Knowledge catalogue
arXiv:2605.12919v1 Announce Type: new Abstract: 3D Gaussian Splatting (3DGS) is becoming a practical representation for novel view synthesis, but its growing adoption, together with rapid advances in
arXiv:2605.13632v1 Announce Type: cross Abstract: In this paper, we propose GTA-VLA(Guide, Think, Act), an interactive Vision-Language-Action (VLA) framework that enables spatially steerable embodied
arXiv:2602.07029v3 Announce Type: replace-cross Abstract: This work introduces the first closed-loop adaptive optics (AO) system capable of optically correcting aberrations in real-time without a guid
arXiv:2601.18842v3 Announce Type: replace-cross Abstract: As GUI agents increasingly rely on screenshots to perceive and operate digital environments, they may inadvertently expose sensitive informati
arXiv:2605.13664v1 Announce Type: new Abstract: Thermal-infrared (TIR) hyperspectral imagery (HSI) provides critical scene information for various applications. However, its practical utility is sever
arXiv:2605.13073v1 Announce Type: new Abstract: In-the-wild 3D Gaussian Splatting remains challenging due to transient distractors and illumination-induced cross-view appearance inconsistencies. Exist
arXiv:2605.13821v1 Announce Type: new Abstract: Agentic evolution has emerged as a powerful paradigm for improving programs, workflows, and scientific solutions by iteratively generating candidates, e
arXiv:2605.13321v1 Announce Type: new Abstract: VLN has achieved remarkable progress by scaling data and model capacity. However, the assumption of a static environment breaks down in real-world indoo
arXiv:2605.12823v1 Announce Type: new Abstract: Coarse-grained (CG) molecular dynamics enables simulations of atomic systems such as biomolecules at timescales inaccessible to all-atom (AA) methods, b
arXiv:2605.13586v1 Announce Type: cross Abstract: Generating controllable and physically plausible indoor scenes is a pivotal prerequisite for constructing high-fidelity simulation environments for em
arXiv:2605.13213v1 Announce Type: new Abstract: Multi-modal multi-agent systems (MM-MAS) have gained increasing attention for their capacity to enable complex reasoning and coordination across diverse
arXiv:2605.13343v1 Announce Type: cross Abstract: Neural preconditioners for real-time physics simulation offer promising data-driven priors, but they often fail to capture long-range couplings effici
arXiv:2505.14587v2 Announce Type: replace-cross Abstract: Bootstrap methods have long been the cornerstone of ensemble learning in machine learning. This paper presents a theoretical analysis of boots
arXiv:2601.01860v2 Announce Type: replace Abstract: Detecting high-order epistasis is a fundamental challenge in genetic association studies due to the combinatorial explosion of candidate locus combi
arXiv:2601.17187v2 Announce Type: replace-cross Abstract: This paper investigates the problem of quantized matrix multiplication (MatMul), which has become crucial for the efficient deployment of larg
arXiv:2605.13768v1 Announce Type: cross Abstract: This is the second part of the work investigating quantized matrix multiplication (MatMul). In part I we considered the case of calibration-free quant
arXiv:2510.27258v2 Announce Type: replace-cross Abstract: The quadratic cost of scaled dot-product attention is a central obstacle to scaling autoregressive language models to long contexts. Linear-ti
arXiv:2605.13581v1 Announce Type: new Abstract: Hyperspectral image (HSI) restoration is crucial for reliable analysis, as real HSIs suffer from degradations like noise, blur, and resolution loss. How
arXiv:2605.13825v1 Announce Type: new Abstract: Frontier LLMs are increasingly deployed as agents that pick the next action after a long log of prior tool calls produced by the same or a different mod
arXiv:2605.13536v1 Announce Type: cross Abstract: High-Level Synthesis (HLS) compiles algorithmic C/C++ descriptions into hardware, with Quality of Results (QoR) -- latency and resource utilization --
arXiv:2605.13773v1 Announce Type: cross Abstract: Large Language Models (LLMs) are being employed widely to automate tasks across the software development life-cycle. It is, however, unclear whether t
arXiv:2601.19208v2 Announce Type: replace-cross Abstract: Semantic associations such as the link between 'bird' and 'flew' are foundational for language modeling as they enable models to go beyond mem
arXiv:2605.13625v1 Announce Type: new Abstract: Autonomous agents such as Claude Code and Codex now operate for hours or even days. Understanding their runtime behavior has become critical for downstr
arXiv:2602.11618v4 Announce Type: replace Abstract: Chemical Language Models (CLMs) pre-trained on large scale molecular data are widely used for molecular property prediction. However, the common bel
arXiv:2605.12619v1 Announce Type: cross Abstract: The perceptual representations supporting our ability to recognize faces remain a computational mystery. Deep neural networks offer mechanistic hypoth
arXiv:2605.13723v1 Announce Type: cross Abstract: The phrase 'human in the loop' is increasingly used to imply a sense of safety in relation to AI decision systems. It shouldn't. There are contexts wh
arXiv:2605.13311v1 Announce Type: new Abstract: Current AI-assisted innovation systems typically apply a single ideation methodology (such as TRIZ or Design Thinking) using sequential prompt-based wor
arXiv:2605.13094v1 Announce Type: new Abstract: The local analysis is an established approach to the study of singularities and mobility of linkages. Key result of such analyses is a local picture of
arXiv:2605.13706v1 Announce Type: cross Abstract: From pre-training to query-time augmentation, web-scraped data helps to improve the quality and contextual relevancy of content generated by large lan
arXiv:2605.12785v1 Announce Type: new Abstract: Hybrid machine learning combines physical knowledge with data-driven models to enhance interpretability and performance. In this context, Port-Hamiltoni
arXiv:2605.12693v1 Announce Type: new Abstract: Decision-focused learning trains predictive models end-to-end against downstream decision loss, but online settings suffer delayed feedback: outcomes ma
arXiv:2605.12967v1 Announce Type: new Abstract: The rapid advancement of generative AI has enabled the creation of highly realistic and diverse synthetic images, posing critical challenges for image p
arXiv:2605.13293v1 Announce Type: new Abstract: Boundary Representation (BRep) is the standard format for Computer-Aided Design (CAD), yet reconstructing high-quality BReps from single-view images rem
arXiv:2605.12999v1 Announce Type: cross Abstract: Closed-loop brain-computer interfaces often require both a forecast of upcoming neural population activity and a readout of the animal's behavioral st
arXiv:2602.04923v2 Announce Type: replace Abstract: Neural operators have emerged as powerful surrogates for the solution of partial differential equations (PDEs), yet their ability to handle general,
arXiv:2605.08320v1 Announce Type: cross Abstract: Monocular depth estimation (MDE) with self-supervised training approaches struggles in low-texture areas, where photometric losses may lead to ambiguo
arXiv:2601.21892v2 Announce Type: replace-cross Abstract: Classifier-free guidance (CFG) is a widely used technique for controllable generation in diffusion and flow-based models. Despite its empirica
arXiv:2605.13229v1 Announce Type: new Abstract: LLMs have shown immense potential for code translation, yet they often struggle to ensure both syntactic correctness and semantic consistency. While pre
arXiv:2605.12573v1 Announce Type: cross Abstract: Diffusion-based posterior sampling (PS) is a leading framework for imaging inverse problems, combining learned priors with measurement constraints. Ye
arXiv:2605.13801v1 Announce Type: cross Abstract: As generative AI models such as large language models (LLMs) become more pervasive, ensuring the safety, robustness, and overall trustworthiness of th
arXiv:2605.12530v1 Announce Type: cross Abstract: LLM fairness should be evaluated through in-situ conversational behavior rather than standardized-test Q&A benchmarks. We show that the standardized-t
arXiv:2605.13292v1 Announce Type: cross Abstract: Most existing medical dialogue systems operate in a single-turn question--answering paradigm or rely on template-based datasets, limiting conversation
arXiv:2605.13338v1 Announce Type: cross Abstract: Large Reasoning Models (LRMs) are increasingly integrated into systems requiring reliable multi-step inference, yet this growing dependence exposes ne
arXiv:2601.22853v3 Announce Type: replace Abstract: Multimodal deep learning (MDL) has achieved remarkable success across various domains, yet its practical deployment is often hindered by incomplete
arXiv:2605.12765v1 Announce Type: new Abstract: Large Language Models memorize vast amounts of training data, raising concerns regarding privacy, copyright infringement, and safety. Machine unlearning
arXiv:2605.12536v1 Announce Type: cross Abstract: The Free Energy Principle (FEP) is a leading framework for mathematically modeling self-organization and learning, while Integrated Information Theory
arXiv:2605.12724v1 Announce Type: cross Abstract: Instruction-based image editing exhibits heterogeneous difficulty not only across cases but also across regions of an image, motivating refinement app
arXiv:2605.13539v1 Announce Type: new Abstract: Simulative and scenario-based testing are crucial methods in the safety assurance for automated driving systems. To ensure that simulation results are r
arXiv:2605.13786v1 Announce Type: new Abstract: Background: Pregnancy-associated thrombotic microangiopathy (P-TMA) is rare but life-threatening. Early risk prediction before overt clinical presentati
arXiv:2602.11202v3 Announce Type: replace-cross Abstract: Reasoning models produce long traces of intermediate decisions and tool calls, making test-time verification important for ensuring correctnes
arXiv:2604.04692v2 Announce Type: replace-cross Abstract: Automated fact-checking is a crucial task that supports a responsible information ecosystem. While recent research has progressed from text-on
arXiv:2605.12725v1 Announce Type: new Abstract: Recent video anomaly detection research has expanded rapidly with an emphasis on general models of normality intended to work across many different scen
arXiv:2605.12768v1 Announce Type: cross Abstract: Open time-series forecasting (TSF) benchmarks cover retail, energy, weather, and traffic, but supply-chain logistics remains underserved. We introduce
arXiv:2605.13261v1 Announce Type: cross Abstract: In menstrual cycle tracking apps (MCTAs), AI-based predictions and insights have become increasingly popular. These features enable users to receive p
arXiv:2605.13245v1 Announce Type: new Abstract: Language models can produce convincing scientific analyses, but repeated generations on the same data do not guarantee the same result. A researcher may
arXiv:2605.12924v1 Announce Type: new Abstract: The instrumental-variables (IV) setting is standard for partial identification of causal effects when unobserved confounding makes point identification
arXiv:2605.13813v1 Announce Type: new Abstract: Automated CT triage requires models that are simultaneously accurate across diverse pathologies and reliable under institutional shift. While Vision Tra
arXiv:2605.13013v1 Announce Type: new Abstract: Diffusion world models have recently become competitive for online model-based reinforcement learning, but current approaches expose a tension: pixel di
arXiv:2605.12772v1 Announce Type: new Abstract: Wu et al. (2026) showed that most frontier large language models (LLMs) recommend a sponsored, roughly twice-as-expensive flight when their system promp
arXiv:2605.13322v1 Announce Type: new Abstract: Kamon (family crests) are an important part of Japanese culture and a natural test case for compositional visual recognition: each crest combines a smal