Search: “benchmark”

FOCUS: Fine-Grained Open-Vocabulary Change Detection for Uncertainty-Aware Semi-Static Scenes

model-releasesarxiv-cs-ro

arXiv:2610.05639v1 Announce Type: new Abstract: Autonomous robots operating over long periods must keep their environmental memory up to date as the world changes between visits. In semi-static enviro

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Tutorials

Frequency-Decoupled Diffusion Guidance for Non-Blind Image Deblurring

DGX agent

arXiv:2610.06221v1 Announce Type: new Abstract: Pretrained diffusion models provide powerful image priors for training-free posterior sampling in image restoration. To guide this sampling process, fre

tutorialsarxiv-cs-cv

6 Oct 2026

Applications

From Abusive Language Classification to Sequence Labeling Identification

arXiv:2610.06287v1 Announce Type: cross Abstract: Industrial content moderation must process massive message streams under tight latency constraints, yet most abusive language (AL) detection systems r

applicationsarxiv-cs-lg

From Transformation to Target State: Rethinking Query Representation for Zero-Shot Composed Image Retrieval

arXiv:2610.05993v1 Announce Type: new Abstract: Composed image retrieval (CIR) aims to retrieve a desired target image from a query consisting of a reference image and a modification text. This task e

Frozen in a Frame: The Velocity Blind Spot in JEPA World Models

arXiv:2610.04585v1 Announce Type: new Abstract: Joint-embedding predictive architectures (JEPAs) for world modeling train an encoder so a predictor maps a current embedding and action to the next fram

Gated Graph Neural Networks for Learning Hidden Independent Cascade Dynamics

arXiv:2610.04033v1 Announce Type: cross Abstract: Information and infectious diseases spread through social networks, but the spreading probabilities driving them are hard to estimate without per-node

Gaussian Flow Dynamics: Simulation-Free Neural SDE Learning Beyond One-Time Marginals

arXiv:2610.04390v1 Announce Type: cross Abstract: Simulation-free training of latent Stochastic Differential Equations (SDEs) relies on a variational posterior process whose one-time marginals are tra

GeoFunFlow: Geometric function flow matching for joint probabilistic inference of physical fields and complex geometries

arXiv:2509.24117v2 Announce Type: replace Abstract: Inverse problems governed by partial differential equations (PDEs) arise widely in science and engineering, but are often ill-posed and limited by s

tutorialsarxiv-cs-lg

Geometry-Aware Preference Optimization for Text-to-Image Diffusion Models

arXiv:2610.04980v1 Announce Type: new Abstract: Preference alignment has become a standard practice for text-to-image diffusion models. Direct Preference Optimization (DPO) simplifies this process by

GPU-Accelerated Bregman Douglas-Rachford Splitting for Discrete Optimal Transport

arXiv:2610.04715v1 Announce Type: cross Abstract: We present GPU-accelerated Bregman Douglas--Rachford splitting algorithm (BDRS) for discrete optimal transport problem in three input formats: an expl

GR-LIO: A Local Ground-Aware LiDAR-Inertial Odometry System Using Body-to-Ground Height

model-releasesarxiv-cs-ro

arXiv:2610.05546v1 Announce Type: new Abstract: LiDAR-inertial odometry (LIO) is widely used for state estimation in ground-based autonomous mobile robots. However, the geometric constraints provided

Graph-Based Floor Separation Using Node Embeddings and Clustering of WiFi Trajectories

arXiv:2505.08088v4 Announce Type: replace-cross Abstract: Vertical localization, particularly floor separation, remains a major challenge in indoor positioning systems operating in GPS-denied multisto

Hamiltonian Metric Learning and Energy-Based Training: A Dissipative Geometric Framework for Optimization

arXiv:2610.04969v1 Announce Type: new Abstract: Optimization in machine learning is usually expressed through iterative rules that update model parameters using information from the loss landscape. In

Hierarchical Credit Assignment for RLVR on Fused Gromov-Wasserstein Geometry

arXiv:2610.04344v1 Announce Type: new Abstract: Reinforcement learning with verifiable rewards (RLVR) has been shown to improve the reasoning capability of large language models (LLMs) across diverse

local-aiarxiv-cs-lg

How Should Teachers Be Prepared? RL on Student-Induced States for On-Policy Distillation

arXiv:2610.04950v1 Announce Type: new Abstract: On-policy distillation (OPD) improves the reasoning capabilities of small language models through token-level teacher supervision on student-generated t

safetyarxiv-cs-lg

IdeaLens: Detecting AI Ideas in Long-form Writing

arXiv:2610.06778v1 Announce Type: cross Abstract: While modern AI detectors identify who wrote the words, emerging policies on AI use increasingly hinge on a different question: who came up with the i

IGA-KAN: Isogeometric Analysis with Physics-Informed Closed-Form Kolmogorov-Arnold Networks for Forward and Inverse PDEs

arXiv:2610.06348v1 Announce Type: cross Abstract: Isogeometric analysis (IGA) solves partial differential equations accurately on exact NURBS geometry, whereas neural solvers are mesh-free but often o

local-aiarxiv-cs-lg

Agents

Imagine to Act: High-Fidelity Data Synthesis via Image Editing World Model for Scalable GUI Agent Training

arXiv:2610.05861v1 Announce Type: new Abstract: Graphical User Interface (GUI) agents have emerged as a promising paradigm for automating complex digital workflows across diverse applications. However

agentsarxiv-cs-cv

Applications

Isotropic Gaussian Processes Improve Vanilla Bayesian Optimization in High Dimensions

arXiv:2610.05780v1 Announce Type: cross Abstract: High-dimensional Bayesian optimization (BO) often fits Gaussian process (GP) surrogates from far fewer observations than input dimensions. Modern Vani

applicationsarxiv-cs-lg

Joint Class-Time Learning for Video Classification with Multi-Instance Partial-Label Learning

arXiv:2610.06234v1 Announce Type: new Abstract: Multi-instance partial-label learning (MIPL) addresses inexact supervision in both the instance and label spaces, which can be applied to video classifi

researcharxiv-cs-cv

Kinematics-Centric Continuous Sign Language Retrieval with Gloss-Guided Boundary-Aware Alignment

arXiv:2610.05306v1 Announce Type: new Abstract: Sign language-text alignment remains a fundamental challenge for text-driven sign language understanding. Existing methods predominantly rely on appeara

local-aiarxiv-cs-cv

KVE-KD: Key Visual Evidence-Guided Knowledge Distillation for Vision-Language Models

arXiv:2610.03842v1 Announce Type: new Abstract: Knowledge distillation is crucial for deploying vision-language models on resource-constrained devices. However, existing methods typically impose unifo

Label-Free Coreset Selection with Foundation Models for Efficient Annotation in Computational Pathology

arXiv:2610.05987v1 Announce Type: new Abstract: Computational pathology has the potential to improve clinical outcomes through a demonstrated increase in diagnostic and prognostic accuracy. However, t

researcharxiv-cs-cv

Latent Flow Matching for Molecular Graph Generation

arXiv:2610.06468v1 Announce Type: new Abstract: Modern graph generative models typically operate directly in the discrete graph space, explicitly generating node and edge variables, which can become c

LD-EnFF: Latent-Dynamics Ensemble Flow Filtering for Data Assimilation with Sparse Observations

arXiv:2610.04034v1 Announce Type: new Abstract: Data assimilation combines model forecasts with noisy, incomplete observations to estimate the evolving state of a dynamical system. Existing methods fa

Learning in Continuous Games from Pairwise Preference Feedback

arXiv:2610.05428v1 Announce Type: cross Abstract: We study learning in continuous games when players receive only pairwise preference feedback, revealing which of two actions is preferred but neither

Learning Interpretable Differentiable Logic Networks for Tabular Regression

arXiv:2505.23615v2 Announce Type: replace Abstract: Neural networks (NNs) achieve outstanding performance in many domains; however, their decision processes are often opaque and their inference can be

Learning Interpretable Differentiable Logic Networks for Time-Series Classification

arXiv:2508.17512v2 Announce Type: replace Abstract: Differentiable logic networks (DLNs) have shown promising results in tabular domains by combining accuracy, interpretability, and computational effi

Learning under Localized Minority Imbalance

arXiv:2610.04936v1 Announce Type: new Abstract: Class-imbalance methods implicitly assume that the minority class is uniformly undersampled relative to the majority class. However, in many real-world

LEON: Location Embeddings from OSM Neighborhoods via Hexagonal Graph Masked Autoencoders

arXiv:2610.05497v1 Announce Type: new Abstract: Geographic information systems increasingly rely on sophisticated spatial representation learning techniques to extract meaningful patterns from complex

tutorialsarxiv-cs-lg

Less Context, Better Geometry: Masked Geometric Encoder for Robust 3D Foundation Models

arXiv:2610.06813v1 Announce Type: new Abstract: Recent progress in 3D foundation models has enabled rapid 3D reconstruction and camera calibration by leveraging learned 3D priors from vast amount of s

tutorialsarxiv-cs-cv

Local Fisher Information Enables Sparse Causal Discovery

arXiv:2610.04291v1 Announce Type: cross Abstract: Sparse causal discovery calls for methods that exploit graph structure without estimating high-dimensional densities. We introduce Fisher Information

Localized Operator Learning with Adaptive Partition-of-Unity Mixture-of-Expert Networks

arXiv:2610.04708v1 Announce Type: new Abstract: Operator learning methods such as DeepONets and FNOs often struggle with PDE families featuring sharp interfaces, heterogeneous coefficients, and locali

Hardware

LogSig-SSM: Time-Series Modelling with Multi-Scale Log-Signature Compression for State-Space Models

arXiv:2610.05051v1 Announce Type: new Abstract: Time-series data are often sampled irregularly at high frequencies and exhibit long-range dependencies, which makes long-horizon modelling difficult. Co

hardwarearxiv-cs-lg

Long-MDR: Long-Context Reinforcement Learning for Multimodal Deep-Research Agents

arXiv:2610.05195v1 Announce Type: new Abstract: The next generation of multimodal research agents must reason over long-lived research histories rather than short model completions. During a single ta

Look Where You Say You're Looking: Self-Grounded Attention for Visual Reasoning

arXiv:2610.05023v1 Announce Type: new Abstract: We introduce Self-Saliency, a method for training Vision-Language Models (VLMs) to increase the alignment between their visual attention and the image r

local-aiarxiv-cs-cv

LoopMoEVR: Loop-Based Degradation-Aware Mixture-of-Experts for Unified UHD Video Restoration

arXiv:2610.05109v1 Announce Type: new Abstract: Recently, unified high-definition image restoration has attracted considerable attention; however, existing models tend to excessively increase their de

M^2-Occ: Resilient 3D Semantic Occupancy Prediction for Autonomous Driving with Incomplete Camera Inputs

arXiv:2603.09737v2 Announce Type: replace Abstract: Semantic occupancy prediction enables dense 3D geometric and semantic understanding for autonomous driving. However, existing camera-based approache

MaDeL: Manifold-Decomposed Feature Losses for Generative Modeling

arXiv:2610.04419v1 Announce Type: new Abstract: Generative models are often trained with isotropic objectives such as mean-squared error. For data concentrated near a low-dimensional manifold, however

MAGEFormer: Learning Metric-Consistent Representations for Anisotropic CT Segmentation

arXiv:2610.04036v1 Announce Type: new Abstract: Vision Transformers (ViTs) have shown strong performance in volumetric segmentation, but their effectiveness on clinical CT is limited by an isotropic E

MambaVF: State Space Model for Efficient Video Fusion

arXiv:2602.06017v2 Announce Type: replace Abstract: Video fusion aims to integrate complementary information from multiple source videos while preserving temporal consistency. Effective modeling of te

MatrixFormer: A Foundation Model for Matrix Completion

arXiv:2610.06751v1 Announce Type: new Abstract: Matrix completion underlies problems from tabular imputation to causal inference, yet existing tabular foundation models treat it as entry-by-entry pred

MemPilot: Orchestrating On-Demand Multimodal Memory Curation for LLM Agents

arXiv:2610.06830v1 Announce Type: cross Abstract: Memory has become integral to the LLM agent ecosystem, supporting information retention and reuse across interactions. However, most existing agent me

safetyarxiv-cs-lg

MercerFlow: Flow Matching in a Kernel-Induced Latent Space for Probabilistic Forecasting

arXiv:2610.06039v1 Announce Type: new Abstract: Recent work has shown that probabilistic flow matching for time series forecasting benefits from a data-matched prior. The resulting prior introduces lo

local-aiarxiv-cs-lg

MeSD: Multi-Evidence Self-Distillation for VideoLLM

arXiv:2610.06342v1 Announce Type: new Abstract: While reinforcement learning with verifiable rewards provides reliable outcome supervision for VideoLLMs, sequence-level rewards offer limited token-lev

METRO: Metric-Enhanced Token Routing Operator

arXiv:2610.05100v1 Announce Type: new Abstract: State-of-the-art neural operators scale to complex meshes via slice-and-process architectures, yet many rely on linear compatibility scores for latent t

tutorialsarxiv-cs-lg

Mitigating Over-squashing without Rewiring: A Sheaf Effective Resistance Perspective

arXiv:2610.04157v1 Announce Type: cross Abstract: Graph Neural Networks (GNNs) often struggle to capture long-range dependencies due to over-squashing -- a phenomenon in which the repeated compression

MoCAR: Motion-code Coordinate-aware AutoRegression for Continuous Trajectory Forecasting

arXiv:2610.06210v1 Announce Type: new Abstract: Autoregressive generation is natural for language, where predicted tokens can be directly reused as the next prediction state, but trajectory forecastin