Knowledge catalogue
arXiv:2607.07883v1 Announce Type: new Abstract: There is relatively little, public, and model-ready data on industrial machinery for African economies. This makes it hard to do quantitative analysis o
arXiv:2607.07720v1 Announce Type: cross Abstract: Sleep physiology arises from the coordinated dynamics of the central nervous system (CNS) and autonomic nervous system (ANS), as reflected by multimod
arXiv:2607.08423v1 Announce Type: new Abstract: The rapid integration of Large Vision-Language Models (VLMs) into critical infrastructure promises to revolutionize personalized healthcare and dietary
arXiv:2606.15920v2 Announce Type: replace Abstract: Reinforcement learning for multimodal large language models (MLLMs) is often hindered by severe reward sparsity in complex reasoning tasks. This cha
arXiv:2607.08391v1 Announce Type: cross Abstract: Making tradeoffs between execution latency and result utility (i.e., anytime computing) for adapting to dynamic operational requirements has been show
arXiv:2607.08250v1 Announce Type: new Abstract: Dynamic scene reconstruction remains challenging due to the heterogeneous and spatially varying nature of real-world motion. Although recent 3D Gaussian
arXiv:2511.13999v2 Announce Type: replace Abstract: We study the running time, in terms of first order oracle queries, of differentially private empirical/population risk minimization of Lipschitz con
arXiv:2607.08371v1 Announce Type: cross Abstract: Synthetic multi-speaker conversations are widely used to train conversational automatic speech recognition (ASR) systems, but it remains unclear which
arXiv:2607.08193v1 Announce Type: cross Abstract: Open-ended curricula in Reinforcement Learning (RL) aim to train generally-capable agents by identifying tasks that facilitate learning increasingly c
arXiv:2407.09016v2 Announce Type: replace Abstract: Object-oriented embodied navigation tasks require agents to locate specific objects, either defined by category or images, in unseen environments. W
arXiv:2607.08763v1 Announce Type: cross Abstract: Reasoning has become a core capability for large models, especially when reliable decisions require understanding logical consequences. Recent video g
arXiv:2607.08766v1 Announce Type: new Abstract: We propose OPSD-V, an on-policy self-distillation paradigm for post-training few-step autoregressive (AR) video diffusion models. Existing few-step AR v
arXiv:2607.07884v1 Announce Type: new Abstract: In this short note we consider the gradient descent dynamics of deep scalar linear networks, f(x) = prod_{l=1}^L w_l x, which enjoy exact time-course so
arXiv:2607.08180v1 Announce Type: cross Abstract: The rise of LLM-based agents with reasoning, summarization, and memory capabilities has created a new threat surface for online content that conventio
arXiv:2607.08173v1 Announce Type: new Abstract: Black box auditing of language models is an essential pre-deployment tool, but it may miss subtle forms of misalignment and hidden information. To bette
arXiv:2607.08079v1 Announce Type: new Abstract: Accurate photovoltaic (PV) power forecasting is essential for reliable grid dispatch and renewable energy integration, yet it remains challenging becaus
arXiv:2502.15543v4 Announce Type: replace-cross Abstract: Large language models (LLMs) integrated with retrieval-augmented generation (RAG) have improved factuality by grounding outputs in external ev
arXiv:2603.02204v2 Announce Type: replace Abstract: Selective conformal prediction can yield substantially tighter uncertainty sets when we can identify calibration examples that are exchangeable with
arXiv:2607.07935v1 Announce Type: cross Abstract: We present path_boost, a Python package for interpretable supervised learning on graph-structured input data. The package implements PathBoost, a grad
arXiv:2512.16558v3 Announce Type: replace Abstract: Clustering is a cornerstone of modern data analysis. Detecting clusters in exploratory data analyses (EDA) requires algorithms that make few assumpt
arXiv:2607.07916v1 Announce Type: new Abstract: Large language models exhibit recurring behavioural patterns -- personas -- that shape generalisation and safety, but we lack reliable tools for decompo
arXiv:2607.08066v1 Announce Type: new Abstract: Chain-of-thought (CoT) monitoring is a promising safety mechanism for AI agents, based on the premise that visible reasoning traces can surface misalign
arXiv:2607.08025v1 Announce Type: new Abstract: While neural PDE solvers have demonstrated significant potential for accelerating engineering simulations, existing architectures remain constrained by
arXiv:2603.15886v3 Announce Type: replace-cross Abstract: We present PhasorFlow, an open-source Python library for computing on the S^1 unit circle. Inputs are encoded as complex phasors z=e^{iphi} on
arXiv:2505.16456v3 Announce Type: replace Abstract: Recent advances in 3D content generation have amplified demand for dynamic models that are both visually realistic and physically consistent. Howeve
arXiv:2607.07830v1 Announce Type: new Abstract: Model-free reinforcement learning has enabled impressive humanoid locomotion; however, control on steep slopes remains largely unexplored. Unlike flat o
arXiv:2607.07863v1 Announce Type: new Abstract: In physically dominated machining processes, experimental datasets are small, expensive, and material-specific; in this regime, data curation, evaluatio
arXiv:2607.08202v1 Announce Type: new Abstract: Estimating original-space conditional expectations is central to value-driven recommender systems, including dwell time, GMV, and LTV forecasting. Stand
arXiv:2607.08233v1 Announce Type: new Abstract: A central challenge in building intelligent systems is enabling agents to jointly perceive complex inputs, form hypotheses about hidden patterns, and de
arXiv:2607.08034v1 Announce Type: cross Abstract: Large language models (LLMs) are used worldwide, yet disproportionately reflect Western values, limiting their ability to represent diverse value syst
arXiv:2607.08269v1 Announce Type: new Abstract: Existing retrieval-augmented generation (RAG) systems treat web pages as flat text, losing the structural and semantic signals encoded in HTML. We prese
arXiv:2607.08725v1 Announce Type: cross Abstract: Recent progress in 3D human pose estimation has made markerless recovery of skeletal motion increasingly accurate and scalable. However, most pose est
arXiv:2607.08072v1 Announce Type: new Abstract: End-to-end models that map multimodal inputs directly to future trajectories/maneuvers have emerged as an increasingly prominent research paradigm in au
arXiv:2310.07895v2 Announce Type: replace Abstract: This paper presents a method to efficiently classify the gastroenterologic section of images derived from Video Capsule Endoscopy (VCE) studies by e
arXiv:2607.08429v1 Announce Type: cross Abstract: Male infertility is a significant yet often underdiagnosed aspect of reproductive health, with semen analysis serving as the cornerstone of clinical e
arXiv:2607.07834v1 Announce Type: new Abstract: Pseudo-nitzschia diatoms pose recurrent risks to coastal ecosystems and shellfish harvesting along the Portuguese Atlantic coast. Here we develop and ev
arXiv:2607.08449v1 Announce Type: new Abstract: Determining agricultural potential is fundamental to sustainable land management and agricultural planning. Remote sensing data is increasingly valuable
arXiv:2607.08347v1 Announce Type: cross Abstract: Active testing provides a label--efficient approach to risk estimation by adaptively selecting which test points should be labelled. However, existing
arXiv:2510.19475v2 Announce Type: replace Abstract: Monocular 3D human pose estimation remains a fundamentally ill-posed inverse problem due to the inherent depth ambiguity in 2D-to-3D lifting. While
arXiv:2607.07769v1 Announce Type: cross Abstract: Starting from the utilization of deep neural networks to approximate the state-action value function that led to winning one of the most challenging g
arXiv:2607.08147v1 Announce Type: cross Abstract: Autonomous web agents promise to automate everyday browsing tasks, but inherit one of the web's oldest attack surfaces. Cross-Site Scripting proved th
arXiv:2607.08270v1 Announce Type: new Abstract: Forecasting the future anatomy of slow-evolving neurodegenerative diseases could enable earlier, more targeted intervention and improve clinical trial d
arXiv:2607.08691v1 Announce Type: cross Abstract: Repository-level code generation requires implementing target functions while accounting for complex cross-file dependencies and project-specific conv
arXiv:2607.08399v1 Announce Type: new Abstract: Large language models process prompts by propagating activations through dozens of layers before generating a response. We ask whether the task-relevant
arXiv:2607.08162v1 Announce Type: cross Abstract: Whole slide images (WSIs) provide rich diagnostic information for computational pathology, but their gigapixel scale, stain variation, scanner differe
arXiv:2602.23785v2 Announce Type: replace Abstract: We investigate the identifiability of nonlinear canonical correlation analysis (CCA) in a multi-view setup, in which each view is generated by apply
arXiv:2607.08012v1 Announce Type: cross Abstract: This paper studies an online variant of the assistance games framework, where an informed agent and an uninformed agent repeatedly interact over T tim
arXiv:2607.08111v1 Announce Type: cross Abstract: Training target speaker extraction (TSE) models for real conversational mixtures remains challenging because large-scale training corpora and clean ta
arXiv:2607.08285v1 Announce Type: new Abstract: Current AI evaluation frameworks focus primarily on technical performance, including accuracy, robustness, reasoning ability, and policy compliance. The
arXiv:2607.08045v1 Announce Type: cross Abstract: Angular radio maps describe the received-power distribution over the angle of arrival and underpin beam selection and receiver localization in sixth-g
arXiv:2607.08115v1 Announce Type: new Abstract: While global localization using spinning radar has gained attention for its robustness to adverse weather and challenging environments, many studies hav
arXiv:2607.08003v1 Announce Type: cross Abstract: Catalysts are essential for sustainable chemical manufacturing, yet discovering novel architectures remains a bottleneck dominated by trial-and-error
arXiv:2202.13142v3 Announce Type: replace Abstract: A key challenge of real-world image super-resolution (SR) is to recover the missing details in low-resolution (LR) images with complex unknown degra
arXiv:2607.07719v1 Announce Type: cross Abstract: Parameter-efficient fine-tuning adapts a large language model to one task cheaply, but across a task sequence LoRA-style methods keep stacking low-ran
arXiv:2607.08056v1 Announce Type: cross Abstract: Diffusion Language Models (DLMs) have recently achieved substantial progress in natural language generation tasks. Recent research demonstrates that a
arXiv:2607.08716v1 Announce Type: new Abstract: In long-horizon tasks, decision-relevant state is often scattered across an expanding trajectory, while the action agent must surface it and act. As tra
arXiv:2607.08665v1 Announce Type: new Abstract: Routing among large language models (LLMs) trades response quality against serving cost, motivated by the reported gap between deployed routers and a pe
arXiv:2601.09071v2 Announce Type: replace Abstract: The existence of multiple, equally accurate models for a given predictive task leads to predictive multiplicity, where a Rashomon set of models achi
arXiv:2603.16453v3 Announce Type: replace Abstract: Large language model (LLM) agents have made rapid progress on short-horizon, well-scoped tasks, yet their ability to sustain coherent decisions in d
arXiv:2601.22588v2 Announce Type: replace-cross Abstract: Large language models (LLMs) are widely used as reference-free evaluators via prompting, but this 'LLM-as-a-Judge' paradigm is costly, opaque,