Knowledge catalogue
arXiv:2610.05604v1 Announce Type: new Abstract: This paper reports on the challenges encountered and lessons learned during the development and deployment of a flexible multi-sensor platform for auton
arXiv:2610.05332v1 Announce Type: new Abstract: This paper tackles visible challenges in deep ensemble learning, where deep neural networks serve as ensemble members: training and storage burdens, and
arXiv:2610.04775v1 Announce Type: new Abstract: In this paper, we present a distributed cascade force control system (DCFC) for multiple robots with the aim of pushing a rigid object towards a desired
arXiv:2610.06078v1 Announce Type: new Abstract: This paper asks whether VLA generalization is grounded in a global understanding of objects' physical properties that enables policies to adapt their mo
arXiv:2610.05607v1 Announce Type: new Abstract: Emergency obstacle avoidance requires a mobile robot to brake or change its heading within the available distance. This paper investigates the dependenc
arXiv:2604.26654v1 Announce Type: cross Abstract: This paper presents an application of the biologically realistic JASTAP neural network model to classification tasks. The JASTAP neural network model
arXiv:2610.05869v1 Announce Type: cross Abstract: This paper studies online quantile regression for large-scale and streaming data using Stochastic SubGradient Descent (SSGD) with constant learning ra
arXiv:2510.14242v2 Announce Type: replace-cross Abstract: Large Language Models (LLMs) often produce inconsistent answers when faced with different phrasings of the same prompt. In this paper, we prop
arXiv:2610.05757v1 Announce Type: new Abstract: This paper introduces Hybrid DeepSEE (HDS), a Human-in-the-Loop (HITL) neuro-symbolic framework for proactive drift anticipation in Visual SLAM (V-SLAM)
arXiv:2610.05250v1 Announce Type: new Abstract: Kolmogorov--Arnold Networks (KANs) have attracted increasing attention in recent years, with applications across a wide range of AI tasks. In this paper
arXiv:2610.05915v1 Announce Type: new Abstract: This paper revisits the distributed learning problem for training a multinomial logistic regression model with the Federated Averaging (exttt{FedAvg}) a
arXiv:2503.09950v2 Announce Type: replace Abstract: In this paper, we address the problem of human trajectory forecasting, which aims to predict the inherently multi-modal future movements of humans b
arXiv:2610.04545v1 Announce Type: cross Abstract: This paper presents a nonlinear extension of Density-Driven Optimal Control (D2OC) for multi-agent spatial coverage with prescribed density distributi
arXiv:2610.04067v1 Announce Type: new Abstract: In this paper, we unravel the effect of different decoder architectures on the interpretability of the latent space of the Physically Recurrent Neural N
arXiv:2610.04765v1 Announce Type: new Abstract: In this paper, we develop a method that enables bimanual dexterous hands to manipulate articulated objects with a high success rate without suffering fr
arXiv:1908.04284v5 Announce Type: replace-cross Abstract: In this paper, we propose 'personal VAD', a system to detect the voice activity of a target speaker at the frame level. This system is useful
arXiv:2610.04928v1 Announce Type: new Abstract: This paper studies GRPO at 1B scale from both directions: what estimator choices do to the learning signal, and what a degraded reward signal does to wh
arXiv:2610.04299v1 Announce Type: new Abstract: Self-evolving reasoning models learn from their own generated questions, yet repeated self-training can lead to performance collapse. In this paper, we
arXiv:2603.05925v2 Announce Type: replace Abstract: In this paper, we propose a Rectified Flow Auto Coder (RAC) inspired by Rectified Flow to replace the traditional VAE: 1. It achieves multi-step dec
arXiv:2610.04266v1 Announce Type: new Abstract: This paper presents a conformal-seeded hybrid strategy for solving inverse kinematics of offset, redundant 7-DoF robot arms of the humanoid class. Analy
arXiv:2410.18275v2 Announce Type: replace Abstract: In this paper, we study the problem of methodically obtaining a sufficient set of kinesthetic demonstrations, one at a time, such that a robot can b
arXiv:2610.04130v1 Announce Type: cross Abstract: In this paper we investigate whether geometrically aligned astronomical representations are also scientifically interchangeable. We analyze every laye
arXiv:2610.03931v1 Announce Type: new Abstract: This paper presents sparse calibration-based personalization for kernel-based gait phase and walking speed co-estimation from wearable inertial measurem
arXiv:2610.05939v1 Announce Type: cross Abstract: This paper establishes the first general structural solution to the problem of state estimation for nonlinear systems driven by unknown inputs. Buildi
arXiv:2610.05270v1 Announce Type: cross Abstract: This paper investigates the real-time dispatch of a shared drone fleet for on-demand food delivery and urban road network monitoring. We consider a co
arXiv:2008.06006v5 Announce Type: replace-cross Abstract: In this paper, we propose Textual Echo Cancellation (TEC) - a framework for cancelling the text-to-speech (TTS) playback echo from overlapping
arXiv:2610.05756v1 Announce Type: new Abstract: In the rapidly evolving field of construction management, worker safety remains a top priority. This paper introduces an innovative vision-based system
arXiv:2610.02673v1 Announce Type: cross Abstract: A theory draws many independent observations into one framework with novel hypotheses. A researcher building such a theory must synthesize observation
arXiv:2509.20239v2 Announce Type: replace-cross Abstract: This paper investigates theoretical and methodological foundations for stochastic optimal control (SOC) in discrete time. We start formulating
arXiv:2610.03016v1 Announce Type: cross Abstract: In this paper, we propose a Role-aware Stimulus-guided (RASG) framework for interlocutor emotion recognition, which predicts listener emotions from li
arXiv:2610.03717v1 Announce Type: cross Abstract: This paper examines the role of Novel View Synthesis (NVS) in geometric representation learning. In principle, NVS should reason about 3D scene struct
arXiv:2610.03222v1 Announce Type: cross Abstract: This paper studies the complexity of convex optimization using lazy second-order oracles (Doikov, Chayti, and Jaggi, ICML 2023), where an algorithm qu
arXiv:2610.03195v1 Announce Type: new Abstract: As LLM agents decide on users' behalf which product to buy, which hotel to book, or which paper to cite, a preference for items from certain sources (th
arXiv:2610.02779v1 Announce Type: new Abstract: In this paper, we present trajectory-aware reuse and adaptive correction (TRAC), a training-free framework for efficient autoregressive (AR) video gener
arXiv:2307.07113v2 Announce Type: replace-cross Abstract: In this paper, we consider the decentralized, stochastic nonconvex strongly-concave (NCSC) minimax problem with nonsmooth regularization terms
In our #NeurIPS2026 paper “A Minimal Interpretable Architecture for Zero-Shot Reconstruction of Dynamical Systems (DS)” (preprint: https://arxiv.org/abs/2607.14937) we reduce a DS foundation model to
arXiv:2602.14363v2 Announce Type: replace-cross Abstract: This paper presents Adaptive Whole-body Loco-Manipulation, AdaptManip, a fully autonomous framework for humanoid robots to perform integrated
arXiv:2610.00533v1 Announce Type: cross Abstract: This paper addresses the control of multi-input strict-feedback nonlinear systems subject to a joint capacity constraint, in which the admissible inpu
arXiv:2610.01326v1 Announce Type: new Abstract: This paper presents the European Crisis Management Ontology (ECMO), a modular OWL-based ontology intended as a cross-sectoral reference for disaster ris
arXiv:2507.12091v2 Announce Type: replace-cross Abstract: This paper presents an improved analysis for sign-based methods with momentum updates. Traditional sign-based methods obtain a convergence rat
arXiv:2610.00563v1 Announce Type: cross Abstract: This paper presents a preliminary study of an alternative to the affine transformation underlying conventional neural-network layers. In the proposed
arXiv:2610.00619v1 Announce Type: cross Abstract: In this paper, we extend earlier findings of supra-competitive outcomes in optimal-execution games by identifying a learned punitive mechanism that de
arXiv:2610.01141v1 Announce Type: cross Abstract: Morohoshi, Nakayama, Manabe, and Mitarai proposed a physically motivated quantum machine learning problem in which the goal is to predict quantities o
arXiv:2610.00208v1 Announce Type: cross Abstract: This paper studies decentralized safe path following for multiple quadrotors on intersecting paths, where safety requires both collision avoidance and
arXiv:2610.00136v1 Announce Type: cross Abstract: This paper presents a reproducible, educational study of evasion attacks in image classification and text classification. A compact convolutional netw
arXiv:2610.00576v1 Announce Type: new Abstract: In this paper, we propose Gestalt, a new paradigm of large multimodal model built around multimodal interplay. Despite rapid advances, large multimodal
arXiv:2601.22169v2 Announce Type: replace-cross Abstract: Humans are susceptible to undesirable behaviours and privacy leaks under the influence of alcohol. This paper investigates drunk language, i.e
arXiv:2610.00864v1 Announce Type: cross Abstract: In this paper, we study how to achieve one-step action generation in Robotic Foundation Models (RFMs), aiming to overcome the high inference latency o
arXiv:2610.01573v1 Announce Type: new Abstract: This paper presents a guidance algorithm for micro aerial vehicles operating in unknown, cluttered environments using only onboard sensing. The method i
arXiv:2404.17569v4 Announce Type: replace Abstract: This paper aims to generate materials for 3D meshes from text descriptions. Unlike existing methods that synthesize texture maps, we propose to gene
arXiv:2610.00705v1 Announce Type: new Abstract: This paper develops a meta-multi-agent reinforcement learning (meta-MARL) framework to enable fast adaptation of interactive policies in a multi-agent s
More agents don't mean higher performance. There is a coordination bottleneck to consider. Not to mention the unnecessary costs. So how many of a multi-agent team's actions actually help it finish the
arXiv:2509.11337v2 Announce Type: replace Abstract: Adversarial training has been widely studied in recent years due to its role in improving model robustness against adversarial attacks. This paper f
arXiv:2610.01134v1 Announce Type: new Abstract: An optical character recognition (OCR) can scan a paper and extract text using technology, making people's jobs easier. While various OCR systems are av
arXiv:2610.01572v1 Announce Type: cross Abstract: This paper investigates stochastic multi-level optimization where the objective is a nested composition of several smooth non-convex functions. We ass
arXiv:2607.17384v3 Announce Type: replace Abstract: This paper provides an experimentally verified formal law for calculating the uplift that diversity of thought provides in Large Language Model (LLM
arXiv:2610.00729v1 Announce Type: new Abstract: This paper explores a reward-based policy to achieve zero-shot transfer between source and target environments with completely different observation spa
arXiv:2610.01004v1 Announce Type: cross Abstract: In this paper, we explore the impulsive dynamics common to single-joint, two-link models of walking and brachiating gaits with respect to slope and sw
arXiv:2610.01097v1 Announce Type: new Abstract: End-to-end research agents can now produce complete scientific papers, yet manuscript claims often diverge from executed experiments. This gap is struct
arXiv:2609.39027v1 Announce Type: new Abstract: AI reviewers can assign different judgments to manuscripts that report the same science in different wording, potentially rewarding rhetorical optimizat