Search: “papers”

SoftSEEPS improves ML-based precipitation forecasting

arXiv:2610.09752v1 Announce Type: new Abstract: In this paper we have developed a differentiable approximation of the well-known SEEPS score, which we name SoftSEEPS. This allows the training of a Mac

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Stationary Bias and Extrapolation in Nonlinear Two-Timescale Stochastic Approximation

DGX agent

arXiv:2610.10246v1 Announce Type: new Abstract: Constant-step stochastic approximation generally has a nonzero stationary mean error that persists under time averaging. This paper studies that error f

safetyarxiv-cs-lg

8 Oct 2026

The IroyinSpeech Text Corpus: 24,905 Curated Yoruba Sentences for Speech and Language Technology

arXiv:2610.05366v2 Announce Type: replace Abstract: IroyinSpeech is a 42-hour, 80-speaker Yoruba read-speech corpus whose audio has been distributed by ELRA since 2024. This paper describes the releas

researcharxiv-cs-cl

Tracing Inputs, Verifying Outputs: Validating Attribution in Music Generation

arXiv:2610.09637v1 Announce Type: cross Abstract: How can we verify whose music contributed to an AI-generated output? This paper demonstrates how input-based attribution can provide verifiable eviden

Agents

TRACK: Telemetry-Based Racing Analysis and Coaching Kit in Sim Racing Games

arXiv:2610.10061v1 Announce Type: cross Abstract: This paper presents TRACK (Telemetry-Based Racing Analysis and Coaching Kit), which is a framework for analyzing driving performance in sim racing and

agentsarxiv-cs-lg

Video-to-Model: Automatic Modeling of Deformable Linear Objects

model-releasesarxiv-cs-ro

arXiv:2610.10039v1 Announce Type: new Abstract: This paper presents a video-to-model framework for automatically modeling the motion of a deformable suture thread from an input video. We utilize a rec

Local Ai

WAPR: A Foundation Model for Wide-Angle Refinement in Unseen Object Pose Estimation

arXiv:2610.09535v1 Announce Type: new Abstract: Real-world applications require 6D pose estimation to be accurate, fast, and scalable to unseen objects. This paper introduces WAPR, a zero-shot wide-an

local-aiarxiv-cs-cv

Whatever happened to BABA is AI from 2024? [D]

model-releasesr-machinelearning

We test three state-of-the-art multi-modal large language models (GPT-4o, Gemini-1.5-Pro, Gemini-1.5-Flash) and find that they fail dramatically when generalization requires that the rules of the game

Adaptive Model Inversion Attacks Generalize a Privacy-Robustness Tradeoff

arXiv:2610.07677v1 Announce Type: new Abstract: In this paper, we show that standard evaluations of high-resolution Model Inversion Attacks (MIAs) significantly underestimate training-data privacy lea

Anchor Divergence for Semantic Geometry in Contrastive Learning

arXiv:2610.06919v1 Announce Type: new Abstract: This paper concerns how semantic context determines geometry in learned vector representations. Similarity is typically measured using cosine similarity

Comparative review of hybrid forecasting models for short-term prediction of building thermal load

arXiv:2610.06881v1 Announce Type: cross Abstract: In this paper, a comparative review of different hybrid models for short-term forecasting of building thermal demand is carried out. Particularly, the

Diffusion Model-Based Video Editing: A Survey

arXiv:2407.07111v2 Announce Type: replace-cross Abstract: The rapid development of diffusion models (DMs) has significantly advanced image and video applications, making 'what you want is what you see

Efficient Gaussian Splatting Sequence Compression with Standard Video Codecs

arXiv:2610.07795v1 Announce Type: new Abstract: This paper presents a novel effective Gaussian Splatting (GS) sequence Compression method that utilizes the Video codec (GSCV). Existing video-based GS

Energy-Conditioned Noise Schedule and Whitening for Spectral Diffusion

arXiv:2610.07206v1 Announce Type: new Abstract: This paper introduces an energy-adaptive noise scheduling and whitening strategy for transform-domain diffusion models. Existing spectral diffusion meth

Event Detection in Table Tennis Videos using 2D Keypoints

arXiv:2610.08286v1 Announce Type: new Abstract: This paper addresses the challenge of automatic, frame-accurate event detection in table tennis videos. Current methods for estimating 3d ball trajector

Hardware

Feature Space Analysis by Guided Diffusion Model

arXiv:2509.07936v3 Announce Type: replace Abstract: This paper aims to analyse the feature space of a vision-related Deep Neural Network (DNN) by proposing a decoder that can generate an image whose f

hardwarearxiv-cs-cv

HINTT Submission to the 2nd MLC-SLM Challenge: Comparing Cascaded and Unified Approaches to Diarization and ASR

arXiv:2610.08063v1 Announce Type: cross Abstract: This paper presents the HINTT system submitted to the 2nd Challenge and Workshop on Multilingual Conversational Speech Language Model (MLC-SLM). We ad

researcharxiv-cs-cl

Micro Neural Policies for Safe Real-Time Robotic Control

arXiv:2610.08541v1 Announce Type: cross Abstract: In this paper, we investigate the synthesis of Micro Neural Policies (MNP) to enable safe and robust real-time robotic control on computationally cons

safetyarxiv-cs-ai

Modeling Latent Disturbances for Robust Decision-Making in World Models

arXiv:2610.07599v1 Announce Type: cross Abstract: In this paper, we study robust decision-making in the latent space of world models (WMs). Robust optimization is a mathematical framework where, given

safetyarxiv-cs-ai

Modeling Time-Dependent Responses of Optical Compressors with Selective State Space Models

arXiv:2408.12549v4 Announce Type: replace-cross Abstract: This paper presents a method for modeling optical dynamic range compressors using deep neural networks with Selective State Space models. The

Near-Optimal Sample Complexity for Recursive Entropic Risk Reinforcement Learning with a Generative Model

model-releasesarxiv-cs-lg

arXiv:2610.06931v1 Announce Type: new Abstract: In this paper, we study the sample complexities of value and policy learning in finite discounted Markov decision processes (MDPs) under recursive entro

Applications

RMRRT: Riemannian Barrier Metric RRT for Inequality-Aware Steering on Equality Manifolds

arXiv:2610.06863v1 Announce Type: new Abstract: This paper presents a motion planning framework that unifies equality and inequality constraints within a single geometric formulation for sampling-base

applicationsarxiv-cs-ro

Applications

Robust Nonprehensile Object Transport with Quadruped Robots

arXiv:2610.07245v1 Announce Type: new Abstract: In this paper, we present a robust nonprehensile object transportation framework for quadruped robots. An uncertainty-aware trajectory optimization meth

applicationsarxiv-cs-ro

Safe Multi-Robot Collaborative Transport Using Density Functions

arXiv:2610.08441v1 Announce Type: new Abstract: This paper presents a hierarchical density-based model predictive control framework for safe collaborative manipulation by multiple quadrupedal robots.

Scaling Laws for Deepfake Detection

arXiv:2510.16320v2 Announce Type: replace Abstract: This paper presents a systematic study of scaling laws for the deepfake detection task. Specifically, we analyze the model performance against the n

SENSE: State-aware Emotion Navigation Storytelling Engine

arXiv:2610.07666v1 Announce Type: cross Abstract: This paper presents SENSE, a state-aware framework for generating playable branching visual novels with multi-track emotional navigation. Integrating

Thin Evidence, Thick Priors: How Language Models Substitute Identity for Missing Financial Facts

model-releasesarxiv-cs-ai

arXiv:2610.07798v1 Announce Type: new Abstract: People increasingly ask large language models what to do with their money, yet seldom describe their finances in full. This paper asks what a model does

Trajectools Demo: Towards No-Code Solutions for Movement Data Analytics

arXiv:2610.06858v1 Announce Type: cross Abstract: This demo paper presents the conceptual foundations and the first steps towards implementation of a novel no-code solution for movement data analytics

VALSE: Vertical Adaptive Layer Skipping for Efficient Inference in Large Language Models

arXiv:2610.07606v1 Announce Type: new Abstract: This paper establishes a theoretical framework for vertical adaptive layer skipping, proving three foundational results: (i) an Expected FLOPs formula (

Who Wrote It Is Not Enough: Detecting Who Contributed the Insight

model-releasesarxiv-cs-cl

arXiv:2610.07365v1 Announce Type: new Abstract: As LLMs increasingly assist scientific writing and peer review, detecting who wrote the text is no longer sufficient: we need to determine who contribut

Any-scale Object Detection using Arbitrary-scaled Images

arXiv:2610.04346v1 Announce Type: new Abstract: This paper proposes any-scale object detection using arbitrary-scale super-resolution for continuously rescaling object images, while general multi-scal

Application of sequence learning for predicting radiation damage of the CMS electromagnetic calorimeter

arXiv:2610.04058v1 Announce Type: cross Abstract: In this paper we use machine learning methods to predict radiation damage in the lead tungstate crystals of the CMS electromagnetic calorimeter at the

Barrier-Shaped Recurrent Reinforcement Learning for Autonomous Landing on a Heaving Ship Deck

arXiv:2610.03924v1 Announce Type: cross Abstract: This paper addresses autonomous landing of unmanned aerial vehicle (UAV) rotorcrafts on a heaving ship deck using a recurrent policy trained via asymm

safetyarxiv-cs-ro

Base Models Can Reason By Taking a Cue From Training Data

arXiv:2610.06851v1 Announce Type: new Abstract: In this paper, we study how training data creates associations between the tokens at the start of a base model's response and the reasoning behavior tha

Billion-Scale Thumbnail Optimization for Uncurated Short-Form Videos via Multi-Armed Bandits

arXiv:2610.04931v1 Announce Type: new Abstract: This paper introduces a real-time thumbnail optimization system deployed at a global O(B) scale on a major short-form video platform. Unlike traditional

Boosting Transferable Adversarial Attacks against Deep Reinforcement Learning

arXiv:2610.06083v1 Announce Type: new Abstract: Most adversarial attacks on deep reinforcement learning (DRL) assume white-box access to the victim policy, which rarely holds in practice. This paper s

Agents

Building A Multi-Sensor Platform For Autonomous Driving Research: Challenges and Lessons Learned

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

agentsarxiv-cs-ro

Compact set-valued deep ensembling in multi-class classification

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

Distributed Cascade Force Control of Soft-Tactile-Based Multi-robot System for Object Transportation

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

Do VLAs Understand and Adapt to the Objects They Handle, or Simply Replay Learned Behaviors?

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

safetyarxiv-cs-ro

Emergency Obstacle Avoidance Maneuvers in Differential-Drive Mobile Robots

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

Evolutionary feature selection for spiking neural network pattern classifiers

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

Finite-Sample Distribution Theory and Efficient Large-Scale Inference for Online Quantile Regression

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

Flip-Flop Consistency: Unsupervised Training for Robustness to Prompt Perturbations in LLMs

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

Human-in-the-Loop Neuro-Symbolic Drift Anticipation for Reliable Visual SLAM

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)

Kolmogorov-Arnold Networks for Personal Context Recognition on ExtraSensory

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

Large Stepsizes Federated Learning on Logistic Regression with Linearly Separable Data: The Case of Heterogeneous Devices

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

Applications

MoFlow: One-Step Flow Matching for Human Trajectory Forecasting via Implicit Maximum Likelihood Estimation based Distillation

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

applicationsarxiv-cs-cv