Knowledge catalogue
arXiv:2607.03131v1 Announce Type: cross Abstract: Modern video surveillance systems generate far more video streams than human operators can effectively monitor, making automated analysis essential fo
arXiv:2607.05222v1 Announce Type: new Abstract: Charts and images appear together throughout scientific publications, yet most computational work does not characterize their coherence. We argue that a
arXiv:2607.03503v1 Announce Type: new Abstract: Graph-based semi-supervised learning (SSL) propagates a few labels over a similarity graph by minimizing a Dirichlet-type energy. The standard quadratic
arXiv:2607.04518v1 Announce Type: new Abstract: Gait is a distinctive behavioral characteristic that enables non-invasive individual identification without requiring physical interaction with an anima
arXiv:2607.04367v1 Announce Type: new Abstract: In the development of cooking robots, mastering the task of cutting is crucial. A significant challenge lies in the diverse properties of food, which ne
arXiv:2607.04680v1 Announce Type: new Abstract: Grain growth is governed by the reduction in grain boundary energy and exhibits well-established statistical scaling laws. Developing data-driven surrog
arXiv:2607.04056v1 Announce Type: cross Abstract: Modern supply chains span diverse operational environments, ranging from e-commerce distribution networks to customized production-to-order manufactur
arXiv:2607.02944v1 Announce Type: cross Abstract: We propose PRECEDE, a precedent-guided co-scientist for side-effect-aware drug redesign that revises a parent compound to mitigate a specified side ef
arXiv:2607.02782v1 Announce Type: cross Abstract: Predictions by machine learning (ML) and artificial intelligence (AI) models are often received skeptically unless they are paired with intelligible e
arXiv:2511.07109v2 Announce Type: replace-cross Abstract: Nonnegative matrix factorization (NMF) is a linear dimensionality reduction technique for nonnegative data, with applications such as hyperspe
arXiv:2602.02908v2 Announce Type: replace-cross Abstract: Diffusion models trained on different, non-overlapping subsets of a dataset often produce strikingly similar outputs when given the same noise
A recent NYT article by @CadeMetz [1] claims that neural network distillation was first developed in 2015 by a team of Google researchers including G. Hinton [2]. Not true! I published the technique i
arXiv:2607.04689v1 Announce Type: cross Abstract: Safe operation of autonomous vehicles in dense urban traffic depends on perception and planning that remain reliable when onboard sensing is degraded.
arXiv:2607.04436v1 Announce Type: cross Abstract: Natural language requirements (NLRs) are essential for bridging communication gaps among diverse stakeholders in software development. However, the in
arXiv:2603.00819v2 Announce Type: replace-cross Abstract: This paper surveys recent developments at the intersection of operator learning, statistical learning theory, and approximation theory. First,
arXiv:2607.03815v1 Announce Type: cross Abstract: For compact convex sets L,K subset R^n, denote by lambda_K(L) the smallest size of a homothet of K that contains L. We define a measure of symmetry ba
arXiv:2408.05124v2 Announce Type: replace-cross Abstract: Image sensors are fundamental to many intelligent systems, allowing visual perception and AI-driven decision-making. However, their integrity
arXiv:2607.02941v1 Announce Type: new Abstract: Multi-product kitting delivery imposes significant challenges for real-time scheduling in hybrid manufacturing systems that integrate processing and ass
arXiv:2607.02753v1 Announce Type: cross Abstract: Neuromorphic controllers for size, weight, and power-constrained systems require neural architectures that are both energy-efficient and interpretable
arXiv:2607.03600v1 Announce Type: cross Abstract: Adversarial robustness in Unsupervised Domain Adaptation (UDA) remains a significant challenge due to noisy pseudo labels and inherent distributional
arXiv:2607.03664v1 Announce Type: new Abstract: The Gaussian Error Linear Unit is usually motivated as the expected output of an input-dependent stochastic Bernoulli gate. This work gives a complement
arXiv:2503.23650v2 Announce Type: replace Abstract: Reinforcement learning (RL), with its ability to explore and optimize policies in complex, dynamic decision-making tasks, has emerged as a promising
arXiv:2509.08269v5 Announce Type: replace-cross Abstract: Large language models (LLMs) are increasingly integrated with evolutionary computation to support optimization tasks. This survey primarily fo
arXiv:2507.04136v2 Announce Type: replace Abstract: This survey offers a comprehensive foundation on the integration of RL with language models, highlighting prominent algorithms such as Proximal Poli
arXiv:2204.02803v2 Announce Type: replace-cross Abstract: Sign language recognition from monocular video or 2D pose sequences is challenging, both because 3D information must be inferred from 2D obser
arXiv:2607.04028v1 Announce Type: cross Abstract: We propose a unified algebraic framework for classification performance evaluation that encompasses binary, multiclass, multilabel, ordinal, hierarchi
arXiv:2606.16933v2 Announce Type: replace-cross Abstract: Reinforcement learning (RL) systems often degrade when operating conditions differ from those previously encountered, reflecting distributiona
arXiv:2607.04081v1 Announce Type: new Abstract: In-context learning (ICL) has emerged as a central capability of pretrained language models, yet its theoretical analysis has focused primarily on causa
arXiv:2607.03860v1 Announce Type: new Abstract: The Strong Lottery Ticket Hypothesis (SLTH) asserts that sufficiently overparameterized, randomly initialized neural networks contain sparse subnetworks
arXiv:2602.19107v3 Announce Type: replace Abstract: Robotaxis are emerging as a promising form of urban mobility, but removing human drivers fundamentally reshapes passenger-vehicle interaction and ra
arXiv:2607.03621v1 Announce Type: new Abstract: This paper presents the development and evaluation of a collaborative system for real-time reconstruction of fragmented paper documents in the context o
arXiv:2607.04162v1 Announce Type: cross Abstract: Open-ended tabletop manipulation requires agents to not only understand natural language but also adapt to dynamic environments and execution failures
arXiv:2607.04426v1 Announce Type: new Abstract: Embodied AI is moving from isolated perception or action modules toward physical agents that understand, plan under goals, act through robot bodies, mon
arXiv:2607.03126v1 Announce Type: cross Abstract: Reinforcement Learning (RL) has substantially improved the reasoning ability of large language models (LLMs), but sparse outcome rewards still make to
arXiv:2510.01038v2 Announce Type: replace Abstract: Perturbation-based explainability methods face criticism due to their reliance on out-of-distribution mutants. This raises doubts about the quality
arXiv:2607.04869v1 Announce Type: new Abstract: Motivated by real-world scenarios where malicious entities tamper with existing networks, we define a model where an adversary seeks to hide a set of co
arXiv:2603.08457v2 Announce Type: replace-cross Abstract: Robust single-vessel tracking from fixed coastal platforms is hindered by modality-specific degradations: cameras suffer from illumination and
arXiv:2607.05272v1 Announce Type: cross Abstract: Inference serving systems must balance throughput and latency under bursty, heterogeneous workloads, yet the industry standard remains static batching
arXiv:2607.03304v1 Announce Type: cross Abstract: Reliable analysis of bird vocalisations in passive acoustic monitoring requires models handling multiple, imbalanced annotation targets. We extend Bir
arXiv:2509.22851v4 Announce Type: replace-cross Abstract: Margin-based optimization is fundamental to improving generalization and robustness in classification tasks. In the context of reward model le
arXiv:2512.14991v2 Announce Type: replace Abstract: We study reinforcement learning for controlled diffusion processes with unbounded continuous state spaces, bounded continuous actions, and polynomia
arXiv:2507.23033v2 Announce Type: replace Abstract: Spiking Neural Networks (SNNs) provide an energy-efficient computing paradigm for neural rendering, but existing spike-based Neural Radiance Field (
arXiv:2607.03876v1 Announce Type: new Abstract: With the rise of small quantized GGUF-based language models and their increasing use for on-device inference tasks, we have seen the growing need for an
arXiv:2607.04256v1 Announce Type: new Abstract: Current feed-forward 3D reconstruction methods predict pixel aligned Gaussian primitives, resulting in highly redundant representations. A natural solut
arXiv:2607.02572v1 Announce Type: cross Abstract: In multi-source image fusion scenarios, heterogeneous inputs are typically driven by distinct generative mechanisms and can be viewed as a composition
arXiv:2607.03454v1 Announce Type: cross Abstract: In this paper, we propose Adversarial Dynamics Priors (ADP) for perturbation-resilient humanoid locomotion control. Existing motion prior-based method
arXiv:2607.03887v1 Announce Type: cross Abstract: The increasing complexity and frequency of software vulnerabilities demand efficient methods to analyze and prioritize threats. Traditional approaches
arXiv:2607.05280v1 Announce Type: new Abstract: Many real-world systems evolve continuously, yet most machine learning models interpret time series as discrete sequences. Continuous-time approaches in
arXiv:2607.03839v1 Announce Type: new Abstract: Sparse feature selection is critical for high-dimensional machine learning, yet traditional ell_1-regularized methods are often brittle under observatio
arXiv:2607.04719v1 Announce Type: new Abstract: Aerial robots are increasingly moving from remote observation toward physical interaction with objects, surfaces, structures, loads, and surrounding flo
arXiv:2607.05120v1 Announce Type: cross Abstract: AI agents act on behalf of user prompts, consuming external data and taking actions based on the agent context. Prior research on AI agent security ha
arXiv:2607.04331v1 Announce Type: cross Abstract: Evaluating autonomous driving systems in closed-loop settings requires realistic and interactive simulation, yet existing simulators largely rely on l
arXiv:2607.03702v1 Announce Type: new Abstract: Large language model (LLM) agents have shown strong decision-making capabilities in long-horizon interactive tasks, yet they still struggle to effective
arXiv:2607.04419v1 Announce Type: new Abstract: Most agent evaluations collapse a multi-step trace into a final answer, a success flag, or a trajectory-level score. These aggregates obscure the diagno
arXiv:2603.23115v2 Announce Type: replace Abstract: The realism of AI-generated images (AIGI) poses increasing challenges for reliable forensic detection, where heterogeneous expert detectors may prod
arXiv:2607.05174v1 Announce Type: new Abstract: Language agents, i.e., LLM agents, progress rapidly and are increasingly deployed in production environments. This trend underscores the urgent need for
arXiv:2602.24115v2 Announce Type: replace Abstract: Open RAN (O-RAN) exposes rich control and telemetry interfaces across the Non-RT RIC, Near-RT RIC, and distributed units, but also makes it harder t
arXiv:2607.03233v1 Announce Type: cross Abstract: The rapid growth of publicly available digital information has rendered manual open-source intelligence (OSINT) analysis insufficient for modern intel
arXiv:2509.17255v2 Announce Type: replace-cross Abstract: We present the first language-model-driven agentic artificial intelligence (AI) system to autonomously execute multi-stage physics experiments
arXiv:2607.04219v1 Announce Type: new Abstract: The integration of AI into Internet of Things (AIoT) systems has gradually transformed them from passive data collection infrastructures into intelligen