Vision-Language Model (VLM) have gained widespread adoption in Open-Vocabulary (OV) object detection and segmentation tasks. Despite they have shown promise on OV-related tasks, their effectiveness in conventional vision tasks has thus far been unevaluated. In this work, we present the systematic review of VLM-based detection and segmentation, view VLM as the foundational model and conduct comprehensive evaluations across multiple downstream tasks for the first time: 1) The evaluation spans eight detection scenarios (closed-set detection, domain adaptation, crowded objects, etc.) and eight segmentation scenarios (few-shot, open-world, small object, etc.), revealing distinct performance advantages and limitations of various VLM architectures across tasks. 2) As for detection tasks, we evaluate VLMs under three finetuning granularities: \textit{zero prediction}, \textit{visual fine-tuning}, and \textit{text prompt}, and further analyze how different finetuning strategies impact performance under varied task. 3) Based on empirical findings, we provide in-depth analysis of the correlations between task characteristics, model architectures, and training methodologies, offering insights for future VLM design. 4) We believe that this work shall be valuable to the pattern recognition experts working in the fields of computer vision, multimodal learning, and vision foundation models by introducing them to the problem, and familiarizing them with the current status of the progress while providing promising directions for future research. A project associated with this review and evaluation has been created at https://github.com/better-chao/perceptual_abilities_evaluation.

Recent advancements in machine learning (ML) have enabled its deployment on resource-constrained edge devices, fostering innovative applications such as intelligent environmental sensing. However, these devices, particularly microcontrollers (MCUs), face substantial challenges due to limited memory, computing capabilities, and the absence of dedicated floating-point units (FPUs). These constraints hinder the deployment of complex ML models, especially those requiring lifelong learning capabilities. To address these challenges, we propose Tin-Tin, an integer-based on-device training framework designed specifically for low-power MCUs. Tin-Tin introduces novel integer rescaling techniques to efficiently manage dynamic ranges and facilitate efficient weight updates using integer data types. Unlike existing methods optimized for devices with FPUs, GPUs, or FPGAs, Tin-Tin addresses the unique demands of tiny MCUs, prioritizing energy efficiency and optimized memory utilization. We validate the effectiveness of Tin-Tin through end-to-end application examples on real-world tiny devices, demonstrating its potential to support energy-efficient and sustainable ML applications on edge platforms.

-- This paper introduces the Compliant Explicit Reference Governor (C-ERG), an extension of the Explicit Reference Governor that allows the robot to operate safely while in contact with the environment. The C-ERG is an intermediate layer that can be placed between a high-level planner and a low-level controller: its role is to enforce operational constraints and to enable the smooth transition between free-motion and contact operations. The C-ERG ensures safety by limiting the total energy available to the robotic arm at the time of contact. In the absence of contact, however, the C-ERG does not penalize the system performance. The emerging trend in the modern industry is to prioritize flexibility [1]. Until recently, robotics has been dominated by sampling-based motion planning which places an emphasis on "collision-free" paths to avoid harming itself or anything in its path [2].

Wenjie LI 1, 2, Sibo Zhu 1, 2, Zhijian Li 1, 2, and Hanlin Wang 1, 2 Abstract -- This paper introduces a novel oversampling technique designed to improve classification performance on imbalanced datasets. The proposed method enhances the traditional SMOTE algorithm by incorporating convex combination and kernel-based weighting to generate synthetic samples that better represent the minority class. Through experiments on multiple real-world datasets, we demonstrate that the new technique outperforms existing methods in terms of F1-score, G-mean, and AUC, providing a robust solution for handling imbalanced datasets in classification tasks. I NTRODUCTION Imbalanced datasets are a pervasive issue in the domain of classification, where the distribution of classes is skewed, with one class (often referred to as the minority class) being significantly underrepresented compared to the other (the majority class). The imbalance issue is especially problematic in classification tasks, as traditional machine learning algorithms are generally designed to maximize overall accuracy, leading them to favor the majority class. Consequently, it results in a bias where the model performs well on the majority class but poorly on the minority class, which is often the class of greater interest [1].

Recently, the reasoning capabilities of large reasoning models (LRMs), such as DeepSeek-R1, have seen significant advancements through the slow thinking process. Despite these achievements, the substantial computational demands of LRMs present considerable challenges. In contrast, small reasoning models (SRMs), often distilled from larger ones, offer greater efficiency and can exhibit distinct capabilities and cognitive trajectories compared to LRMs. This work surveys around 170 recently published papers on SRMs for tackling various complex reasoning tasks. We review the current landscape of SRMs and analyze diverse training and inference techniques related to SRMs. Furthermore, we provide a comprehensive review of SRMs for domain-specific applications and discuss possible future research directions. This survey serves as an essential reference for researchers to leverage or develop SRMs for advanced reasoning functionalities with high efficiency.

The increasing size and complexity of machine learning (ML) models have driven the growing need for custom hardware accelerators capable of efficiently supporting ML workloads. However, the design of such accelerators remains a time-consuming process, heavily relying on engineers to manually ensure design interpretability through clear documentation and effective communication. Recent advances in large language models (LLMs) offer a promising opportunity to automate these design interpretability tasks, particularly the generation of natural language descriptions for register-transfer level (RTL) code, what we refer to as "RTL-to-NL tasks." In this paper, we examine how design interpretability, particularly in RTL-to-NL tasks, influences the efficiency of the hardware design process. We review existing work adapting LLMs for these tasks, highlight key challenges that remain unaddressed, including those related to data, computation, and model development, and identify opportunities to address them. By doing so, we aim to guide future research in leveraging ML to automate RTL-to-NL tasks and improve hardware design interpretability, thereby accelerating the hardware design process and meeting the increasing demand for custom hardware accelerators in machine learning and beyond.

The scientific discourse surrounding Chronic Lyme Disease (CLD) and Post-Treatment Lyme Disease Syndrome (PTLDS) has evolved over the past twenty-five years into a complex and polarised debate, shaped by shifting research priorities, institutional influences, and competing explanatory models. This study presents the first large-scale, systematic examination of this discourse using an innovative hybrid AI-driven methodology, combining large language models with structured human validation to analyse thousands of scholarly abstracts spanning 25 years. By integrating Large Language Models (LLMs) with expert oversight, we developed a quantitative framework for tracking epistemic shifts in contested medical fields, with applications to other content analysis domains. Our analysis revealed a progressive transition from infection-based models of Lyme disease to immune-mediated explanations for persistent symptoms. This study offers new empirical insights into the structural and epistemic forces shaping Lyme disease research, providing a scalable and replicable methodology for analysing discourse, while underscoring the value of AI-assisted methodologies in social science and medical research.

The exponential growth of academic literature creates urgent demands for comprehensive survey papers, yet manual writing remains time-consuming and labor-intensive. Recent advances in large language models (LLMs) and retrieval-augmented generation (RAG) facilitate studies in synthesizing survey papers from multiple references, but most existing works restrict users to title-only inputs and fixed outputs, neglecting the personalized process of survey paper writing. In this paper, we introduce InteractiveSurvey - an LLM-based personalized and interactive survey paper generation system. InteractiveSurvey can generate structured, multi-modal survey papers with reference categorizations from multiple reference papers through both online retrieval and user uploads. More importantly, users can customize and refine intermediate components continuously during generation, including reference categorization, outline, and survey content through an intuitive interface. Evaluations of content quality, time efficiency, and user studies show that InteractiveSurvey is an easy-to-use survey generation system that outperforms most LLMs and existing methods in output content quality while remaining highly time-efficient.

The rise of deep learning in natural language processing has fostered the creation of text to structured query language models composed of an encoder and a decoder. Researchers have experimented with various intermediate processing like schema linking, table type aware, value extract. To generate accurate SQL results for the user question. However error analysis performed on the failed cases on these systems shows, 29 percentage of the errors would be because the system was unable to understand the values expressed by the user in their question. This challenge affects the generation of accurate SQL queries, especially when dealing with domain-specific terms and specific value conditions, where traditional methods struggle to maintain consistency and precision. To overcome these obstacles, proposed two intermediations like implementing data balancing technique and over sampling domain-specific queries which would refine the model architecture to enhance value recognition and fine tuning the model for domain-specific questions. This proposed solution achieved 10.98 percentage improvement in accuracy of the model performance compared to the state of the art model tested on WikiSQL dataset. to convert the user question accurately to SQL queries. Applying oversampling technique on the domain-specific questions shown a significant improvement as compared with traditional approaches.

Multimodal Retrieval-Augmented Generation (MRAG) enhances large language models (LLMs) by integrating multimodal data (text, images, videos) into retrieval and generation processes, overcoming the limitations of text-only Retrieval-Augmented Generation (RAG). While RAG improves response accuracy by incorporating external textual knowledge, MRAG extends this framework to include multimodal retrieval and generation, leveraging contextual information from diverse data types. This approach reduces hallucinations and enhances question-answering systems by grounding responses in factual, multimodal knowledge. Recent studies show MRAG outperforms traditional RAG, especially in scenarios requiring both visual and textual understanding. This survey reviews MRAG's essential components, datasets, evaluation methods, and limitations, providing insights into its construction and improvement. It also identifies challenges and future research directions, highlighting MRAG's potential to revolutionize multimodal information retrieval and generation. By offering a comprehensive perspective, this work encourages further exploration into this promising paradigm.