Knowledge catalogue
arXiv:2604.10305v1 Announce Type: cross Abstract: Cooperative perception allows connected vehicles and roadside infrastructure to share sensor observations, creating a fused scene representation beyon
arXiv:2604.11251v1 Announce Type: new Abstract: Training language-conditioned whole-body controllers for humanoid robots requires large-scale datasets pairing motion trajectories with natural-language
arXiv:2604.11790v1 Announce Type: cross Abstract: Tool-augmented Large Language Model (LLM) agents have demonstrated impressive capabilities in automating complex, multi-step real-world tasks, yet rem
arXiv:2604.11784v1 Announce Type: cross Abstract: GUI agents drive applications through their visual interfaces instead of programmatic APIs, interacting with arbitrary software via taps, swipes, and
arXiv:2604.10352v1 Announce Type: new Abstract: Stateful tool-using LLM agents treat the context window as working memory, yet today's agent harnesses manage residency and durability as best-effort, c
arXiv:2604.11539v1 Announce Type: cross Abstract: Human perception of visual similarity is inherently adaptive and subjective, depending on the users' interests and focus. However, most image retrieva
arXiv:2604.10032v1 Announce Type: cross Abstract: While modern generative models such as diffusion-based architectures have enabled impressive creative capabilities, they also raise important safety a
arXiv:2604.11801v1 Announce Type: new Abstract: With the recent progress of Large Language Models (LLMs), there is a growing interest in applying these models to solve complex and challenging problems
arXiv:2604.11201v1 Announce Type: cross Abstract: LLM agents now perform strongly in software engineering, deep research, GUI automation, and various other applications, while recent agent scaffolds a
arXiv:2604.10426v1 Announce Type: cross Abstract: Large Language Models (LLMs) struggle with knowledge-intensive tasks due to hallucinations and fragmented reasoning over dispersed information. While
arXiv:2604.10235v1 Announce Type: new Abstract: Agentic code tasks such as fault localization and patch generation require processing long codebases under tight memory constraints, where the Key-Value
arXiv:2604.10496v1 Announce Type: new Abstract: Outliers have emerged as a fundamental bottleneck in preserving accuracy for low-precision large models, particularly within Mixture-of-Experts (MoE) ar
arXiv:2604.11641v1 Announce Type: cross Abstract: Code agents are advancing rapidly, but debugging them is becoming increasingly difficult. As frameworks orchestrate parallel tool calls and multi-stag
arXiv:2604.01687v2 Announce Type: replace Abstract: Anthropic proposes the concept of skills for LLM agents to tackle multi-step professional tasks that simple tool invocations cannot address. A tool
arXiv:2604.10584v1 Announce Type: new Abstract: Multispectral and Hyperspectral Image Fusion (MHIF) aims to reconstruct high-resolution images by integrating low-resolution hyperspectral images (LRHSI
arXiv:2604.10219v1 Announce Type: new Abstract: Multimodal Large Reasoning Models (MLRMs) have achieved remarkable strides in visual reasoning through test time compute scaling, yet long chain reasoni
arXiv:2603.22479v3 Announce Type: replace-cross Abstract: Defining a constructive process to build general capabilities for language models in an automatic manner is considered an open problem in arti
arXiv:2604.11741v1 Announce Type: new Abstract: Vision-language models (VLMs) have shown impressive capabilities in perceptual tasks, yet they degrade in complex multi-hop reasoning under multiplayer
arXiv:2509.19883v2 Announce Type: replace-cross Abstract: Singing Voice Synthesis (SVS) aims to generate expressive vocal performances from structured musical inputs such as lyrics and pitch sequences
arXiv:2604.10689v1 Announce Type: new Abstract: Recent developments have shown that Muon-type optimizers based on linear minimization oracles (LMOs) over non-Euclidean norm balls have the potential to
arXiv:2604.10037v1 Announce Type: cross Abstract: We present a metasurface imaging system capable of simultaneously capturing two images at close range (1-2~cm) and an additional image at long range (
arXiv:2604.10316v1 Announce Type: new Abstract: Background: Large Language Models (LLMs) are transforming artificial intelligence applications in healthcare due to their ability to understand, generat
arXiv:2604.09621v1 Announce Type: new Abstract: We present an agent-driven approach to the construction of parameter inference pipelines for scientific data analysis. Our method leverages a multi-agen
arXiv:2604.10875v1 Announce Type: cross Abstract: AI governance efforts increasingly rely on audit standards: agreed-upon practices for conducting audits. However, poorly designed standards can hide a
arXiv:2604.09836v1 Announce Type: new Abstract: AI agents hold growing promise for accelerating scientific discovery; yet, a lack of frontier evaluations hinders adoption into real workflows. Expert-w
arXiv:2604.11584v1 Announce Type: cross Abstract: We study computational aspects of a key problem in robust statistics -- the penalized least trimmed squares (LTS) regression problem, a robust estimat
arXiv:2604.10035v1 Announce Type: cross Abstract: In this study, we developed a computational implementation for a model of metaphor comprehension based on the theory of indeterminate natural transfor
arXiv:2604.10627v1 Announce Type: cross Abstract: How the brain supports language across different languages is a basic question in neuroscience and a useful test for multilingual artificial intellige
arXiv:2604.11386v1 Announce Type: cross Abstract: Recent advancements in foundational models, such as large language models and world models, have greatly enhanced the capabilities of robotics, enabli
arXiv:2505.11237v3 Announce Type: replace-cross Abstract: Metaphorical imagination, the ability to connect seemingly unrelated concepts, is fundamental to human cognition and communication. While unde
arXiv:2512.09056v3 Announce Type: replace Abstract: Object pose estimation is a fundamental task in computer vision and robotics, yet most methods require extensive, dataset-specific training. Concurr
arXiv:2604.09722v1 Announce Type: cross Abstract: Speculative decoding enables collaborative Large Language Model (LLM) inference across cloud and edge by separating lightweight token drafting from he
arXiv:2604.09750v1 Announce Type: cross Abstract: Large Reasoning Models (LRMs) have achieved remarkable performance across diverse domains, yet their decision-making under conflicting objectives rema
arXiv:2604.09746v1 Announce Type: cross Abstract: As large language models (LLMs) are increasingly deployed as autonomous agents, understanding how strategic behavior emerges in multi-agent environmen
arXiv:2604.10146v1 Announce Type: new Abstract: Multi-output Gaussian Processes provide principled uncertainty-aware learning of vector-valued fields but are difficult to deploy in large-scale, distri
arXiv:2604.11287v1 Announce Type: new Abstract: Background: Large language models (LLMs) have been explored as tools for generating personalized exercise prescriptions, yet the consistency of outputs
arXiv:2601.07224v2 Announce Type: replace Abstract: While Hybrid Supervised Fine-Tuning (SFT) followed by Reinforcement Learning (RL) has become the standard paradigm for training LLM agents, effectiv
arXiv:2602.13135v2 Announce Type: replace Abstract: Assumption-based Argumentation (ABA) is a well-established form of structured argumentation. ABA frameworks with an underlying atomic language are w
arXiv:2601.11310v2 Announce Type: replace Abstract: Semantic ultra-high-resolution (UHR) image segmentation is essential in remote sensing applications such as aerial mapping and environmental monitor
arXiv:2511.01763v2 Announce Type: replace-cross Abstract: Binary decompilation plays an important role in software security analysis, reverse engineering, and malware understanding when source code is
arXiv:2604.11623v1 Announce Type: new Abstract: We introduce Context Kubernetes, an architecture for orchestrating enterprise knowledge in agentic AI systems, with a prototype implementation and eight
arXiv:2604.10344v1 Announce Type: new Abstract: The classical approach to detecting depression from vision emphasizes interpretable features, such as facial expression, and classifiers such as the Sup
arXiv:2604.11521v1 Announce Type: cross Abstract: We propose continuous adversarial flow models, a type of continuous-time flow model trained with an adversarial objective. Unlike flow matching, which
arXiv:2604.10173v1 Announce Type: cross Abstract: Magnetic point-group analysis classifies altermagnets but returns only a binary symmetry verdict, leaving spin-splitting energy (SSE) inaccessible wit
arXiv:2604.10958v1 Announce Type: cross Abstract: We study continuous-time online learning where data are generated by a diffusion process with unknown coefficients. The learner employs a two-layer ne
arXiv:2602.05880v2 Announce Type: replace Abstract: Boundary detection of irregular and translucent objects is an important problem with applications in medical imaging, environmental monitoring and m
arXiv:2604.09813v1 Announce Type: new Abstract: Existing synthetic tool-use corpora are primarily designed for offline supervised fine-tuning, yet reinforcement learning (RL) requires executable envir
arXiv:2601.07516v2 Announce Type: replace-cross Abstract: Vision-language models are increasingly employed as multimodal conversational agents (MCAs) for diverse conversational tasks. Recently, reinfo
arXiv:2604.11389v1 Announce Type: new Abstract: Reliable recognition of standard cine cardiac MRI views is essential because each view determines which cardiac anatomy is visualized and which quantita
arXiv:2604.10505v1 Announce Type: new Abstract: Agent based systems are more common than we may think. A Promise Theory perspective on cooperation, in systems of human-machine agents, offers a unified
arXiv:2508.03447v2 Announce Type: replace Abstract: Recently, large pre-trained vision-language models have shown remarkable performance in zero-shot anomaly detection (ZSAD). With fine-tuning on a si
arXiv:2604.11359v1 Announce Type: new Abstract: Accurate interpretation of electrocardiogram (ECG) remains challenging due to the scarcity of labeled data and the high cost of expert annotation. Self-
arXiv:2604.10597v1 Announce Type: cross Abstract: State Space Models (SSMs), represented by the Mamba family, provide linear-time sequence modeling and are attractive for long-context inference. Yet p
arXiv:2604.10358v1 Announce Type: new Abstract: Ensuring safe physical interaction between torque-controlled manipulators and humans is essential for deploying robots in everyday environments. Model P
arXiv:2604.11165v1 Announce Type: cross Abstract: Clinical decision-making often involves selecting tests that are costly, invasive, or time-consuming, motivating individualized, sequential strategies
arXiv:2604.10031v1 Announce Type: cross Abstract: Theory of Mind (ToM), the ability to attribute mental states to others, is a hallmark of social intelligence. While large language models (LLMs) demon
arXiv:2502.11008v2 Announce Type: replace Abstract: Counterfactual reasoning is widely recognized as one of the most challenging and intricate aspects of causality in artificial intelligence. In this
arXiv:2604.10039v1 Announce Type: new Abstract: Vision--language models (VLMs) have achieved impressive performance on complex multimodal reasoning tasks, yet they still fail on simple grounding skill
arXiv:2508.16644v4 Announce Type: replace Abstract: Diffusion models excel at photorealistic synthesis but struggle with precise object counts, especially in high-density settings. We introduce COUNTL
arXiv:2508.09533v2 Announce Type: replace-cross Abstract: Detecting tiny objects in multimodal Red-Green-Blue-Thermal (RGBT) imagery is a critical challenge in computer vision, particularly in surveil