Knowledge catalogue
arXiv:2604.24447v1 Announce Type: cross Abstract: Vision-Language-Action (VLA) models are promising for generalist robot control, but on-robot deployment is bottlenecked by real-time inference under t
arXiv:2603.27507v2 Announce Type: replace Abstract: Recent advancements in multi-modal large language models (MLLMs) have shown strong potential for 3D scene understanding. However, existing methods s
arXiv:2510.13312v2 Announce Type: replace Abstract: We present ChatR1, a reasoning framework based on reinforcement learning (RL) for conversational question answering (CQA). Reasoning plays an import
arXiv:2604.22989v1 Announce Type: cross Abstract: Recent medical multimodal foundation models are built as multimodal LLMs (MLLMs) by connecting a CLIP-pretrained vision encoder to an LLM using LLaVA-
arXiv:2604.23009v1 Announce Type: new Abstract: Job Skill Named Entity Recognition (JobSkillNER) aims to automatically extract key skill information from large-scale job posting data, which is importa
arXiv:2604.22897v1 Announce Type: cross Abstract: Patent retrieval underpins critical decisions in innovation, examination, and IP strategy, yet progress has been hampered by the absence of benchmarks
arXiv:2602.23452v2 Announce Type: replace Abstract: Scientific research relies on accurate citation for attribution and integrity, yet large language models (LLMs) introduce a new risk: fabricated ref
arXiv:2604.23781v1 Announce Type: new Abstract: Language-model agents are increasingly used as persistent coworkers that assist users across multiple working days. During such workflows, the surroundi
arXiv:2604.23853v1 Announce Type: new Abstract: Skill-distillation pipelines learn reusable rules from LLM agent trajectories, but they lack a key signal: how much each step costs. Without per-step co
arXiv:2604.16922v2 Announce Type: replace Abstract: Climate research is pivotal for mitigating global environmental crises, yet the accelerating volume of multi-scale datasets and the complexity of an
arXiv:2510.22609v2 Announce Type: replace Abstract: Accurate symptom-to-disease classification and clinically grounded treatment recommendations remain challenging, particularly in heterogeneous patie
arXiv:2604.05500v2 Announce Type: replace Abstract: Nighttime image dehazing faces a more complex degradation pattern than its daytime counterpart, as haze scattering couples with low illumination, no
arXiv:2604.24044v1 Announce Type: new Abstract: Accurate 3D object detection is critical for autonomous driving, necessitating reliable, cost-effective sensors capable of operating in adverse weather
arXiv:2509.17314v3 Announce Type: replace-cross Abstract: Software increasingly relies on the emergent capabilities of Large Language Models (LLMs), from natural language understanding to program anal
arXiv:2303.05330v1 Announce Type: cross Abstract: Geo-distributed ML training can benefit many emerging ML scenarios (e.g., large model training, federated learning) with multi-regional cloud resource
arXiv:2604.24201v1 Announce Type: new Abstract: Motivation: Multi-omics integration can improve cancer subtyping, but modality informativeness and noise vary across cancer types and patients. Existing
arXiv:2604.23137v1 Announce Type: cross Abstract: Early detection and classifying brain tumors using Magnetic Resonance Imaging (MRI) images is highly important but difficult to extract in medical ima
arXiv:2601.22301v2 Announce Type: replace Abstract: Traditional rendering pipelines rely on complex assets, accurate materials and lighting, and substantial computational resources to produce realisti
arXiv:2604.23308v1 Announce Type: new Abstract: Offline multi-agent reinforcement learning (MARL) enables policy learning from fixed datasets, but is prone to coordination failure: agents trained on s
arXiv:2604.23088v1 Announce Type: cross Abstract: We present Code Broker, a multi agent system built with Google Agent Development Kit ADK that analyses Python code from files, local directories, or G
arXiv:2509.11717v5 Announce Type: replace-cross Abstract: Text-guided sound separation enables flexible audio editing, assistive listening, and open-domain source extraction, but systems such as Audio
arXiv:2604.22785v1 Announce Type: new Abstract: Large language model (LLM) deployments increasingly rely on multi-agent architectures in which multiple models either compete through routing mechanisms
arXiv:2604.22973v1 Announce Type: new Abstract: Predicting future trajectories of surrounding traffic agents is critical for safe autonomous navigation and collision avoidance. Despite all advances in
arXiv:2604.23003v1 Announce Type: new Abstract: In this paper, we propose a Physics-Informed Neural Network framework for time-dependent simulations of pollution propagation originating from moving em
arXiv:2604.23307v1 Announce Type: cross Abstract: Dual-target molecule generation, which focuses on discovering compounds capable of interacting with two target proteins, has garnered significant atte
arXiv:2604.23546v1 Announce Type: cross Abstract: Optical chemical structure recognition (OCSR) translates molecular images into machine-readable representations like SMILES strings or molecular graph
arXiv:2602.04753v2 Announce Type: replace-cross Abstract: An exponential growth of Machine Learning and its Generative AI applications brings with it significant security challenges, often referred to
arXiv:2604.23612v1 Announce Type: new Abstract: Partial Differential Equation (PDE)-based approaches have gained significant attention in image despeckling due to their strong capability to preserve s
arXiv:2604.22820v1 Announce Type: cross Abstract: Long-horizon tool-using tasks sometimes benefit from revisiting earlier subtasks for recovery and exploration, but added multi-agent workflow flexibil
arXiv:2604.23017v1 Announce Type: new Abstract: Stochastic Gradient Descent (SGD) is a known stochastic iterative method popular for large-scale convex optimization problems due to its simple implemen
arXiv:2604.24393v1 Announce Type: cross Abstract: There has been growing interest in studying the complexity of Rectified Linear Unit (ReLU) based activation networks. Recent work investigates the evo
arXiv:2604.23585v1 Announce Type: new Abstract: Financial institutions must track over 60,000 regulatory events annually, overwhelming manual compliance teams; the industry has paid over USD 300 billi
arXiv:2604.24648v1 Announce Type: new Abstract: Climate change and resource depletion demand a shift from the dominant linear 'take-make-use-dispose' paradigm of construction toward circular, low-wast
arXiv:2604.24634v1 Announce Type: cross Abstract: Light-activated drugs are a promising way to treat localized diseases for which existing treatments have severe side effects. However, their developme
arXiv:2604.24193v1 Announce Type: new Abstract: Containerised shipping underpins global trade, yet container loss at sea remains a persistent safety, environmental, and economic challenge. Despite com
arXiv:2604.23112v1 Announce Type: new Abstract: Multimodal Federated Learning (MMFL) enables privacy-preserving collaborative training, but real-world clinical applications often suffer from within-mo
arXiv:2604.23952v1 Announce Type: cross Abstract: Stochastic reduced-order models are widely used to represent the effective dynamics of complex systems, but estimating their drift and diffusion coeff
arXiv:2604.24745v1 Announce Type: new Abstract: In this paper, we propose a harmonized rotational gradient method, termed HRGrad, for simultaneously tackling multiscale time-dependent kinetic problems
arXiv:2604.22787v1 Announce Type: cross Abstract: Africa's green industrialization imperative demands reliable infrastructure for monitoring air quality. We present a satellite-reanalysis PM2.5 fusion
arXiv:2504.02316v4 Announce Type: replace-cross Abstract: Recent advances in zero-shot text-to-3D generation have revolutionized 3D content creation by enabling direct synthesis from textual descripti
arXiv:2309.00578v2 Announce Type: replace Abstract: In the context of unsupervised learning, Lloyd's algorithm is one of the most widely used clustering algorithms. It has inspired a plethora of work
arXiv:2604.23377v1 Announce Type: new Abstract: Neurosymbolic systems can satisfy logical constraints during learning without achieving the intended concept-label correspondence; this is a problem kno
arXiv:2604.23940v1 Announce Type: cross Abstract: Decompilation -- recovering source code from compiled binaries -- is essential for security analysis, malware reverse engineering, and legacy software
arXiv:2604.23949v1 Announce Type: new Abstract: Medical and public health experts must make real-time resource decisions, such as expanding hospital bed capacity, based on projected hospitalization tr
arXiv:2604.22801v1 Announce Type: cross Abstract: It is a challenging task to forecast equity prices in fast moving financial markets as this becomes even more difficult when the predictive signal is
arXiv:2604.24693v1 Announce Type: new Abstract: Linear activation steering is a powerful approach for eliciting the capabilities of large language models and specializing their behavior using limited
arXiv:2604.23069v1 Announce Type: new Abstract: Large language model (LLM) agents often struggle in long-context interactions. As the agent accumulates more interaction history, context management app
arXiv:2604.23987v1 Announce Type: new Abstract: Continual learning for large language models is typically evaluated through accuracy retention under sequential fine-tuning. We argue that this perspect
arXiv:2604.23281v1 Announce Type: cross Abstract: Human activity recognition serves as the foundation for various emerging applications. In recent years, researchers have used collaborative sensing of
arXiv:2604.23039v1 Announce Type: new Abstract: Physical human-robot interaction offers the potential to leverage human intelligence and robot physical capabilities to enable a range of exciting appli
arXiv:2604.23179v1 Announce Type: cross Abstract: Monitoring human activity in indoor environments is important for applications such as facility management, safety assessment, and space utilization a
arXiv:2604.23863v1 Announce Type: new Abstract: Autonomous systems are increasingly deployed in real-world environments, where they must achieve high performance while maintaining safety under state a
arXiv:2410.13903v3 Announce Type: replace-cross Abstract: Proprietary large language models (LLMs) exhibit strong generalization capabilities across diverse tasks and are increasingly deployed on edge
arXiv:2601.14952v2 Announce Type: replace-cross Abstract: While large language models now handle million-token contexts, their capacity for reasoning across entire document repositories remains largel
arXiv:2604.24637v1 Announce Type: cross Abstract: Block-sequential continual learning demands that a single model both protect prior solutions from catastrophic forgetting and efficiently infer at inf
arXiv:2604.24008v1 Announce Type: new Abstract: Post-Training Quantization (PTQ) compresses large language models to low bit-widths using a small calibration set, and its quality depends strongly on w
arXiv:2604.24170v1 Announce Type: new Abstract: Concept Bottleneck Models (CBMs) predict through human-interpretable concepts, but they typically output point concept probabilities that conflate epist
arXiv:2508.13650v3 Announce Type: replace Abstract: As large language models (LLMs) are increasingly deployed in real-world applications, the need to selectively remove unwanted knowledge while preser
arXiv:2604.22812v1 Announce Type: cross Abstract: STEM dropout rates remain high at universities, particularly in computer science programs with theory-intensive courses. Digital learning environments
arXiv:2510.17687v2 Announce Type: replace-cross Abstract: Multimodal Large Language Models (MLLMs) achieve strong reasoning and perception capabilities but are increasingly vulnerable to jailbreak att