Search: “evaluation”

CoAction: Cross-task Correlation-aware Pareto Set Learning

arXiv:2605.01712v1 Announce Type: new Abstract: Pareto set learning (PSL) is an emerging paradigm in multi-objective optimization that trains neural networks to map preference vectors to Pareto optima

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Model Releases

COCORELI: Enforcing Execution Preconditions for Reliable Collaborative Instruction Following

DGX agent

arXiv:2509.04470v2 Announce Type: replace Abstract: Autonomous agents executing human instructions must operate reliably even when instructions are incomplete. While recent approaches improve detectio

model-releasesarxiv-cs-cl

5 May 2026

Complex Diffusion Maps with omega-Parameterized Kernels Revealing Inherent Harmonic Representations

arXiv:2605.01691v1 Announce Type: new Abstract: In this paper, we propose Complex Diffusion Maps (CDM), a novel diffusion mapping framework that aims to reveal the dominant complex harmonics of high-d

Component-Aware Self-Speculative Decoding in Hybrid Language Models

arXiv:2605.01106v1 Announce Type: new Abstract: Speculative decoding accelerates autoregressive inference by drafting candidate tokens with a fast model and verifying them in parallel with the target.

Compositional Multi-hop Factual Error Correction via Decomposition-and-Injection

arXiv:2605.02277v1 Announce Type: new Abstract: Factual Error Correction (FEC) aims to revise inaccurate text into statements that are factually consistent with external evidence. Although recent meth

applicationsarxiv-cs-cl

Constructing Interpretable Features from Compositional Neuron Groups

arXiv:2506.10920v2 Announce Type: replace Abstract: A central goal for mechanistic interpretability has been to identify the right units of analysis in large language models (LLMs) that causally expla

Controlled Paraphrase Geometry in Sentence Embedding Space: Local Manifold Modeling and Latent Probing

arXiv:2605.01073v1 Announce Type: new Abstract: The paper studies the local geometry of embedding clouds induced by controlled local classes of semantically close sentences. The central question is ho

Counting as a minimal probe of language model reliability

arXiv:2605.02028v1 Announce Type: new Abstract: Large language models perform strongly on benchmarks in mathematical reasoning, coding and document analysis, suggesting a broad ability to follow instr

Cross-Language Learning within Arabic Script for Low-Resource HTR

arXiv:2605.02089v1 Announce Type: new Abstract: Handwritten Text Recognition (HTR) under limited labeled data remains a challenging problem, particularly for Arabic-script languages. Although modern s

Cross-Polarization Fusion of VV AND VH SAR Observations for Improved Flood Mapping

arXiv:2605.02153v1 Announce Type: new Abstract: Synthetic Aperture Radar (SAR) imagery is widely used for flood monitoring due to its all-weather and day-night imaging capability. However, flood mappi

Decoding-Time Debiasing via Process Reward Models: From Controlled Fill-in to Open-Ended Generation

arXiv:2605.02348v1 Announce Type: new Abstract: Large language models pick up social biases from the data they are trained on and carry those biases into downstream applications, often reinforcing ste

Deep Time Series Models: A Comprehensive Survey and Benchmark

arXiv:2407.13278v3 Announce Type: replace Abstract: Time series, characterized by a sequence of data points organized in a discrete-time order, are ubiquitous in real-world scenarios. Unlike other dat

Demographic-Aware Transfer Learning for Sleep Stage Classification in Clinical Polysomnography

arXiv:2605.02245v1 Announce Type: new Abstract: Automated sleep stage classification typically employs a single population-agnostic model, disregarding established demographic variations in sleep arch

researcharxiv-cs-lg

DIAGRAMS: A Review Framework for Reasoning-Level Attribution in Diagram QA

arXiv:2605.00905v1 Announce Type: new Abstract: Diagram question answering (Diagram QA) requires reasoning-level attribution that links each question-answer pair to all visual regions needed to derive

Local Ai

Differentiable Kernel Ridge Regression for Deep Learning Pipelines

arXiv:2605.02313v1 Announce Type: new Abstract: Deep neural networks dominate modern machine learning, while alternative function approximators remain comparatively underexplored at scale. In this wor

local-aiarxiv-cs-lg

DirectEdit: Step-Level Accurate Inversion for Flow-Based Image Editing

arXiv:2605.02417v1 Announce Type: new Abstract: With recent advancements in large-scale pre-trained text-to-image (T2I) models, training-free image editing methods have demonstrated remarkable success

Disciplined Diffusion: Text-to-Image Diffusion Model against NSFW Generation

arXiv:2605.01113v1 Announce Type: new Abstract: Text-to-image (T2I) diffusion models have the ability to build high-quality pictures from text prompts, but they pose safety concerns because they can g

DissolveStereo: Coarse Depth Injection for Zero-Shot Stereo Video Generation

arXiv:2411.14295v3 Announce Type: replace Abstract: Generating high-quality stereo videos requires consistent depth perception and temporal coherence across frames. Despite advances in image and video

Do Large Language Models Plan Answer Positions? Position Bias in Multiple-Choice Question Generation

arXiv:2605.01846v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly used to generate multiple-choice questions (MCQs), where correct answers should ideally be uniformly distr

safetyarxiv-cs-cl

Agents

DynFlowDrive: Flow-Based Dynamic World Modeling for Autonomous Driving

arXiv:2603.19675v2 Announce Type: replace Abstract: Recently, world models have been incorporated into the autonomous driving systems to improve the planning reliability. Existing approaches typically

agentsarxiv-cs-cv

E-mem: Multi-agent based Episodic Context Reconstruction for LLM Agent Memory

model-releasesarxiv-cs-ai

arXiv:2601.21714v2 Announce Type: replace Abstract: The evolution of Large Language Model (LLM) agents towards System~2 reasoning, characterized by deliberative, high-precision problem-solving, requir

Agents

EAPFusion: Intrinsic Evolving Auxiliary Prior Guidance for Infrared and Visible Image Fusion

arXiv:2605.01916v1 Announce Type: new Abstract: Infrared-visible image fusion aims to create an information-rich fused image by integrating the complementary thermal saliency from infrared sensing and

agentsarxiv-cs-cv

Edge Case Detection in Automated Driving: Methods, Challenges and Future Directions

arXiv:2410.08491v2 Announce Type: replace Abstract: Automated vehicles promise to enhance transportation safety and efficiency. However, ensuring their reliability in real-world conditions remains cha

safetyarxiv-cs-ro

EdgeLPR: On the Deep Neural Network trade-off between Precision and Performance in LiDAR Place Recognition

arXiv:2605.02275v1 Announce Type: new Abstract: Place recognition is essential for long-term autonomous navigation, enabling loop closure and consistent mapping. Although deep learning has improved pe

EduGage: Methods and Dataset for Sensor-Based Momentary Assessment of Engagement in Self-Guided Video Learning

arXiv:2605.01238v1 Announce Type: cross Abstract: Engagement, which links to attentional, emotional, and cognitive dimensions, plays an important role in learning. In online and video-based learning e

EmoMM: Benchmarking and Steering MLLM for Multimodal Emotion Recognition under Conflict and Missingness

arXiv:2605.01024v1 Announce Type: new Abstract: Multimodal Emotion Recognition (MER) is critical for interpreting real-world interactions. While Multimodal Large Language Models (MLLM) have shown prom

ENFORCE: Nonlinear Constrained Learning with Adaptive-depth Neural Projection

arXiv:2502.06774v4 Announce Type: replace Abstract: Ensuring neural networks adhere to domain-specific constraints is crucial for addressing safety and trustworthiness while also enhancing inference a

Enhanced LLM Reasoning by Optimizing Reward Functions with Search-Driven Reinforcement Learning

arXiv:2605.02073v1 Announce Type: new Abstract: Mathematical reasoning is a key benchmark for large language models. Reinforcement learning is a standard post-training mechanism for improving the reas

Enhancing Multimodal In-Context Learning via Inductive-Deductive Reasoning

arXiv:2605.02378v1 Announce Type: new Abstract: In-context learning (ICL) allows large models to adapt to tasks using a few examples, yet its extension to vision-language models (VLMs) remains fragile

Enhancing RL Generalizability in Robotics through SHAP Analysis of Algorithms and Hyperparameters

arXiv:2605.02867v1 Announce Type: new Abstract: Despite significant advances in Reinforcement Learning (RL), model performance remains highly sensitive to algorithm and hyperparameter configurations,

Environment-Aware Indoor LoRaWAN Path Loss: Parametric Regression Comparisons, Shadow Fading, and Calibrated Fade Margins

arXiv:2510.04346v2 Announce Type: replace-cross Abstract: Indoor long range wide area network (LoRaWAN) propagation is shaped by structural and time-varying environmental factors, which limit single-s

researcharxiv-cs-lg

EventADL: Open-Box Anomaly Detection and Localization Framework for Events in Cloud-Based Service Systems

arXiv:2605.00936v1 Announce Type: new Abstract: Anomaly detection and localization (ADL) is critical for maintaining reliability and availability in cloud systems. Recent ADL developments focus on met

Exploring Entropy-based Active Learning for Fair Brain Segmentation

arXiv:2605.01706v1 Announce Type: new Abstract: Active learning (AL) has emerged as a crucial strategy for reducing the prohibitive costs associated with medical image segmentation. However, standard

Exploring Prompt Alignment with Clinical Factors in Zero-Shot Segmentation VLMs for NSCLC Tumor Segmentation

arXiv:2605.01266v1 Announce Type: new Abstract: Zero-shot vision-language models (VLMs) offer a promptable alternative to task-specific training for gross tumor volume (GTV) delineation in non-small-c

Federated Reinforcement Learning for Efficient Mobile Crowdsensing under Incomplete Information

arXiv:2605.02705v1 Announce Type: new Abstract: Mobile crowdsensing (MCS) is a distributed sensing architecture that utilizes existing sensors on mobile units (MUs) to perform sensing tasks. A mobile

FedPLT: Scalable, Resource-Efficient, and Heterogeneity-Aware Federated Learning via Partial Layer Training

arXiv:2605.02337v1 Announce Type: cross Abstract: Federated Learning (FL) has gained significant attention in distributed machine learning by enabling collaborative model training across decentralized

Five must-have guides to move agents into production with Gemini Enterprise Agent Platform

model-releasesgoogle-cloud-ai

Building AI agents that work well in a demo is one thing, but running them in production requires serious infrastructure. At Google Cloud Next '26, we introduced Gemini Enterprise Agent Platform to he

FLoRA: Fusion-Latent for Optical Reconstruction and Flood Area Segmentation via Cross-Modal Multi-Task Distillation Network

arXiv:2605.02137v1 Announce Type: new Abstract: Accurate flood water mapping is critical for disaster management, yet current methods struggle to fully exploit the potential of spaceborne imagery. Opt

FoR-Net: Learning to Focus on Hard Regions for Efficient Semantic Segmentation

arXiv:2605.02764v1 Announce Type: new Abstract: We present FoR-Net, a lightweight architecture for semantic segmentation that focuses on identifying and enhancing hard regions. Instead of relying on h

From Euler to Dormand-Prince: ODE Solvers for Flow Matching Generative Models

arXiv:2605.00836v1 Announce Type: new Abstract: Sampling from Flow Matching generative models requires solving an ordinary differential equation (ODE) whose computational cost is dominated by neural n

From Mice to Trains: Amortized Bayesian Inference on Graph Data

arXiv:2601.02241v5 Announce Type: replace-cross Abstract: Graphs arise across diverse domains, from biology and chemistry to social and information networks, as well as in transportation and logistics

From Spherical to Gaussian: A Comparative Analysis of Point Cloud Cropping Strategies in Large-Scale 3D Environments

arXiv:2605.02098v1 Announce Type: new Abstract: Large-scale 3D point clouds can consist of billions of points. Even after downsampling, these point clouds are too large for modern 3D neural networks.

FunFuzz: An LLM-Powered Evolutionary Fuzzing Framework

arXiv:2605.02789v1 Announce Type: cross Abstract: Modern fuzzers increasingly use Large Language Models (LLMs) to generate structured inputs, but LLM-driven fuzzing is sensitive to prompt initializati

Fuzzy Fingerprinting Encoder Pre-trained Language Models for Emotion Recognition in Conversations: Human Assessment and Validity Study

arXiv:2605.02665v1 Announce Type: new Abstract: In Emotion Recognition in Conversations (ERC), model decisions should align with nuanced human perception and ideally provide insights on the classifica

GD-FPS: Growth-Driven Feedforward Parameter Selection for Efficient Fine-Tuning

arXiv:2510.27359v2 Announce Type: replace Abstract: Parameter-Efficient Fine-Tuning (PEFT) has emerged as a key strategy for adapting large-scale pre-trained models to downstream tasks, but existing a

Gen-Searcher: Reinforcing Agentic Search for Image Generation

arXiv:2603.28767v2 Announce Type: replace Abstract: Recent image generation models have shown strong capabilities in generating high-fidelity and photorealistic images. However, they are fundamentally

Generative Interfaces for Language Models

arXiv:2508.19227v3 Announce Type: replace Abstract: Large language models (LLMs) are increasingly seen as assistants, copilots, and consultants, capable of supporting a wide range of tasks through nat

Tutorials

Generative Modeling with Orbit-Space Particle Flow Matching

arXiv:2605.02222v1 Announce Type: cross Abstract: We present Orbit-Space Geometric Probability Paths (OGPP), a particle-native flow-matching framework for generative modeling of particle systems. OGPP

tutorialsarxiv-cs-cv