Knowledge catalogue
arXiv:2512.13876v2 Announce Type: replace Abstract: Detection Transformers (DETR) formulate object detection as a set prediction problem and enable end-to-end training without post-processing. However
arXiv:2604.11514v1 Announce Type: cross Abstract: This work addresses test output prediction, a key challenge in test case generation. To improve the reliability of predicted outputs by LLMs, prior ap
arXiv:2604.09599v1 Announce Type: cross Abstract: High-performance computing systems are complex machines whose behaviour is governed by the correct functioning of its many subsystems. Among these, th
arXiv:2604.11423v1 Announce Type: new Abstract: For the past decades medical robotic solutions were mostly based on the concept of tele-manipulation. While their design was extremely intelligent, allo
arXiv:2604.10459v1 Announce Type: new Abstract: The exponential growth of user-generated movie reviews on digital platforms has made accurate text sentiment classification a cornerstone task in natura
arXiv:2604.09650v1 Announce Type: cross Abstract: Accurately predicting stock repurchases is crucial for quantitative investment and risk management, yet traditional static models fail to capture the
arXiv:2604.11334v1 Announce Type: new Abstract: Depression remains widely underdiagnosed and undertreated because stigma and subjective symptom ratings hinder reliable screening. To address this chall
arXiv:2604.10661v1 Announce Type: cross Abstract: Mobile apps have become essential of our daily lives, making code quality a critical concern for developers. Behavioural code smells are characteristi
arXiv:2604.11094v1 Announce Type: cross Abstract: Contemporary microservice systems continue to grow in scale and complexity, leading to increasingly frequent and costly failures. While recent LLM-bas
arXiv:2604.11400v1 Announce Type: cross Abstract: High-speed autonomous racing presents extreme perception challenges, including large relative velocities and substantial domain shifts from convention
arXiv:2604.10567v1 Announce Type: cross Abstract: Diffusion-based language models (dLLMs) have emerged as a promising alternative to autoregressive language models, offering the potential for parallel
arXiv:2604.09603v1 Announce Type: cross Abstract: Speculative Decoding promises to accelerate the inference of Large Language Models, yet its efficacy often degrades in production-grade serving. Exist
arXiv:2508.06445v3 Announce Type: replace-cross Abstract: The rapid rise of Generative AI (GenAI), particularly LLMs, poses concerns for journalistic integrity and authorship. This study examines AI-g
arXiv:2604.09694v1 Announce Type: new Abstract: Autonomous Unmanned Aerial Vehicles (UAVs) must reliably detect thin obstacles such as wires, poles, and branches to navigate safely in real-world envir
arXiv:2604.11512v1 Announce Type: cross Abstract: The growing demand for deploying Small Language Models (SLMs) on edge devices, including laptops, smartphones, and embedded platforms, has exposed fun
arXiv:2603.05463v2 Announce Type: replace Abstract: Single-object tracking (SOT) on edge devices is a critical computer vision task, requiring accurate and continuous target localization across video
arXiv:2604.10268v1 Announce Type: new Abstract: We propose EditCrafter, a high-resolution image editing method that operates without tuning, leveraging pretrained text-to-image (T2I) diffusion models
arXiv:2604.10200v1 Announce Type: new Abstract: As Vision-Language Models (VLMs) become integral to educational decision-making, ensuring their fairness is paramount. However, current text-centric eva
arXiv:2604.05005v2 Announce Type: replace-cross Abstract: Large language models are increasingly used as educational assistants, yet evaluation of their educational capabilities remains concentrated o
arXiv:2510.06965v2 Announce Type: replace-cross Abstract: Math word problems (MWPs) are critical K-12 educational tools, and customizing them to students' interests and ability levels can enhance lear
arXiv:2604.09815v1 Announce Type: new Abstract: Computer-use agents that combine GUI interaction with structured API calls via the Model Context Protocol (MCP) show promise for automating software tas
arXiv:2509.25667v2 Announce Type: replace-cross Abstract: This paper presents an Artificial Intelligence (AI) integrated approach to Brain-Computer Interface (BCI)-based wheelchair development, utiliz
arXiv:2510.05837v2 Announce Type: replace Abstract: Balancing exploration and exploitation remains a central challenge in reinforcement learning with verifiable rewards (RLVR) for large language model
arXiv:2511.18957v2 Announce Type: replace Abstract: Video virtual try-on technology provides a cost-effective solution for creating marketing videos in fashion e-commerce. However, its practical adopt
arXiv:2604.09647v1 Announce Type: cross Abstract: Criminal networks, such as the Sicilian Mafia, pose substantial threats to public safety, national security, and economic stability. Outdated disrupti
arXiv:2604.11417v1 Announce Type: cross Abstract: Co-speech gestures increase engagement and improve speech understanding. Most data-driven robot systems generate rhythmic beat-like motion, yet few in
arXiv:2604.11775v1 Announce Type: cross Abstract: Perturbation-based explainability methods such as KernelSHAP provide model-agnostic attributions but are typically impractical for patch-based 3D medi
arXiv:2604.09742v1 Announce Type: cross Abstract: Rotary Position Embedding (RoPE) has become a core component of modern Transformer architectures across language, vision, and 3D domains. However, exi
arXiv:2604.09876v1 Announce Type: cross Abstract: Generative user interfaces (UIs) create new opportunities to adapt interfaces to individual users on demand, but personalization remains difficult bec
arXiv:2604.10660v1 Announce Type: cross Abstract: Recent research has devoted considerable effort to verifying the intermediate reasoning steps of chain-of-thought (CoT) trajectories using process rew
arXiv:2604.11096v1 Announce Type: cross Abstract: Currently, large language models (LLMs) predominantly focus on the text modality. To enable more natural human-AI interaction, speech LLMs are emergin
arXiv:2604.11098v1 Announce Type: new Abstract: Large-scale three-dimensional (3D) scene reconstruction in low-altitude intelligent networks (LAIN) demands highly efficient wireless image transmission
arXiv:2604.11038v1 Announce Type: new Abstract: We present EgoFun3D, a coordinated task formulation, dataset, and benchmark for modeling interactive 3D objects from egocentric videos. Interactive obje
arXiv:2602.04849v2 Announce Type: replace-cross Abstract: We present El Agente Estructural, a multimodal, natural-language-driven geometry-generation and manipulation agent for autonomous chemistry an
arXiv:2604.11547v1 Announce Type: cross Abstract: While large language models hold promise for complex medical applications, their development is hindered by the scarcity of high-quality reasoning dat
arXiv:2601.19019v2 Announce Type: replace-cross Abstract: Neural population activity in sensory cortex is organized on low-dimensional manifolds, but why such manifolds arise and what determines their
arXiv:2604.11174v1 Announce Type: cross Abstract: Recent progress in embodied AI has produced a growing ecosystem of robot policies, foundation models, and modular runtimes. However, current evaluatio
arXiv:2604.09561v1 Announce Type: cross Abstract: We present the first empirical analysis of social structure formation among autonomous AI agents on a live network. Our study examines 626 agents -- p
arXiv:2604.11043v1 Announce Type: new Abstract: Unified multimodal embedding spaces underpin practical applications such as cross-modal retrieval and zero-shot recognition. In many real deployments, h
arXiv:2604.11283v1 Announce Type: new Abstract: Recent developments in video translation have further enhanced cross-lingual access to video content, with multimodal large language models (MLLMs) play
arXiv:2604.11422v1 Announce Type: cross Abstract: The ``differentiability gap'' presents a primary bottleneck in Earth system deep learning: since models cannot be trained directly on non-differentiab
arXiv:2604.10978v1 Announce Type: cross Abstract: This study investigates students' AI use concealment intention in higher education by integrating the cognition-affect-conation (CAC) framework with a
arXiv:2503.16771v3 Announce Type: replace-cross Abstract: As Large Language Models for Code (LM4Code) become integral to software engineering, establishing trust in their output becomes critical. Howe
arXiv:2604.10117v1 Announce Type: new Abstract: Photoplethysmography (PPG)-based blood pressure (BP) estimation is a challenging task, particularly on resource-constrained wearable devices. However, f
arXiv:2511.09282v3 Announce Type: replace-cross Abstract: Significant progress has been made in spoken question answering (SQA) in recent years. However, many existing methods, including large audio l
arXiv:2604.11733v1 Announce Type: cross Abstract: We study routing games in which travelers optimize over routes that are remembered or surfaced, rather than over a fixed exogenous action set. The pap
arXiv:2604.10662v1 Announce Type: new Abstract: Large-scale Internet of Things (IoT) networks enable intelligent services such as smart cities and autonomous driving, but often face resource constrain
arXiv:2604.10983v1 Announce Type: new Abstract: Diffusion bridge models have shown great promise in image restoration by explicitly connecting clean and degraded image distributions. However, they oft
arXiv:2604.09988v1 Announce Type: cross Abstract: Deep Neural Networks (DNNs) are widely used by engineers to solve difficult problems that require predictive modeling from data. However, these models
arXiv:2601.04392v2 Announce Type: replace-cross Abstract: This paper introduces a fuzzy reinforcement learning framework, Enhanced-FQL(lambda), that integrates novel Fuzzified Eligibility Traces (FET)
arXiv:2604.10652v1 Announce Type: new Abstract: Vehicle routing problems (VRPs) constitute a core optimization challenge in modern logistics and supply chain management. The recent neural combinatoria
arXiv:2604.10437v1 Announce Type: new Abstract: Vision--language models (VLMs) for radiology report generation (RRG) can produce long-form chest CT reports from volumetric scans and show strong potent
arXiv:2512.11811v3 Announce Type: replace-cross Abstract: Crowdsourced social media imagery provides real-time visual evidence of urban flooding but often lacks reliable geographic metadata for emerge
arXiv:2601.02956v3 Announce Type: replace Abstract: Multilingual Retrieval-Augmented Generation (mRAG) systems often exhibit a perceived preference for high-resource languages, particularly English, r
arXiv:2604.11299v1 Announce Type: cross Abstract: In recent years, rapid advances in Multimodal Large Language Models (MLLMs) have increasingly stimulated research on ancient Chinese scripts. As the e
arXiv:2604.11154v1 Announce Type: new Abstract: New multi-modal large language models (MLLMs) are continuously being trained and deployed, following rapid development cycles. This generative AI frenzy
arXiv:2604.11462v1 Announce Type: new Abstract: Large Language Models (LLMs) struggle with long-horizon tasks due to the 'context bottleneck' and the 'lost-in-the-middle' phenomenon, where accumulated
arXiv:2604.11721v1 Announce Type: cross Abstract: Governing common-pool resources requires agents to develop enduring strategies through cooperation and self-governance to avoid collective failure. Wh
arXiv:2604.11182v1 Announce Type: new Abstract: Nowadays, wearable devices can continuously lifelog ambient conversations, creating substantial opportunities for memory systems. However, existing benc
arXiv:2604.09606v1 Announce Type: new Abstract: Traditional benchmarks for large language models (LLMs), such as HELM and AIR-BENCH, primarily assess safety risk through breadth-oriented evaluation ac