Knowledge catalogue
arXiv:2605.12562v1 Announce Type: cross Abstract: Multi-window CT imaging captures complementary pathological information across anatomical structures of differing densities, yet existing deep learnin
arXiv:2605.13260v1 Announce Type: new Abstract: Physics-Informed Neural Networks (PINNs) and their variational counterparts (VPINNs) are neural networks that incorporate physical laws, making them use
The dual-threat landscape of enterprise AI security is coming into focus. The same autonomous agents transforming workforce productivity are also expanding the attack surface — and most organizations
arXiv:2605.13601v1 Announce Type: new Abstract: As intelligent systems are increasingly implemented in our society to make autonomous decisions, their commitment to human values raises serious concern
arXiv:2605.13277v1 Announce Type: cross Abstract: Visual evidence selection is a critical component of multimodal retrieval-augmented generation (RAG), yet existing methods typically rely on semantic
arXiv:2605.13407v1 Announce Type: new Abstract: Predicting cross-sectional stock returns is challenging due to low signal-to-noise ratios and evolving market regimes. Classical factor models offer int
arXiv:2605.13318v1 Announce Type: new Abstract: Chatbot usage has increased, including in fields for which they were never developed for--notably mental health support. To that end, we introduce Valid
arXiv:2605.13034v1 Announce Type: new Abstract: Recent deep research systems have improved the ability of large language models to produce long, grounded reports through iterative retrieval and reason
arXiv:2504.11944v3 Announce Type: replace-cross Abstract: Offline reinforcement learning (RL) learns effective policies from pre-collected datasets, offering a practical solution for applications wher
arXiv:2605.13621v1 Announce Type: new Abstract: Infrared-visible object detection improves detection performance by combining complementary features from multispectral images. Existing backbone-specif
arXiv:2605.12745v1 Announce Type: cross Abstract: Discrepancies between an agent's actual knowledge and what a person thinks the agent knows can hinder interactions. If an agent could detect such disc
arXiv:2605.13328v1 Announce Type: cross Abstract: Vision-and-Language Navigation (VLN) is a cornerstone of embodied intelligence. However, current agents often suffer from significant performance degr
arXiv:2605.13290v1 Announce Type: new Abstract: Validating training data for reasoning models typically requires expensive trial-and-error fine-tuning cycles. In this work, we investigate whether the
arXiv:2605.13105v1 Announce Type: new Abstract: Reinforcement learning (RL) fine-tuning has shown promise for Vision-Language-Action (VLA) models in robotic manipulation, but deployment-time visual sh
arXiv:2605.13242v1 Announce Type: cross Abstract: This paper studies the convergence of the Optimistic Multiplicative Weights Update algorithm (OMWU) in two player zero-sum games. Recent works have id
arXiv:2605.12922v1 Announce Type: new Abstract: Large language models can follow complex instructions in a single turn, yet over long multi-turn interactions they often lose the thread of instructions
arXiv:2601.14104v2 Announce Type: replace-cross Abstract: Backdoor attacks can cause reinforcement learning (RL) policies to behave normally under clean inputs while executing malicious behaviors when
arXiv:2511.13658v2 Announce Type: replace-cross Abstract: Deceptive reviews mislead consumers, harm businesses, and undermine trust in online marketplaces. Machine learning classifiers can learn from
arXiv:2605.13258v1 Announce Type: cross Abstract: In this work, we present our winning solution for the 8th UG2+ Challenge (CVPR 2026) Track 1: Image Restoration under All-weather Conditions. Our meth
arXiv:2604.04667v2 Announce Type: replace Abstract: Real-time depth reconstruction from ultra-high-resolution UAV imagery is essential for time-critical geospatial tasks such as disaster response, yet
arXiv:2605.11489v1 Announce Type: cross Abstract: 3D Gaussian Splatting (3DGS) enables high-quality real-time 3D rendering but faces challenges in efficiently scaling to ultra-dense scenes and high-re
arXiv:2605.12027v1 Announce Type: new Abstract: Reconstructing dynamic 4D scenes from monocular videos is a fundamental yet challenging task. While recent 3D foundation models provide strong geometric
arXiv:2605.12328v1 Announce Type: new Abstract: Data entry systems remain structurally vulnerable to categorical misclassifications, particularly in small and medium sized enterprises (SMEs). When nom
arXiv:2605.11585v1 Announce Type: new Abstract: This paper addresses the problem of image denoising for grayscale images. We propose a probabilistic image generative model that combines a quadtree reg
arXiv:2605.11759v1 Announce Type: cross Abstract: Dimensionality reduction is essential in simulation-based shape design, where high-dimensional parameterizations hinder optimization, surrogate modeli
arXiv:2605.11021v1 Announce Type: new Abstract: This paper develops a switching-system interpretation of Q-learning with linear function approximation (LFA) based on the joint spectral radius (JSR). W
arXiv:2605.10956v1 Announce Type: cross Abstract: Machine-learned surrogate modeling of advection may accelerate geoscientific models, but existing approaches have either achieved limited speedup or h
arXiv:2506.02215v5 Announce Type: replace Abstract: Collision avoidance -- involving a rapid threat detection and quick execution of the appropriate evasive maneuver -- is a critical aspect of driving
arXiv:2605.03629v1 Announce Type: cross Abstract: Distributed quantum algorithms offer a promising pathway to scale variational quantum algorithms beyond the constraints of noisy intermediate-scale qu
arXiv:2605.10987v1 Announce Type: new Abstract: Modern machine learning deployments increasingly compose specialized models into dynamic inference pipelines, where upstream components produce intermed
arXiv:2605.12375v1 Announce Type: new Abstract: Accurate crop yield forecasting in commercial soft fruit production is constrained by the data available in typical commercial farm records, which lack
arXiv:2605.11732v1 Announce Type: cross Abstract: In this paper, we present AgentDisCo, a novel Disentangled and Collaborative agentic architecture that formulates deep research as an adversarial opti
arXiv:2605.11026v1 Announce Type: cross Abstract: Defenses against indirect prompt injection (IPI) in tool-using LLM agents share two structural weaknesses. First, they all attempt to prevent attacks
arXiv:2605.11378v1 Announce Type: new Abstract: Agent evaluation requires assessing complex multi-step behaviors involving tool use and intermediate reasoning, making it costly and expertise-intensive
arXiv:2605.12069v1 Announce Type: new Abstract: Zero-shot anomaly detection aims to identify defects in unseen categories without target-specific training. Existing methods usually apply the same feat
arXiv:2605.12301v1 Announce Type: new Abstract: Operator learning has been highly successful for continuous mappings between infinite-dimensional spaces, such as PDE solution operators. However, many
arXiv:2512.12165v3 Announce Type: replace Abstract: Understanding camera motion is a fundamental problem in embodied perception and 3D scene understanding. While visual methods have advanced rapidly,
B9134 is a recent build release of llama.cpp, an open-source C/C++ library for efficient large language model inference. The release was tagged on May 13, 2026, representing an ongoing development ite
arXiv:2605.11387v1 Announce Type: new Abstract: We address the problem of fine-tuning pre-trained generative policies with reinforcement learning (RL) while preserving the multimodality of their actio
arXiv:2605.11904v1 Announce Type: new Abstract: The Nearest Class Mean (NCM) classifier is widely favored in Class-Incremental Learning (CIL) for its superior resistance to catastrophic forgetting com
arXiv:2605.11287v1 Announce Type: new Abstract: A persistent paradox in time-series forecasting is that structurally simple MLP and linear models often outperform high-capacity Transformers. We argue
arXiv:2605.12271v1 Announce Type: new Abstract: Humans often specify and create through visual artifacts: typography sheets, sketches, reference images, and annotated scenes. Yet modern visual generat
arXiv:2508.07642v3 Announce Type: replace-cross Abstract: Vision-and-Language Navigation (VLN) poses significant challenges for agents to interpret natural language instructions and navigate complex 3
arXiv:2605.11705v1 Announce Type: new Abstract: The training of large multimodal models fundamentally relies on massive image-text datasets, which inevitably incur prohibitive computational overhead.
arXiv:2605.11186v1 Announce Type: new Abstract: Auto-regressive decoding in Large Language Models (LLMs) is inherently memory-bound: every generation step requires loading the model weights and interm
arXiv:2605.11373v1 Announce Type: cross Abstract: The trustworthiness of AI decision-making systems is increasingly important. A key feature of such systems is the ability to provide recommendations f
arXiv:2605.11362v1 Announce Type: new Abstract: In the data-driven era, large-scale datasets are routinely collected and analyzed using machine learning (ML) and artificial intelligence (AI) to inform
arXiv:2605.12496v1 Announce Type: new Abstract: Autoregressive video generation aims at real-time, open-ended synthesis. Yet, cinematic storytelling is not merely the endless extension of a single sce
arXiv:2605.12053v1 Announce Type: new Abstract: This paper addresses the Motion Execution Gap, the disconnect between high-level symbolic task descriptions using semantic constraints and executable ro
arXiv:2508.14780v2 Announce Type: replace Abstract: Compression-based dissimilarities (CD) offer a flexible and domain-agnostic means of measuring similarity by identifying implicit information throug
arXiv:2605.11485v1 Announce Type: new Abstract: Imitation learning powered by generative models has proven effective for modeling complex single-agent behaviors. However, teaching multi-agent systems,
arXiv:2605.11165v1 Announce Type: new Abstract: Federated learning (FL) in heterogeneous environments remains challenging because client models often differ in both architecture and data distribution.
arXiv:2605.11311v1 Announce Type: cross Abstract: Diffusion models typically generate image batches from independent Gaussian initial noises. We argue that this independence assumption is only one cho
arXiv:2603.28488v2 Announce Type: replace Abstract: Large language models (LLMs) remain unreliable for high-stakes claim verification due to hallucinations and shallow reasoning. While retrieval-augme
arXiv:2605.12145v1 Announce Type: new Abstract: Multimodal learning seeks to integrate information across diverse sensory sources, yet current approaches struggle to balance cross-modal generalizabili
arXiv:2605.11504v1 Announce Type: new Abstract: Recent advances in Large Language Models (LLMs) have enabled agentic systems for complex, multi-step tasks; cybersecurity is emerging as a prominent app
arXiv:2605.11145v1 Announce Type: cross Abstract: Collaborative filtering (CF) models based on graph neural networks (GNNs) achieve strong performance in recommender systems by propagating user-item s
arXiv:2605.11764v1 Announce Type: new Abstract: Machine-learning predictors of biochemical activity often exhibit large random-split-to-leave-one-target-out generalisation gaps that have been document
arXiv:2312.02549v2 Announce Type: replace-cross Abstract: Temporal Language Grounding seeks to localize video moments that semantically correspond to a natural language query. Recent advances employ t
arXiv:2605.12138v1 Announce Type: cross Abstract: Generating realistic and user-preferred advertisements is a key challenge in e-commerce. Existing approaches utilize multiple independent models drive