAE) of 0.94% and a relative Root Mean Square Error (RMSE) of 1.14%, with a narrow 95% confidence interval of 3.74% in relative width. These results confirm that CDUA provides both accurate capacity estimation and reliable uncertainty quantification. Comparative experiments further verify its robustness and superior performance over existing mainstream approaches.

Yongbiao Xiao, Hui Li, Chunyang Cheng et al.

Infrared and visible image fusion task aims to generate a fused image which contains salient features and rich texture details from multi-source images. However, under complex illumination conditions, few algorithms pay attention to the edge information of local regions which is crucial for downstream tasks. To this end, we propose a fusion network based on the local edge enhancement, named LE2Fusion. Specifically, a local edge enhancement (LE2) module is proposed to improve the edge information under complex illumination conditions and preserve the essential features of image. For feature extraction, a multi-scale residual attention (MRA) module is applied to extract rich features. Then, with LE2, a set of enhancement weights are generated which are utilized in feature fusion strategy and used to guide the image reconstruction. To better preserve the local detail information and structure information, the pixel intensity loss function based on the local region is also presented. The experiments demonstrate that the proposed method exhibits better fusion performance than the state-of-the-art fusion methods on public datasets.

Bharath Rao, Hui Li, Krishna Krishnan et al.

Humans can determine a proper strategy to grasp an object according to the measured physical attributes or the prior knowledge of the object. This paper proposes an approach to determining the strategy of dexterous grasping by using an anthropomorphic robotic hand simply based on a label or a description of an object. Object attributes are parsed from natural-language descriptions and augmented with an object knowledge base that is scraped from retailer websites. A novel metric named joint probability distance is defined to measure distance between object attributes. The probability distribution of grasp types for the given object is learned using a deep neural network which takes in object features as input. The action of the multi-fingered hand with redundant degrees of freedom (DoF) is controlled by a linear inverse-kinematics model of grasp topology and scales. The grasping strategy generated by the proposed approach is evaluated both by simulation and execution on a Sawyer robot with an AR10 robotic hand.

Yong Huang, Haoyu Zhang, Hui Li et al.

In a structural health monitoring (SHM) system that uses digital cameras to monitor cracks of structural surfaces, techniques for reliable and effective data compression are essential to ensure a stable and energy efficient crack images transmission in wireless devices, e.g., drones and robots with high definition cameras installed. Compressive sensing (CS) is a signal processing technique that allows accurate recovery of a signal from a sampling rate much smaller than the limitation of the Nyquist sampling theorem. The conventional CS method is based on the principle that, through a regularized optimization, the sparsity property of the original signals in some domain can be exploited to get the exact reconstruction with a high probability. However, the strong assumption of the signals being highly sparse in an invertible space is relatively hard for real crack images. In this paper, we present a new approach of CS that replaces the sparsity regularization with a generative model that is able to effectively capture a low dimension representation of targeted images. We develop a recovery framework for automatic crack segmentation of compressed crack images based on this new CS method and demonstrate the remarkable performance of the method taking advantage of the strong capability of generative models to capture the necessary features required in the crack segmentation task even the backgrounds of the generated images are not well reconstructed. The superior performance of our recovery framework is illustrated by comparing with three existing CS algorithms. Furthermore, we show that our framework is extensible to other common problems in automatic crack segmentation, such as defect recovery from motion blurring and occlusion.

Yuequan Bao, Zhiyi Tang, Hui Li

Compressive sensing (CS) has been studied and applied in structural health monitoring for wireless data acquisition and transmission, structural modal identification, and spare damage identification. The key issue in CS is finding the optimal solution for sparse optimization. In the past years, many algorithms have been proposed in the field of applied mathematics. In this paper, we propose a machine-learning-based approach to solve the CS data-reconstruction problem. By treating a computation process as a data flow, the process of CS-based data reconstruction is formalized into a standard supervised-learning task. The prior knowledge, i.e., the basis matrix and the CS-sampled signals, are used as the input and the target of the network; the basis coefficient matrix is embedded as the parameters of a certain layer; the objective function of conventional compressive sensing is set as the loss function of the network. Regularized by l1-norm, these basis coefficients are optimized to reduce the error between the original CS-sampled signals and the masked reconstructed signals with a common optimization algorithm. Also, the proposed network can handle complex bases, such as a Fourier basis. Benefiting from the nature of a multi-neuron layer, multiple signal channels can be reconstructed simultaneously. Meanwhile, the disassembled use of a large-scale basis makes the method memory-efficient. A numerical example of multiple sinusoidal waves and an example of field-test wireless data from a suspension bridge are carried out to illustrate the data-reconstruction ability of the proposed approach. The results show that high reconstruction accuracy can be obtained by the machine learning-based approach. Also, the parameters of the network have clear meanings; the inference of the mapping between input and output is fully transparent, making the CS data reconstruction neural network interpretable.