Search: “evaluation”

Deep RL- Tuned Mo del-Free Adaptive Control for Lower-Limb Exoskeletons During Sit-to-Stand Transitions

arXiv:2606.22040v1 Announce Type: new Abstract: Sit-to-stand (STS) transitions impose significant joint-loading demands on elderly individuals, making them a primary target for lower-limb exoskeleton

local-aiarxiv-cs-ro

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Model Releases

Demystifying Numerical Instability in LLM Inference: Achieving Reproducible Inference for Mission-Critical Tasks with HEAL

DGX agent

arXiv:2606.21023v1 Announce Type: new Abstract: As Large Language Models (LLMs) deploy into mission-critical domains (e.g., finance, medicine, and law), output reproducibility has become a strict syst

model-releasesarxiv-cs-lg

23 Jun 2026

Detecting and Understanding Vulnerabilities in Fully Homomorphic Encryption Frameworks

arXiv:2606.22519v1 Announce Type: cross Abstract: Fully homomorphic encryption (FHE) allows computations to be performed directly on encrypted data without decryption, offering strong privacy guarante

applicationsarxiv-cs-lg

Detecting Satellites in Radio-Frequency Data via Semi-Supervised Learning

arXiv:2606.20976v1 Announce Type: new Abstract: Radio-frequency (RF) monitoring is essential for space domain awareness, but it often generates large, variable, and sparsely populated datasets with fe

DexTeleop-0: Force-Aware Bimanual Dexterous Teleoperation with Ego-Centric Perception towards Shared Autonomy

arXiv:2606.23431v1 Announce Type: new Abstract: Fine-grained, bimanual dexterous manipulation remains a foundational challenge in robotics. Traditional teleoperation systems often fail in contact-rich

applicationsarxiv-cs-ro

Discovering Crystal Structure Prediction Algorithms with an AI Co-Scientist

arXiv:2606.22866v1 Announce Type: new Abstract: We introduce Human-AI Co-discovery system (HACO) for scientific algorithm discovery through cross-domain search and sparse human steering. Starting from

Discovering Latent Groups for Robust Classification

arXiv:2606.23609v1 Announce Type: cross Abstract: Machine learning models exploit spurious correlations, achieving high average accuracy but failing disproportionately on underrepresented subgroups. E

Discretizing Reward Models

arXiv:2606.21795v1 Announce Type: new Abstract: Despite their widespread use, the role of reward models in shaping reinforcement learning is poorly understood. Reward models offer a tempting promise:

Disentangling Intrinsic Importance from Emergent Structure in Multi-Expert Orchestration

arXiv:2602.04291v2 Announce Type: replace Abstract: Multi-expert systems, where multiple Large Language Models (LLMs) collaborate to solve complex tasks, are increasingly adopted for high-performance

Distributed Model Predictive Control with Adaptive Safety Zones for Multi-Fleet Drone Operations

arXiv:2606.20651v1 Announce Type: cross Abstract: Autonomous drone swarms in space-constrained environments such as warehouses, inspection corridors, and urban delivery routes must share limited airsp

Distribution-Aware Diffusion-LLM for Robust Ultra-Long-Term Time Series Forecasting

arXiv:2606.23391v1 Announce Type: new Abstract: Time series forecasting is a fundamental machine learning task. Recent work has explored Large Language Models (LLMs) for this purpose due to their stro

Do Location Encoders Capture Spatial Effects? A GeoShapley Benchmark Across Scales

arXiv:2606.23453v1 Announce Type: new Abstract: Location encoders transform geographic coordinates into high dimensional embeddings for downstream machine learning, but it is unclear how well these re

Domain-incremental audio classification using domain-specific experts and prototype classifier

arXiv:2606.22952v1 Announce Type: cross Abstract: This technical report presents submission systems for Task 7(domain-incremental audio classification) of the DCASE 2026 Challenge. The main obstacle i

DR-Mamba: Automatic Inference-Time Domain Adaptation for Document Image Binarization via Sample-Conditioned Detail-Background Suppression

model-releasesarxiv-cs-cv

arXiv:2606.22625v1 Announce Type: new Abstract: Degraded document image binarization is sensitive to domain shifts caused by paper aging, bleed-through, stains, shadows, and uneven illumination, and t

DreamUV: Unwrap Artist-like UV by End-to-End Flow Matching

arXiv:2606.22445v1 Announce Type: new Abstract: UV parameterization is a fundamental step in 3D content creation, yet producing production-ready UV layouts remains challenging due to the gap between g

applicationsarxiv-cs-cv

DrivingVoxels: Compositional Sparse Voxel Rasterization for Dynamic Driving Scene Reconstruction

model-releasesarxiv-cs-cv

arXiv:2606.23031v1 Announce Type: new Abstract: Reconstructing dynamic urban scenes remains challenging due to the unbounded nature of driving environments and the presence of multiple dynamic objects

Duet: Dual-Robot Understanding via Efficient Teaching

arXiv:2606.20990v1 Announce Type: new Abstract: Dual-robot collaboration enables tasks that exceed the reach and payload of a single robot, such as collaboratively transporting objects across environm

Durability-Aware Multi-Objective Optimization of the Jansen Linkage: Trading Gait Quality Against Joint Wear

arXiv:2606.22129v1 Announce Type: new Abstract: The Jansen linkage is a single-degree-of-freedom planar leg mechanism whose eleven 'holy numbers' were evolved by Theo Jansen to optimize the foot-path

applicationsarxiv-cs-ro

dVLA-RL: Reinforcement Learning over Denoising Trajectories for Discrete Diffusion Vision-Language-Action Models

arXiv:2606.23623v1 Announce Type: new Abstract: Vision-Language-Action (VLA) models have established a powerful paradigm for generalist robotic manipulation by grounding control into the semantic reas

safetyarxiv-cs-ro

EBench: Elemental Diagnosis of Generalist Mobile Manipulation Policies

arXiv:2606.18239v2 Announce Type: replace Abstract: We present EBench, a simulation benchmark that diagnoses generalist mobile manipulation policies beyond a single success-rate scalar. EBench compris

Efficient Training of Boltzmann Generators Using Off-Policy Log-Dispersion Regularization

arXiv:2602.03729v2 Announce Type: replace Abstract: Sampling from unnormalized probability densities is a central challenge in computational science. Boltzmann generators are generative models that en

EgoExo-Con: Exploring View-Invariant Video Temporal Understanding

model-releasesarxiv-cs-cv

arXiv:2510.26113v2 Announce Type: replace Abstract: Do Video-LLMs have consistent temporal understanding when videos capture the same event from different viewpoints? To study this question, we introd

ELADO: Elliptic PDE Assessment Datasets for Operator Learning

arXiv:2606.20771v1 Announce Type: new Abstract: We introduce ELADO (Elliptic PDE Assessment Datasets for Operator Learning), a systematic benchmark suite constructed to show and quantify failure modes

EmbodiedUS-FS: Fast Slow Intelligence for Ultrasound Robotics

arXiv:2606.22319v1 Announce Type: cross Abstract: Robotic ultrasound scanning in real clinical environments requires both high-level clinical workflow reasoning and low-level closed-loop execution. Ph

safetyarxiv-cs-cv

Agents

Empowering Embodied AI in 6G Networks: Architecture, Enablers, and Open Challenges

arXiv:2606.20592v1 Announce Type: cross Abstract: Embodied artificial intelligence (AI) is emerging as a key driver of the sixth-generation (6G) wireless networks by enabling agents that continuously

agentsarxiv-cs-lg

Energy Injection Identification enabled Disaggregation with Deep Multi-Task Learning

arXiv:2508.14600v4 Announce Type: replace Abstract: Non-Intrusive Load Monitoring (NILM) offers a cost-effective method to obtain fine-grained appliance-level energy consumption in smart homes and bui

Agents

Enhancing Creativity in 3D Generative Design via a TRIZ-Inspired Text-to-CAD Framework

arXiv:2606.21378v1 Announce Type: new Abstract: Recent advances in large language models (LLMs) have demonstrated significant potential in supporting engineering design tasks, including computer-aided

agentsarxiv-cs-lg

Enhancing IMU-Based Online Handwriting Recognition via Contrastive Learning with Zero Inference Overhead

arXiv:2602.07049v2 Announce Type: replace Abstract: Online handwriting recognition using inertial measurement units opens up handwriting on paper as input for digital devices. Doing it on edge hardwar

safetyarxiv-cs-cv

Error Highways: Scaling Predictive Coding to Very Deep Networks

arXiv:2606.22744v1 Announce Type: new Abstract: Predictive coding networks (PCNs) offer a biologically-plausible, local-learning alternative to back-propagation of errors (backprop). Nevertheless, the

local-aiarxiv-cs-lg

Tutorials

Evolutionary Discovery of Developmental Reward Schedules in Deep Reinforcement Learning

arXiv:2606.20858v1 Announce Type: new Abstract: The temporal structure of reward composition in reinforcement learning (RL) is typically hand-designed and held fixed throughout training, leaving the p

tutorialsarxiv-cs-lg

Exact and Approximate Range Queries for Efficient Ball Mapper Construction

arXiv:2601.01405v2 Announce Type: replace-cross Abstract: Ball Mapper is a tool in topological data analysis that summarizes a finite metric dataset by covering it with metric balls and encoding their

Expert Consensus on Criteria for the Automated Assessment of Laparoscopic Camera Navigation

arXiv:2606.23131v1 Announce Type: new Abstract: Background: Laparoscopic camera navigation (LCN) is a critical skill, yet its current assessment typically relies on manual rating systems which are tim

safetyarxiv-cs-cv

Explainable Boosting Machine for Predicting Claim Severity and Frequency in Car Insurance

arXiv:2503.21321v2 Announce Type: replace-cross Abstract: With the rapid development of machine learning and deep learning techniques, actuaries and the broader insurance industry face a persistent tr

Explanations for Automatic Speech Recognition

arXiv:2302.14062v2 Announce Type: replace-cross Abstract: We address quality assessment for neural network based ASR by providing explanations that help increase our understanding of the system and ul

local-aiarxiv-cs-lg

Expressivity Saturation: Reduced Affine Region Usage Under Increasing Task Complexity

arXiv:2606.21687v1 Announce Type: new Abstract: Piecewise-affine neural networks (e.g., with ReLU or LeakyReLU activations) implement continuous piecewise-affine maps, and the number of affine regions

Factor-Aware Mixture-of-Experts with Pretrained Encoder for Combinatorial Generalization

arXiv:2606.21100v1 Announce Type: new Abstract: The integration of pretrained encoders with diffusion policies has become a dominant paradigm for visual robotic manipulation. However, it still struggl

FAIRVAR: Fair Federated Learning via Variance Regularization

arXiv:2508.12042v2 Announce Type: replace Abstract: Federated learning (FL) allows collaborative training of machine learning models across multiple parties without sharing raw data. However, heteroge

FAST: A Framework for Aligned Sampling and Training in Parallel Reinforcement Learning for Autonomous Driving

arXiv:2606.21587v1 Announce Type: new Abstract: Deep reinforcement learning is pivotal for closed-loop autonomous driving yet remains constrained by severe bottlenecks in sampling efficiency. Standard

Hardware

Fast-TurboQuant: A Multiplier-Free Online Vector Quantization Approach

arXiv:2606.21448v1 Announce Type: new Abstract: As large language models scale, memory bandwidth for key-value caches and retrieval-augmented generation systems becomes a critical bottleneck. While 1-

hardwarearxiv-cs-lg

FCR: Investigating Generative AI models for Forensic Craniofacial Reconstruction

arXiv:2508.18031v2 Announce Type: replace Abstract: Craniofacial reconstruction in forensics is one of the processes to identify victims of crime and natural disasters. Identifying an individual from

Federated Learning for Global Carbon Emission Forecasting: A Hybrid Time-Series Approach with Statistical and Neural Models

arXiv:2606.22618v1 Announce Type: new Abstract: Climate change, primarily driven by carbon dioxide (CO2) emissions, requires accurate forecasting tools to support effective mitigation policies and sus

Federated Temporal Attention Intelligence for Cyber-Resilient IoMT: Lightweight Digital Twins and PPO-Driven Honeypot Deception

arXiv:2606.21422v1 Announce Type: new Abstract: The rapid proliferation of Internet of Medical Things (IoMT) devices introduces critical cybersecurity vulnerabilities in healthcare environments where

FedSA-GCL: A Semi-Asynchronous Federated Graph Learning Framework with Personalized Aggregation and Cluster-Aware Broadcasting

arXiv:2507.18219v3 Announce Type: replace Abstract: Federated Graph Learning (FGL) is a distributed learning paradigm that enables collaborative training over large-scale subgraphs located on multiple

local-aiarxiv-cs-lg

FetSelect: Task-Specific Architectures and Self-Supervised Learning for Automated Fetal Ultrasound Frame Selection

arXiv:2606.22487v1 Announce Type: new Abstract: Automated frame selection for fetal biometry remains under addressed, with most prior work targeting generic quality assessment or downstream measuremen

Few-Shot Hyperspectral Aphid Detection via FastGAN Synthetic Data Generation, Transformer-Based Classification and Explainable AI

arXiv:2606.21267v1 Announce Type: new Abstract: Early detection of aphid infestation in crops is essential for preventing yield loss and reducing unnecessary pesticide use. Hyperspectral imaging combi

FirstPass: Grounding AI Scientific Judgment in Multi-Round Editorial Outcomes

arXiv:2606.20769v1 Announce Type: cross Abstract: AI systems for peer review fail on three fronts: they train on Computer Science and Machine Learning venues alone, ignore the iterative dialogue that

FLFL: Federated Latent Factor Learning for Private Recovery of Spatio-Temporal Signals

arXiv:2606.23091v1 Announce Type: new Abstract: Wireless sensor network (WSNs) stands out as a burgeoning and promising domain in intelligent sensing. Owing to various factors such as sudden sensor ma

applicationsarxiv-cs-lg

Flow Annealing Posterior Sampling for Function-Space Regression and Inverse Problems

arXiv:2606.22346v1 Announce Type: cross Abstract: Principled regression for stochastic processes is a long-standing challenge with deep connections to scientific inverse problems. We introduce Flow An