Quality control is a critical aspect of manufacturing, particularly in ensuring the proper assembly of small components in production lines. Existing solutions often rely on single-view imaging or manual inspection, which are prone to errors due to occlusions, restricted perspectives, or lighting inconsistencies. These limitations require the installation of additional inspection stations, which could disrupt the assembly line and lead to increased downtime and costs. This paper introduces a novel multi-view quality control module designed to address these challenges, integrating a multi-camera imaging system with advanced object detection algorithms. By capturing images from three camera views, the system provides comprehensive visual coverage of components of an assembly process. A tailored image fusion methodology combines results from multiple views, effectively resolving ambiguities and enhancing detection reliability. To support this system, we developed a unique dataset comprising annotated images across diverse scenarios, including varied lighting conditions, occlusions, and angles, to enhance applicability in real-world manufacturing environments. Experimental results show that our approach significantly outperforms single-view methods, achieving high precision and recall rates in the identification of improperly fastened small assembly parts such as screws. This work contributes to industrial automation by overcoming single-view limitations, and providing a scalable, cost-effective, and accurate quality control mechanism that ensures the reliability and safety of the assembly line. The dataset used in this study is publicly available to facilitate further research in this domain.

Large Language Models (LLMs) have achieved remarkable success on question answering (QA) tasks, yet they often encode harmful biases that compromise fairness and trustworthiness. Most existing bias mitigation approaches are restricted to predefined categories, limiting their ability to address novel or context-specific emergent biases. To bridge this gap, we tackle the novel problem of open-set bias detection and mitigation in text-based QA. We introduce OpenBiasBench, a comprehensive benchmark designed to evaluate biases across a wide range of categories and subgroups, encompassing both known and previously unseen biases. Additionally, we propose Open-DeBias, a novel, data-efficient, and parameter-efficient debiasing method that leverages adapter modules to mitigate existing social and stereotypical biases while generalizing to unseen ones. Compared to the state-of-the-art BMBI method, Open-DeBias improves QA accuracy on BBQ dataset by nearly $48\%$ on ambiguous subsets and $6\%$ on disambiguated ones, using adapters fine-tuned on just a small fraction of the training data. Remarkably, the same adapters, in a zero-shot transfer to Korean BBQ, achieve $84\%$ accuracy, demonstrating robust language-agnostic generalization. Through extensive evaluation, we also validate the effectiveness of Open-DeBias across a broad range of NLP tasks, including StereoSet and CrowS-Pairs, highlighting its robustness, multilingual strength, and suitability for general-purpose, open-domain bias mitigation. The project page is available at: https://sites.google.com/view/open-debias25

State-space models (SSMs), particularly Mamba, emerge as an efficient Transformer alternative with linear complexity for long-sequence modeling. Recent empirical works demonstrate Mamba's in-context learning (ICL) capabilities competitive with Transformers, a critical capacity for large foundation models. However, theoretical understanding of Mamba's ICL remains limited, restricting deeper insights into its underlying mechanisms. Even fundamental tasks such as linear regression ICL, widely studied as a standard theoretical benchmark for Transformers, have not been thoroughly analyzed in the context of Mamba. To address this gap, we study the training dynamics of Mamba on the linear regression ICL task. By developing novel techniques tackling non-convex optimization with gradient descent related to Mamba's structure, we establish an exponential convergence rate to ICL solution, and derive a loss bound that is comparable to Transformer's. Importantly, our results reveal that Mamba can perform a variant of \textit{online gradient descent} to learn the latent function in context. This mechanism is different from that of Transformer, which is typically understood to achieve ICL through gradient descent emulation. The theoretical results are verified by experimental simulation.

Large language model (LLM) personalization aims to tailor model behavior to individual users based on their historical interactions. However, its effectiveness is often hindered by two key challenges: the \textit{cold-start problem}, where users with limited history provide insufficient context for accurate personalization, and the \textit{biasing problem}, where users with abundant but skewed history cause the model to overfit to narrow preferences. We identify both issues as symptoms of a common underlying limitation, i.e., the inability to model collective knowledge across users. To address this, we propose a local-global memory framework (LoGo) that combines the personalized local memory with a collective global memory that captures shared interests across the population. To reconcile discrepancies between these two memory sources, we introduce a mediator module designed to resolve conflicts between local and global signals. Extensive experiments on multiple benchmarks demonstrate that LoGo consistently improves personalization quality by both warming up cold-start users and mitigating biased predictions. These results highlight the importance of incorporating collective knowledge to enhance LLM personalization.

Interpretable risk scores play a vital role in clinical decision support, yet traditional methods for deriving such scores often rely on manual preprocessing, task-specific modeling, and simplified assumptions that limit their flexibility and predictive power. We present SHAPoint, a novel, task-agnostic framework that integrates the predictive accuracy of gradient boosted trees with the interpretability of point-based risk scores. SHAPoint supports classification, regression, and survival tasks, while also inheriting valuable properties from tree-based models, such as native handling of missing data and support for monotonic constraints. Compared to existing frameworks, SHAPoint offers superior flexibility, reduced reliance on manual preprocessing, and faster runtime performance. Empirical results show that SHAPoint produces compact and interpretable scores with predictive performance comparable to state-of-the-art methods, but at a fraction of the runtime, making it a powerful tool for transparent and scalable risk stratification.

Multimodal large language models (MLLMs) promise enhanced reasoning by integrating diverse inputs such as text, vision, and audio. Yet cross-modal reasoning remains underexplored, with conflicting reports on whether added modalities help or harm performance. These inconsistencies stem from a lack of controlled evaluation frameworks and analysis of models' internals to isolate when and why modality interactions support or undermine reasoning. We address this gap through a logic-grounded evaluation framework that categorizes multimodal reasoning into six interaction patterns, varying how facts are distributed across modalities and logically combined. Empirically, additional modalities enhance reasoning only when they provide independent and sufficient reasoning paths, while redundant or chained entailment support often hurts performance. Moreover, reasoning degrades in three systematic ways: weaker modalities drag down overall performance, conflicts bias preference toward certain modalities, and joint signals from different modalities fail to be integrated effectively. Therefore, we identify two core failures: task-composition bottleneck, where recognition and reasoning cannot be jointly executed in one pass, and fusion bottleneck, where early integration introduces bias. For further investigation, we find that attention patterns fail to encode fact usefulness, but a simple two-step prompting (recognize then reason) restores performance, confirming the task-composition bottleneck. Moreover, modality identity remains recoverable in early layers, and softening attention in early fusion improves reasoning, highlighting biased fusion as another failure mode. Overall, our findings show that integration, not perception, is the main barrier to multimodal reasoning, suggesting composition-aware training and early fusion control as promising directions.

To effectively handle clustering task for large-scale datasets, we propose a novel scalable skeleton clustering algorithm, namely GBSK, which leverages the granular-ball technique to capture the underlying structure of data. By multi-sampling the dataset and constructing multi-grained granular-balls, GBSK progressively uncovers a statistical "skeleton" -- a spatial abstraction that approximates the essential structure and distribution of the original data. This strategy enables GBSK to dramatically reduce computational overhead while maintaining high clustering accuracy. In addition, we introduce an adaptive version, AGBSK, with simplified parameter settings to enhance usability and facilitate deployment in real-world scenarios. Extensive experiments conducted on standard computing hardware demonstrate that GBSK achieves high efficiency and strong clustering performance on large-scale datasets, including one with up to 100 million instances across 256 dimensions. Our implementation and experimental results are available at: https://github.com/XFastDataLab/GBSK/.

Multi-robot Coverage Path Planning (MCPP) addresses the problem of computing paths for multiple robots to effectively cover a large area of interest. Conventional approaches to MCPP typically assume that robots move at fixed velocities, which is often unrealistic in real-world applications where robots must adapt their speeds based on the specific coverage tasks assigned to them.Consequently, conventional approaches often lead to imbalanced workload distribution among robots and increased completion time for coverage tasks. To address this, we introduce a novel Multi-robot Dynamic Coverage Path Planning (MDCPP) algorithm for complete coverage in two-dimensional environments. MDCPP dynamically estimates each robot's remaining workload by approximating the target distribution with Gaussian mixture models, and assigns coverage regions using a capacity-constrained Voronoi diagram. We further develop a distributed implementation of MDCPP for range-constrained robotic networks. Simulation results validate the efficacy of MDCPP, showing qualitative improvements and superior performance compared to an existing sweeping algorithm, and a quantifiable impact of communication range on coverage efficiency.

The safety and security of public spaces is of vital importance, driving the need for sophisticated surveillance systems capable of accurately detecting weapons, which are often hampered by issues like partial occlusion, varying lighting, and cluttered backgrounds. While single-model detectors are advanced, they often lack robustness in these challenging conditions. This paper presents the hypothesis that ensemble of Single Shot Multibox Detector (SSD) models with diverse feature extraction backbones can significantly enhance detection robustness. To leverage diverse feature representations, individual SSD models were trained using a selection of backbone networks: VGG16, ResNet50, EfficientNet, and MobileNetV3. The study is conducted on a dataset consisting of images of three distinct weapon classes: guns, heavy weapons and knives. The predictions from these models are combined using the Weighted Boxes Fusion (WBF) method, an ensemble technique designed to optimize bounding box accuracy. Our key finding is that the fusion strategy is as critical as the ensemble's diversity, a WBF approach using a 'max' confidence scoring strategy achieved a mean Average Precision (mAP) of 0.838. This represents a 2.948% relative improvement over the best-performing single model and consistently outperforms other fusion heuristics. This research offers a robust approach to enhancing real-time weapon detection capabilities in surveillance applications by demonstrating that confidence-aware fusion is a key mechanism for improving accuracy metrics of ensembles.

Time series foundation models (TSFMs) offer strong zero-shot forecasting via large-scale pre-training, yet fine-tuning remains critical for boosting performance in domains with limited public data. With the growing number of TSFMs, efficiently identifying the best model for downstream fine-tuning becomes increasingly challenging. Leveraging the natural tabular structure formed by dataset meta-features, model characteristics, and fine-tuned performance, we employ tabular foundation models to serve as in-context learners. We establish a comprehensive benchmark for transferability estimation including 10 datasets, 10 foundation models, and 3 forecasting tasks. 's estimation demonstrates strong alignment with actual fine-tuned performance for previously unseen datasets, achieving a mean rank correlation of approximately 0.6 and a 30% improvement compared to using zero-shot performance as the transferability score. The emergence of time series foundation models (TSFMs) is reshaping the paradigm of time series forecasting (Liang et al., 2025) through their strong zero-shot capabilities. Although efficient and cost-effective, zero-shot inference often underperforms in out-of-distribution scenarios, particularly in domains with limited public data, such as healthcare (Gupta et al., 2024) and finance (Fu et al., 2024). Fine-tuning helps bridge the gap by transferring generalized knowledge from large-scale pre-training to specific, resource-limited downstream tasks (Li & Zhu, 2025). However, due to the inherent diversity of time series data, no single model consistently outperforms others in all scenarios (Brigato et al., 2025).