Knowledge catalogue
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:2605.13229v1 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:2605.12530v1 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:2605.13338v1 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
Anyscale has introduced a new Agent Skill designed to enhance LLM post-training capabilities, likely enabling developers to build and train agentic systems more effectively using Ray's distributed com
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:2605.13013v1 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:2605.13160v1 Announce Type: cross Abstract: Modern Bayesian optimization and adaptive sampling methods increasingly rely on nonlinear parametric models, yet theoretical guarantees for such model
arXiv:2605.13734v1 Announce Type: cross Abstract: LLMs are widely adopted in production, pushing inference systems to their limits. Disaggregated LLM serving (e.g., PD separation and KV state disaggre
LangChain Labs has launched as a new initiative from the LangChain team. The announcement was made by Harrison Chase, the creator and lead developer of LangChain, on X (formerly Twitter). LangChain La
arXiv:2605.12714v1 Announce Type: new Abstract: Hidden states change substantially across the layers of modern language models, but most layer-wise analyses focus on one aspect of that change. We prop
arXiv:2605.13284v1 Announce Type: cross Abstract: Recent advancements in large language models demonstrate that injecting perturbations can substantially enhance extrapolation performance. However, cu
arXiv:2605.13751v1 Announce Type: new Abstract: Establishing trustworthy safety assurance for autonomous driving systems (ADSs) requires evidence that failures arise from avoidable system deficiencies
arXiv:2605.12682v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly used as reasoning modules in many applications. While they are efficient in certain tasks, LLMs often stru
arXiv:2605.12561v1 Announce Type: new Abstract: Safe reinforcement learning (RL) typically asks extit{what} an agent should do. We ask extit{when} it needs to act, and show that a single policy can jo
arXiv:2605.12741v1 Announce Type: new Abstract: Enabling Large Language Models (LLMs) to continuously improve from environmental interactions is a central challenge in post-training. While on-policy s
arXiv:2605.13188v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly deployed in settings where the available context is incomplete or degraded. We argue that an LLM generat
arXiv:2603.02245v3 Announce Type: replace-cross Abstract: Decoding infant cry causes remains challenging for healthcare monitoring due to short nonstationary signals, limited annotations, and strong d
arXiv:2605.13068v1 Announce Type: new Abstract: Nonlinear inverse problems often trade inexpensive but fragile first-order updates against curvature-aware methods such as Gauss-Newton and Levenberg-Ma
arXiv:2605.13163v1 Announce Type: cross Abstract: Foundation models and low-rank adapters enable efficient on-device generative AI but raise risks such as intellectual property leakage and model recov
arXiv:2605.12556v1 Announce Type: new Abstract: Low-light image enhancement is challenging due to complex degradations, including amplified noise, artifacts, and color distortion. While Retinex-based
arXiv:2605.12570v1 Announce Type: new Abstract: The accurate classification of benign and malignant pulmonary nodules in CT scans is critical for early lung cancer screening, yet remains challenging d
arXiv:2605.13218v1 Announce Type: new Abstract: Cancer is one of the leading causes of death worldwide, making the development of rapid, minimally invasive, label-free and scalable diagnostic strategi
arXiv:2605.13754v1 Announce Type: new Abstract: In this paper, we study the problem of manipulation skill acquisition for performing construction activities consisting of repetitive tasks (e.g., build
arXiv:2605.13037v1 Announce Type: new Abstract: Current interactive LLM agents rely on goal-conditioned stepwise planning, where environmental understanding is acquired reactively during execution rat
arXiv:2506.12876v2 Announce Type: replace Abstract: The rapid scaling of large language models~(LLMs) has made inference efficiency a primary bottleneck in the practical deployment. To address this, s
arXiv:2605.13688v1 Announce Type: new Abstract: Medical segmentation foundation models such as SAM and MedSAM provide strong prompt-driven segmentation, but their image encoders are still too large fo
arXiv:2605.13753v1 Announce Type: cross Abstract: We propose min Generalized Sliced Gromov--Wasserstein (min-GSGW), a sliced formulation for the Gromov--Wasserstein (GW) problem using expressive gener
arXiv:2605.13551v1 Announce Type: new Abstract: Neural Posterior Estimation (NPE) enables rapid parameter inference for complex simulators with intractable likelihoods. NPE trains an inference network
arXiv:2605.13126v1 Announce Type: cross Abstract: Graph Neural Networks (GNNs) suffer from over-squashing in deep message passing, where information from exponentially growing neighborhoods is compres
arXiv:2605.13527v1 Announce Type: new Abstract: Reusable skills have become a core substrate for improving agent capabilities, yet most existing skill packages encode reusable behavior primarily as te
arXiv:2512.12634v3 Announce Type: replace Abstract: Mobile GUI Agents, AI agents capable of interacting with mobile applications on behalf of users, have the potential to transform human computer inte
arXiv:2605.12856v1 Announce Type: new Abstract: The emergence of multi-agent systems introduces novel moderation challenges that extend beyond content filtering. Agents with {em malicious intent} may
arXiv:2605.13345v1 Announce Type: new Abstract: Emergency departments (ED) face challenges in patient care and resource management. We propose to explore optimization strategies in a realistic and fle
arXiv:2605.05739v2 Announce Type: replace-cross Abstract: Forecast evaluation in finance has relied on aggregate accuracy metrics and predictive-accuracy tests built on point-forecast errors. These in
arXiv:2605.12652v1 Announce Type: cross Abstract: Large language models are often post-trained with sparse verifier rewards, which indicate whether a sampled trajectory succeeds but provide limited gu
arXiv:2605.12628v1 Announce Type: new Abstract: As autonomous vehicles move from a simplified research setting to practical use, there exists a large gap between the dynamic behavior of a human drivin
arXiv:2605.13476v1 Announce Type: new Abstract: Content-adaptive compression has always been a key direction in neural video coding (NVC), aiming to mitigate the domain gap between training and testin
arXiv:2605.13044v1 Announce Type: cross Abstract: LLM-powered agents can silently delete documents, leak credentials, or transfer funds on a routine user request, not because the agent was attacked, b
arXiv:2605.13086v1 Announce Type: new Abstract: This paper presents an object manipulation strategy for the Variable Topology Truss (VTT) system, a truss robot that comprises actuated truss members co
arXiv:2605.13018v1 Announce Type: new Abstract: Object-centric scene understanding is a fundamental challenge in computer vision. Existing approaches often rely on multi-stage pipelines that first app
arXiv:2605.13025v1 Announce Type: new Abstract: We study the problem of learning Nash equilibria in offline two-player zero-sum Markov games. While existing approaches often rely on explicit pessimism
arXiv:2605.13146v1 Announce Type: cross Abstract: Artificial intelligence (AI) has transformed imaging inverse problems, from medical diagnostics to Earth observation. Yet deep neural networks can pro
arXiv:2605.13143v1 Announce Type: cross Abstract: Knowledge distillation is widely used to improve generalization in practice, yet its theoretical understanding remains elusive. In the standard distil
arXiv:2510.21060v3 Announce Type: replace-cross Abstract: Policy optimization (PO) is a cornerstone of modern reinforcement learning (RL), with diverse applications spanning robotics, healthcare, and
arXiv:2605.12691v1 Announce Type: new Abstract: Progression, the task of updating a knowledge base to reflect action effects, generally requires second-order logic. Identifying first-order special cas
arXiv:2605.12753v1 Announce Type: cross Abstract: INTRODUCTION | Fully supervised 3D segmentation of high-resolution ex vivo MRI is limited by the prohibitive cost of volumetric annotation, forcing re
arXiv:2605.13155v1 Announce Type: new Abstract: Text-to-image generation models have achieved remarkable progress in preference optimization, yet achieving robust alignment across diverse reward model
arXiv:2605.12541v1 Announce Type: cross Abstract: Electrocardiography (ECG) is the clinical standard for cardiac assessment but requires dedicated hardware that does not scale to daily-life monitoring
arXiv:2605.13583v1 Announce Type: new Abstract: Recent advances have demonstrated that coded aperture snapshot spectral imaging (CASSI) systems show great potential for capturing 3D hyperspectral imag
arXiv:2602.23089v2 Announce Type: replace Abstract: The Bayesian update step poses significant computational challenges in high-dimensional nonlinear estimation. While log-homotopy particle flow filte
arXiv:2605.12966v1 Announce Type: new Abstract: Is monolithic scaling the only path to AGI? This paper challenges the dogma that purely scaling a single model is sufficient to achieve Artificial Gener
arXiv:2605.13081v1 Announce Type: new Abstract: Missing modalities are prevalent in real-world Alzheimer's disease (AD) assessment and pose a significant challenge to multimodal learning, particularly
arXiv:2603.02337v2 Announce Type: replace-cross Abstract: Flow matching (FM) learns vector fields by regressing stochastic velocity targets along intermediate distributions p_t. We identify a geometri
arXiv:2605.13396v1 Announce Type: new Abstract: Face Image Quality Assessment (FIQA) evaluates the utility of a face image for automated face recognition (FR) systems. In this work, we propose PreFIQs
arXiv:2602.02001v2 Announce Type: replace-cross Abstract: Quantization Error Reconstruction (QER) reduces accuracy loss in Post-Training Quantization (PTQ) by approximating weights as mathbf{W} approx
arXiv:2605.13027v1 Announce Type: new Abstract: Text image super-resolution (Text-SR) requires more than visually plausible detail synthesis: slight errors in stroke topology may alter character ident
arXiv:2512.08411v2 Announce Type: replace Abstract: Model-based planning in robotic domains is challenged by the hybrid nature of physical dynamics, where continuous motion is punctuated by discrete e
arXiv:2603.20334v2 Announce Type: replace-cross Abstract: In high-complexity abstract reasoning, a system must infer a latent rule from a few examples or structured observations and apply it to unseen