Search: “evaluation”

ARC-AGI-3: A New Challenge for Frontier Agentic Intelligence

arXiv:2603.24621v2 Announce Type: replace Abstract: We introduce ARC-AGI-3, an interactive benchmark for studying agentic intelligence through novel, abstract, turn-based environments in which agents

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Art3D: Training-Free 3D Generation from Flat-Colored Illustration

DGX agent

arXiv:2504.10466v2 Announce Type: replace Abstract: Large-scale pre-trained image-to-3D generative models have exhibited remarkable capabilities in diverse shape generations. However, most of them str

model-releasesarxiv-cs-cv

20 Apr 2026

AST: Adaptive, Seamless, and Training-Free Precise Speech Editing

arXiv:2604.16056v1 Announce Type: cross Abstract: Text-based speech editing aims to modify specific segments while preserving speaker identity and acoustic context. Existing methods rely on task-speci

Safety

AutoDrive-R^2: Incentivizing Reasoning and Self-Reflection Capacity for VLA Model in Autonomous Driving

arXiv:2509.01944v3 Announce Type: replace-cross Abstract: Vision-Language-Action (VLA) models in autonomous driving systems have recently demonstrated transformative potential by integrating multimoda

safetyarxiv-cs-cv

Automating Crash Diagram Generation Using Vision-Language Models: A Case Study on Multi-Lane Roundabouts

arXiv:2604.15332v1 Announce Type: cross Abstract: Crash diagrams are essential tools in transportation safety analysis, yet their manual preparation remains time-consuming and prone to human variabili

Beyond a Single Frame: Multi-Frame Spatially Grounded Reasoning Across Volumetric MRI

arXiv:2604.15808v1 Announce Type: cross Abstract: Spatial reasoning and visual grounding are core capabilities for vision-language models (VLMs), yet most medical VLMs produce predictions without tran

Beyond MCQ: An Open-Ended Arabic Cultural QA Benchmark with Dialect Variants

arXiv:2510.24328v2 Announce Type: replace-cross Abstract: Large Language Models (LLMs) are increasingly used to answer everyday questions, yet their performance on culturally grounded and dialectal co

Applications

Beyond Passive Viewing: A Pilot Study of a Hybrid Learning Platform Augmenting Video Lectures with Conversational AI

arXiv:2604.15334v1 Announce Type: cross Abstract: The exponential growth of AI education has brought millions of learners to online platforms, yet this massive scale has simultaneously exposed critica

applicationsarxiv-cs-ai

Beyond Text Prompts: Precise Concept Erasure through Text-Image Collaboration

arXiv:2604.15829v1 Announce Type: new Abstract: Text-to-image generative models have achieved impressive fidelity and diversity, but can inadvertently produce unsafe or undesirable content due to impl

Bilevel Optimization of Agent Skills via Monte Carlo Tree Search

arXiv:2604.15709v1 Announce Type: new Abstract: Agent exttt{skills} are structured collections of instructions, tools, and supporting resources that help large language model (LLM) agents perform part

Brain Score Tracks Shared Properties of Languages: Evidence from Many Natural Languages and Structured Sequences

arXiv:2604.15503v1 Announce Type: new Abstract: Recent breakthroughs in language models (LMs) using neural networks have raised the question: how similar are these models' processing to human language

Breakout-picker: Reducing false positives in deep learning-based borehole breakout characterization from acoustic image logs

arXiv:2604.16011v1 Announce Type: new Abstract: Borehole breakouts are stress-induced spalling on the borehole wall, which are identifiable in acoustic image logs as paired zones with near-symmetry az

Tutorials

Bridging the phenotype-target gap for molecular generation via multi-objective reinforcement learning

arXiv:2509.21010v2 Announce Type: replace-cross Abstract: The de novo generation of drug-like molecules capable of inducing desirable phenotypic changes is receiving increasing attention. However, pre

tutorialsarxiv-cs-ai

Can we AI our way to a more sustainable world?

researchmicrosoft-research

Doug Burger, sustainability expert Amy Luers, and optimization researcher Ishai Menache examine the global emissions implications of datacenter operations, efficiency gains, and AI's potential across

Chain-of-Thought Degrades Visual Spatial Reasoning Capabilities of Multimodal LLMs

arXiv:2604.16060v1 Announce Type: cross Abstract: Multimodal Reasoning Models (MRMs) leveraging Chain-of-Thought (CoT) based thinking have revolutionized mathematical and logical problem-solving. Howe

ChemAmp: Amplified Chemistry Tools via Composable Agents

arXiv:2505.21569v3 Announce Type: replace-cross Abstract: Although LLM-based agents are proven to master tool orchestration in scientific fields, particularly chemistry, their single-task performance

CHOP: Chunkwise Context-Preserving Framework for RAG on Multi Documents

model-releasesarxiv-cs-cl

arXiv:2604.15802v1 Announce Type: new Abstract: Retrieval-Augmented Generation (RAG) systems lose retrieval accuracy when similar documents coexist in the vector database, causing unnecessary informat

CLIMB: Controllable Longitudinal Brain Image Generation using Mamba-based Latent Diffusion Model and Gaussian-aligned Autoencoder

arXiv:2604.15611v1 Announce Type: cross Abstract: Latent diffusion models have emerged as powerful generative models in medical imaging, enabling the synthesis of high quality brain magnetic resonance

COEVO: Co-Evolutionary Framework for Joint Functional Correctness and PPA Optimization in LLM-Based RTL Generation

arXiv:2604.15001v2 Announce Type: replace Abstract: LLM-based RTL code generation methods increasingly target both functional correctness and PPA quality, yet existing approaches universally decouple

CollideNet: Hierarchical Multi-scale Video Representation Learning with Disentanglement for Time-To-Collision Forecasting

arXiv:2604.16240v1 Announce Type: new Abstract: Time-to-Collision (TTC) forecasting is a critical task in collision prevention, requiring precise temporal prediction and comprehending both local and g

Consistency Analysis of Sentiment Predictions using Syntactic & Semantic Context Assessment Summarization (SSAS)

arXiv:2604.15547v1 Announce Type: cross Abstract: The fundamental challenge of using Large Language Models (LLMs) for reliable, enterprise-grade analytics, such as sentiment prediction, is the conflic

Safety

Contact-Aware Planning and Control of Continuum Robots in Highly Constrained Environments

arXiv:2604.15638v1 Announce Type: new Abstract: Continuum robots are well suited for navigating confined and fragile environments, such as vascular or endoluminal anatomy, where contact with surroundi

safetyarxiv-cs-ro

Context-Agent: Dynamic Discourse Trees for Non-Linear Dialogue

arXiv:2604.05552v2 Announce Type: replace-cross Abstract: Large Language Models demonstrate outstanding performance in many language tasks but still face fundamental challenges in managing the non-lin

Cost-Aware Model Orchestration for LLM-based Systems

arXiv:2512.01099v2 Announce Type: replace Abstract: As modern artificial intelligence (AI) systems become more advanced and capable, they can leverage a wide range of tools and models to perform compl

COVER:COverage-VErified Roadmaps for Fixed-time Motion Planning in Continuous Semi-Static Environments

arXiv:2510.03875v2 Announce Type: replace Abstract: The ability to solve motion-planning queries within a fixed time budget is critical for deploying robotic systems in time-sensitive applications. Se

researcharxiv-cs-ro

CSLE: A Reinforcement Learning Platform for Autonomous Security Management

arXiv:2604.15590v1 Announce Type: cross Abstract: Reinforcement learning is a promising approach to autonomous and adaptive security management in networked systems. However, current reinforcement lea

Local Ai

cuNNQS-SCI: A Fully GPU-Accelerated Framework for High-Performance Configuration Interaction Selection withNeural Network QQantum States

arXiv:2604.15768v1 Announce Type: cross Abstract: AI-driven methods have demonstrated considerable success in tackling the central challenge of accurately solving the Schrodinger equation for complex

local-aiarxiv-cs-ai

Curing Miracle Steps in LLM Mathematical Reasoning with Rubric Rewards

arXiv:2510.07774v3 Announce Type: replace Abstract: In this paper, we observe that current models are susceptible to reward hacking, leading to a substantial overestimation of a model's reasoning abil

DALM: A Domain-Algebraic Language Model via Three-Phase Structured Generation

arXiv:2604.15593v1 Announce Type: cross Abstract: Large language models compress heterogeneous knowledge into a single parameter space, allowing facts from different domains to interfere during genera

Local Ai

Deep Learning-Enabled Modality Transfer Between Independent Microscopes for High-Throughput Imaging

arXiv:2403.18026v2 Announce Type: replace-cross Abstract: High-throughput biological imaging is often constrained by a trade-off between acquisition speed and image quality. Fast imaging modalities, s

local-aiarxiv-cs-lg

DenTab: A Dataset for Table Recognition and Visual QA on Real-World Dental Estimates

model-releasesarxiv-cs-cv

arXiv:2604.16099v1 Announce Type: new Abstract: Tables condense key transactional and administrative information into compact layouts, but practical extraction requires more than text recognition: sys

Applications

Designing Synthetic Discussion Generation Systems: A Case Study for Online Facilitation

arXiv:2503.16505v4 Announce Type: replace-cross Abstract: A critical challenge in social science research is the high cost associated with experiments involving human participants. We identify Synthet

applicationsarxiv-cs-cl

Tutorials

Diffusion Autoencoder for Unsupervised Artifact Restoration in Handheld Fundus Images

arXiv:2604.15723v1 Announce Type: cross Abstract: The advent of handheld fundus imaging devices has made ophthalmologic diagnosis and disease screening more accessible, efficient, and cost-effective.

tutorialsarxiv-cs-ai

Do LLMs Really Know What They Don't Know? Internal States Mainly Reflect Knowledge Recall Rather Than Truthfulness

arXiv:2510.09033v3 Announce Type: replace Abstract: Recent work suggests that LLMs 'know what they don't know', positing that hallucinated and factually correct outputs arise from distinct internal pr

DPrivBench: Benchmarking LLMs' Reasoning for Differential Privacy

arXiv:2604.15851v1 Announce Type: cross Abstract: Differential privacy (DP) has a wide range of applications for protecting data privacy, but designing and verifying DP algorithms requires expert-leve

DTEA: A Dual-Topology Elastic Actuator Enabling Real-Time Switching Between Series and Parallel Compliance

arXiv:2604.15865v1 Announce Type: new Abstract: Series and parallel elastic actuators offer complementary but mutually exclusive advantages, yet no existing actuator enables real-time transition betwe

researcharxiv-cs-ro

DVP-MVS++: Synergize Depth-Normal-Edge and Harmonized Visibility Prior for Multi-View Stereo

arXiv:2506.13215v2 Announce Type: replace Abstract: Recently, patch deformation-based methods have demonstrated significant effectiveness in multi-view stereo due to their incorporation of deformable

ECG-Lens: Benchmarking ML & DL Models on PTB-XL Dataset

arXiv:2604.15822v1 Announce Type: cross Abstract: Automated classification of electrocardiogram (ECG) signals is a useful tool for diagnosing and monitoring cardiovascular diseases. This study compare

EnvScaler: Scaling Tool-Interactive Environments for LLM Agent via Programmatic Synthesis

arXiv:2601.05808v2 Announce Type: replace-cross Abstract: Large language models (LLMs) are expected to be trained to act as agents in various real-world environments, but this process relies on rich a

EVIL: Evolving Interpretable Algorithms for Zero-Shot Inference on Event Sequences and Time Series with LLMs

arXiv:2604.15787v1 Announce Type: cross Abstract: We introduce EVIL (extbf{EV}olving extbf{I}nterpretable algorithms with extbf{L}LMs), an approach that uses LLM-guided evolutionary search to discover

EvoTest: Evolutionary Test-Time Learning for Self-Improving Agentic Systems

arXiv:2510.13220v2 Announce Type: replace Abstract: A fundamental limitation of current AI agents is their inability to learn complex skills on the fly at test time, often behaving like 'clever but cl

Exascale Multi-Task Graph Foundation Models for Imbalanced, Multi-Fidelity Atomistic Data

arXiv:2604.15380v1 Announce Type: cross Abstract: We present an exascale workflow for materials discovery using atomistic graph foundation models built on HydraGNN. We jointly train on 16 open first-p

Experience Compression Spectrum: Unifying Memory, Skills, and Rules in LLM Agents

arXiv:2604.15877v1 Announce Type: new Abstract: As LLM agents scale to long-horizon, multi-session deployments, efficiently managing accumulated experience becomes a critical bottleneck. Agent memory

Explainable Iterative Data Visualisation Refinement via an LLM Agent

arXiv:2604.15319v1 Announce Type: cross Abstract: Exploratory analysis of high-dimensional data relies on embedding the data into a low-dimensional space (typically 2D or 3D), based on which visualiza

Exploring the Capability Boundaries of LLMs in Mastering of Chinese Chouxiang Language

model-releasesarxiv-cs-cl

arXiv:2604.15841v1 Announce Type: new Abstract: While large language models (LLMs) have achieved remarkable success in general language tasks, their performance on Chouxiang Language, a representative

Factor Graph-Based Shape Estimation for Continuum Robots via Magnus Expansion

arXiv:2604.15619v1 Announce Type: new Abstract: Reconstructing the shape of continuum manipulators from sparse, noisy sensor data is a challenging task, owing to the infinite-dimensional nature of suc

researcharxiv-cs-ro

FACTS: Table Summarization via Offline Template Generation with Agentic Workflows

arXiv:2510.13920v2 Announce Type: replace Abstract: Query-focused table summarization requires generating natural language summaries of tabular data conditioned on a user query, enabling users to acce

agentsarxiv-cs-cl

Faithfulness-Aware Uncertainty Quantification for Fact-Checking the Output of Retrieval Augmented Generation

arXiv:2505.21072v4 Announce Type: replace Abstract: Large Language Models (LLMs) enhanced with retrieval, an approach known as Retrieval-Augmented Generation (RAG), have achieved strong performance in