Search: “evaluation”

arXiv:2605.03900v1 Announce Type: new Abstract: Frontier AI systems perform best in settings with clear, stable, and verifiable objectives, such as code generation, mathematical reasoning, games, and

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Model Releases

Continuum: Efficient and Robust Multi-Turn LLM Agent Scheduling with KV Cache Time-to-Live

DGX agent

arXiv:2511.02230v4 Announce Type: replace-cross Abstract: KV cache management is essential for efficient LLM inference. To maximize utilization, existing inference engines evict finished requests' KV

model-releasesarxiv-cs-ai

7 May 2026

Hardware

Coral: Cost-Efficient Multi-LLM Serving over Heterogeneous Cloud GPUs

arXiv:2605.04357v1 Announce Type: cross Abstract: The usage of large language models (LLMs) has grown increasingly fragmented, with no single model dominating. Meanwhile, cloud providers offer a wide

hardwarearxiv-cs-cl

Counter-Dyna: Data-Efficient RL-Based HVAC Control using Counterfactual Building Models

arXiv:2605.04555v1 Announce Type: new Abstract: Model-based reinforcement learning (MBRL) offers a promising approach for data-efficient energy management in buildings, combining the strengths of pred

Cross-Model Consistency of Feature Importance in Electrospinning: Separating Robust from Model-Dependent Features

arXiv:2605.04905v1 Announce Type: new Abstract: Electrospinning is a highly sensitive fabrication process in which small variations in operating parameters can significantly influence fiber morphology

DALight-3D: A Lightweight 3D U-Net for Brain Tumor Segmentation from Multi-Modal MRI

arXiv:2605.04518v1 Announce Type: new Abstract: Automatic brain tumor segmentation from multi-modal MRI remains challenging because volumetric models often incur substantial computational cost. This p

Decompose to Understand, Fuse to Detect: Frequency-Decoupled Anomaly Detection for Encrypted Network Traffic

arXiv:2605.02970v1 Announce Type: cross Abstract: Network traffic anomaly detection represents a critical cybersecurity task, yet widespread encryption makes this task increasingly challenging. In res

safetyarxiv-cs-ai

Hardware

Deep Wave Network for Modeling Multi-Scale Physical Dynamics

arXiv:2605.04198v1 Announce Type: new Abstract: Performance of deep learning models is strongly governed by architectural capacity, with width and depth as primary controls. However, in physical-scien

hardwarearxiv-cs-lg

Delta-Based Neural Architecture Search: LLM Fine-Tuning via Code Diffs

arXiv:2605.04903v1 Announce Type: cross Abstract: Large language models (LLMs) show strong potential for neural architecture generation, yet existing approaches produce complete model implementations

Denoising Particle Filters: Learning State Estimation with Single-Step Objectives

arXiv:2602.19651v2 Announce Type: replace-cross Abstract: Learning-based methods commonly treat state estimation in robotics as a sequence modeling problem. While this paradigm can be effective at max

Agents

Designing a double deep reinforcement learning selection tool for resilient demand prediction

arXiv:2605.04068v1 Announce Type: new Abstract: The use of artificial intelligence in supply chain forecasting has attracted many scientific studies for several decades. However, the process of select

agentsarxiv-cs-lg

Detecting Deepfakes via Hamiltonian Dynamics

arXiv:2605.04405v1 Announce Type: new Abstract: Driven by the rapid development of generative AI models, deepfake detectors are compelled to undergo periodic recalibration to capture newly developed s

Differentiable Chemistry in PINNs for Solving Parameterized and Stiff Reaction Systems

arXiv:2605.04708v1 Announce Type: new Abstract: From neural ODEs to continuous-time machine learning, differentiable solvers allow physics, optimization, and simulation to become trainable components

Diffusion-Based Feature Denoising with NNMF for Robust handwritten digit multi-class classification

arXiv:2603.29917v2 Announce Type: replace Abstract: This work presents a robust multi-class classification framework for handwritten digits that combines diffusion-driven feature denoising with a hybr

Disentangled Learning Improves Implicit Neural Representations for Medical Reconstruction

arXiv:2605.04234v1 Announce Type: new Abstract: Implicit neural representations (INRs) have emerged as a powerful paradigm for medical imaging via physics-informed unsupervised learning. Classical INR

DistributedEstimator: Distributed Training of Quantum Neural Networks via Circuit Cutting

arXiv:2602.16233v2 Announce Type: replace-cross Abstract: Circuit cutting decomposes a large quantum circuit into smaller subcircuits whose outputs are classically reconstructed to recover original ex

DPD-Cancer: Explainable Graph-Based Deep Learning for Small Molecule Anti-Cancer Activity Prediction

arXiv:2603.26114v2 Announce Type: replace Abstract: DPD-Cancer is a graph-attention deep learning framework for predicting small-molecule DPD-Cancer is a graph-attention deep learning framework for pr

Driver-WM: A Driver-Centric Traffic-Conditioned Latent World Model for In-Cabin Dynamics Rollout

arXiv:2605.05092v1 Announce Type: cross Abstract: Safe L2/L3 driving automation requires anticipating human-in-the-loop reactions during shared-control transitions. While most driving world models for

Dynamic Hyperparameter Importance for Efficient Multi-Objective Optimization

arXiv:2601.03166v2 Announce Type: replace Abstract: Choosing a suitable ML model is a complex task that can depend on several objectives, e.g., accuracy, fairness, or energy consumption. In practice,

Efficient Geometry-Controlled High-Resolution Satellite Image Synthesis

arXiv:2605.04557v1 Announce Type: new Abstract: High-resolution satellite images are often scarce and costly, especially for remote areas or infrequent events. This shortage hampers the development an

safetyarxiv-cs-cv

Efficient Model-Based Reinforcement Learning for Robot Control via Online Optimization

arXiv:2510.18518v2 Announce Type: replace Abstract: We present an online model-based reinforcement learning algorithm suitable for controlling complex robotic systems directly in the real world. Unlik

safetyarxiv-cs-ro

Encoding Predictability and Legibility for Style-Conditioned Diffusion Policy

arXiv:2603.16368v2 Announce Type: replace-cross Abstract: Striking a balance between efficiency and transparent motion is a core challenge in human-robot collaboration, as highly expressive movements

Enhancing Agent Safety Judgment: Controlled Benchmark Rewriting and Analogical Reasoning for Deceptive Out-of-Distribution Scenarios

model-releasesarxiv-cs-ai

arXiv:2605.03242v1 Announce Type: new Abstract: Tool-using agent systems powered by large language models (LLMs) are increasingly deployed across web, app, operating-system, and transactional environm

Entropic Riemannian Neural Optimal Transport

arXiv:2605.04255v1 Announce Type: cross Abstract: Many machine learning problems involve data supported on curved spaces such as spheres, rotation groups, hyperbolic spaces, and general Riemannian man

Evidence-based anomaly detection in clinical domains

arXiv:2605.04664v1 Announce Type: new Abstract: Anomaly detection methods can be very useful in identifying interesting or concerning events. In this work, we develop and examine new probabilistic ano

Applications

Exploring Clustering Capability of Inpainting Model Embeddings for Pattern-based Individual Identification

arXiv:2605.04904v1 Announce Type: new Abstract: In this paper, we explore deep learning techniques for individual identification of animals based on their skin patterns. Individual identification is c

applicationsarxiv-cs-cv

External Validation of Deep Learning Models for BI-RADS Breast Density Prediction from Ultrasound Images

arXiv:2605.05082v1 Announce Type: cross Abstract: We externally validated three deep learning models (DenseNet121, ViT-B/32, and ResNet50) for predicting mammographic breast density from breast ultras

FairEnc: A Fair Vision-Language Model with Fair Vision and Text Encoders for Glaucoma Detection

arXiv:2605.04882v1 Announce Type: new Abstract: Automated glaucoma detection is critical for preventing irreversible vision loss and reducing the burden on healthcare systems. However, ensuring fairne

safetyarxiv-cs-cv

Federated Learning for Early Prediction of EV Charging Demand

arXiv:2605.04993v1 Announce Type: new Abstract: Accurate forecasting of electric vehicle (EV) charging demand is critical for grid stability, infrastructure planning, and real-time charging optimizati

Few-Shot Learning Pipeline for Monkeypox Skin Disease Classification Using CNN Feature Extractors

arXiv:2605.05034v1 Announce Type: new Abstract: Despite the strong performance of Convolutional Neural Networks (CNNs) in disease classification, their effectiveness often depends on access to large a

FlatASCEND: Autoregressive Clinical Sequence Generation with Continuous Time Prediction and Association-Based Pharmacological Testing

arXiv:2605.04071v1 Announce Type: new Abstract: Autoregressive models can predict clinical events, but generating patient-conditioned multi-step trajectories that respond to intervention tokens and te

Agents

FlowDIS: Language-Guided Dichotomous Image Segmentation with Flow Matching

arXiv:2605.05077v1 Announce Type: new Abstract: Accurate image segmentation is essential for modern computer vision applications such as image editing, autonomous driving, and medical image analysis.

agentsarxiv-cs-cv

FLUID: Continuous-Time Hyperconnected Sparse Transformer for Sink-Free Learning

arXiv:2605.04421v1 Announce Type: new Abstract: Continuous-time (CT) Transformers improve irregular and long-range modeling over CT-RNNs by exploiting inputs or outputs embeddings with continuous dyna

Fragile Knowledge, Robust Instruction-Following: The Width Pruning Dichotomy in Llama-3.2

model-releasesarxiv-cs-cl

arXiv:2512.22671v2 Announce Type: replace Abstract: Structured width pruning of GLU-MLP layers, guided by the Maximum Absolute Weight (MAW) criterion, reveals a systematic dichotomy in how reducing th

Free Energy-Driven Reinforcement Learning with Adaptive Advantage Shaping for Unsupervised Reasoning in LLMs

model-releasesarxiv-cs-cl

arXiv:2605.04065v1 Announce Type: new Abstract: Unsupervised reinforcement learning (RL) has emerged as a promising paradigm for enabling self-improvement in large language models (LLMs). However, exi

Local Ai

From Intent to Execution: Composing Agentic Workflows with Agent Recommendation

arXiv:2605.03986v1 Announce Type: new Abstract: Multi-Agent Systems (MAS) built using AI agents fulfill a variety of user intents that may be used to design and build a family of related applications.

local-aiarxiv-cs-ai

From Passive Feeds to Guided Discovery: AI-Initiated Interaction for Vague Intent in Content Exploration

arXiv:2605.02902v1 Announce Type: cross Abstract: Recommendation feeds work well when people are simply browsing, and search works well when they can formulate a query. Between these two cases is a co

researcharxiv-cs-ai

From Reach to Insert: Tactile-Augmented Precision Assembly under Sub-Millimeter Tolerances

arXiv:2605.04649v1 Announce Type: new Abstract: High-precision assembly frequently involves tight-tolerance insertions, where even slight pose errors can cause jamming or excessive interaction forces,

safetyarxiv-cs-ro

Generative Quantum-inspired Kolmogorov-Arnold Eigensolver

arXiv:2605.04604v1 Announce Type: cross Abstract: High-performance computing (HPC) is increasingly important for scalable quantum chemistry workflows that couple classical generative models, quantum c

Graph-Augmented LLMs for Swiss MP Ideology Prediction

arXiv:2605.04643v1 Announce Type: new Abstract: Approximating the ideological position of Members of Parliament (MPs) is a fundamental task in political science, helping researchers understand legisla

safetyarxiv-cs-cl

Harnessing Linguistic Dissimilarity for Language Generalization on Unseen Low-Resource Varieties

arXiv:2605.04500v1 Announce Type: new Abstract: Low-resource language varieties used by specific groups remain neglected in the development of Multilingual Language Models. A great deal of cross-lingu

researcharxiv-cs-cl

Tutorials

HDFlow: Hierarchical Diffusion-Flow Planning for Long-horizon Tasks

arXiv:2605.04525v1 Announce Type: new Abstract: Recent advances in generative models have shown promise in generating behavior plans for long-horizon, sparse reward tasks. While these approaches have

tutorialsarxiv-cs-ro

How BASF manages thousands of supply chain decisions with AlphaEvolve’s agentic algorithms

model-releasesgoogle-cloud-ai

The agricultural and crop protection supply chain is one of the most intricate networks in the world. It takes up to two years to turn active ingredients into the final products farmers need, and a si

Hardware

.@huggingface's agentic robotics app store for Reachy Mini is a big step toward more accessible physical AI. 🙌 Excited to see NVIDIA Isaac …

.@huggingface's agentic robotics app store for Reachy Mini is a big step toward more accessible physical AI. 🙌 Excited to see NVIDIA Isaac GR00T N integrated with Hugging Face LeRobot, helping develop

hardwareclem-delangue--x

Agents

Human-Provenance Verification should be Treated as Labor Infrastructure in AI-Saturated Markets

arXiv:2605.03210v1 Announce Type: cross Abstract: We argue that AI-saturated markets are likely to create Veblen-good premiums, which we term human-provenance premiums, for verified human presence, an

agentsarxiv-cs-ai

IDRBench: Understanding the Capability of Large Language Models on Interdisciplinary Research

arXiv:2507.15736v2 Announce Type: replace Abstract: Innovation is a key driving force of human civilization. As the body of knowledge has grown considerably, bridging knowledge across different discip

researcharxiv-cs-cl

Improving Bias Correction Standards by Quantifying its Effects on Treatment Outcomes

arXiv:2407.14861v3 Announce Type: replace-cross Abstract: With the growing access to administrative health databases, retrospective studies have become crucial evidence for medical treatments. Yet, no

InterFuserDVS: Event-Enhanced Sensor Fusion for Safe RL-Based Decision Making

arXiv:2605.04355v1 Announce Type: new Abstract: Autonomous driving systems rely heavily on robust sensor fusion to perceive complex envi- ronments. Traditional setups using RGB cameras and LiDAR often

safetyarxiv-cs-cv