Search: “evaluation”

OpenSpatial: A Principled Data Engine for Empowering Spatial Intelligence

arXiv:2604.07296v2 Announce Type: replace Abstract: Spatial understanding is a fundamental cornerstone of human-level intelligence. Nonetheless, current research predominantly focuses on domain-specif

applicationsarxiv-cs-cl

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ORACLE-SWE: Quantifying the Contribution of Oracle Information Signals on SWE Agents

DGX agent

arXiv:2604.07789v1 Announce Type: cross Abstract: Recent advances in language model (LM) agents have significantly improved automated software engineering (SWE). Prior work has proposed various agenti

agentsarxiv-cs-cl

10 Apr 2026

Orion-Lite: Distilling LLM Reasoning into Efficient Vision-Only Driving Models

arXiv:2604.08266v1 Announce Type: new Abstract: Leveraging the general world knowledge of Large Language Models (LLMs) holds significant promise for improving the ability of autonomous driving systems

Path Regularization: A Near-Complete and Optimal Nonasymptotic Generalization Theory for Multilayer Neural Networks and Double Descent Phenomenon

arXiv:2503.02129v2 Announce Type: replace-cross Abstract: Path regularization has shown to be a very effective regularization to train neural networks, leading to a better generalization property than

Personalizing Text-to-Image Generation to Individual Taste

arXiv:2604.07427v1 Announce Type: new Abstract: Modern text-to-image (T2I) models generate high-fidelity visuals but remain indifferent to individual user preferences. While existing reward models opt

safetyarxiv-cs-cv

PhyEdit: Towards Real-World Object Manipulation via Physically-Grounded Image Editing

arXiv:2604.07230v2 Announce Type: replace Abstract: Achieving physically accurate object manipulation in image editing is essential for its potential applications in interactive world models. However,

PIKA: Expert-Level Synthetic Datasets for Post-Training Alignment from Scratch

arXiv:2510.06670v2 Announce Type: replace Abstract: High-quality instruction data is critical for LLM alignment, yet existing open-source datasets often lack efficiency, requiring hundreds of thousand

PrivFedTalk: Privacy-Aware Federated Diffusion with Identity-Stable Adapters for Personalized Talking-Head Generation

arXiv:2604.08037v1 Announce Type: cross Abstract: Talking-head generation has advanced rapidly with diffusion-based generative models, but training usually depends on centralized face-video and speech

Prompt reinforcing for long-term planning of large language models

arXiv:2510.05921v2 Announce Type: replace Abstract: Large language models (LLMs) have achieved remarkable success in a wide range of natural language processing tasks and can be adapted through prompt

ProofSketcher: Hybrid LLM + Lightweight Proof Checker for Reliable Math/Logic Reasoning

arXiv:2604.06401v1 Announce Type: new Abstract: The large language models (LLMs) might produce a persuasive argument within mathematical and logical fields, although such argument often includes some

researcharxiv-cs-ai

Prune-Quantize-Distill: An Ordered Pipeline for Efficient Neural Network Compression

arXiv:2604.04988v1 Announce Type: cross Abstract: Modern deployment often requires trading accuracy for efficiency under tight CPU and memory constraints, yet common compression proxies such as parame

Local Ai

Quantifying Explanation Consistency: The C-Score Metric for CAM-Based Explainability in Medical Image Classification

arXiv:2604.08502v1 Announce Type: new Abstract: Class Activation Mapping (CAM) methods are widely used to generate visual explanations for deep learning classifiers in medical imaging. However, existi

local-aiarxiv-cs-cv

Reasoning-Based Refinement of Unsupervised Text Clusters with LLMs

arXiv:2604.07562v1 Announce Type: new Abstract: Unsupervised methods are widely used to induce latent semantic structure from large text collections, yet their outputs often contain incoherent, redund

applicationsarxiv-cs-cl

Replacing Tunable Parameters in Weather and Climate Models with State-Dependent Functions using Reinforcement Learning

model-releasesarxiv-cs-lg

arXiv:2601.04268v2 Announce Type: replace Abstract: Weather and climate models rely on parametrisations to represent unresolved sub-grid processes. Traditional schemes rely on fixed coefficients that

Reset-Free Reinforcement Learning for Real-World Agile Driving: An Empirical Study

arXiv:2604.07672v1 Announce Type: new Abstract: This paper presents an empirical study of reset-free reinforcement learning (RL) for real-world agile driving, in which a physical 1/10-scale vehicle le

safetyarxiv-cs-ro

Safe Large-Scale Robust Nonlinear MPC in Milliseconds via Reachability-Constrained System Level Synthesis on the GPU

arXiv:2604.07644v1 Announce Type: new Abstract: We present GPU-SLS, a GPU-parallelized framework for safe, robust nonlinear model predictive control (MPC) that scales to high-dimensional uncertain rob

safetyarxiv-cs-ro

SALLIE: Safeguarding Against Latent Language & Image Exploits

model-releasesarxiv-cs-ai

arXiv:2604.06247v1 Announce Type: cross Abstract: Large Language Models (LLMs) and Vision-Language Models (VLMs) remain highly vulnerable to textual and visual jailbreaks, as well as prompt injections

sciwrite-lint: Verification Infrastructure for the Age of Science Vibe-Writing

arXiv:2604.08501v1 Announce Type: cross Abstract: Science currently offers two options for quality assurance, both inadequate. Journal gatekeeping claims to verify both integrity and contribution, but

Self-Debias: Self-correcting for Debiasing Large Language Models

arXiv:2604.08243v1 Announce Type: new Abstract: Although Large Language Models (LLMs) demonstrate remarkable reasoning capabilities, inherent social biases often cascade throughout the Chain-of-Though

safetyarxiv-cs-cl

Sell More, Play Less: Benchmarking LLM Realistic Selling Skill

arXiv:2604.07054v2 Announce Type: replace Abstract: Sales dialogues require multi-turn, goal-directed persuasion under asymmetric incentives, which makes them a challenging setting for large language

Spatiotemporal Gaussian representation-based dynamic reconstruction and motion estimation framework for time-resolved volumetric MR imaging (DREME-GSMR)

arXiv:2604.06482v1 Announce Type: cross Abstract: Time-resolved volumetric MR imaging that reconstructs a 3D MRI within sub-seconds to resolve deformable motion is essential for motion-adaptive radiot

researcharxiv-cs-lg

Stop Listening to Me! How Multi-turn Conversations Can Degrade LLM Diagnostic Reasoning

arXiv:2603.11394v2 Announce Type: replace Abstract: Patients and clinicians are increasingly using chatbots powered by large language models (LLMs) for healthcare inquiries. While state-of-the-art LLM

applicationsarxiv-cs-cl

STRIDE-ED: A Strategy-Grounded Stepwise Reasoning Framework for Empathetic Dialogue Systems

arXiv:2604.07100v1 Announce Type: cross Abstract: Empathetic dialogue requires not only recognizing a user's emotional state but also making strategy-aware, context-sensitive decisions throughout resp

researcharxiv-cs-ai

T-Gated Adapter: A Lightweight Temporal Adapter for Vision-Language Medical Segmentation

arXiv:2604.08167v1 Announce Type: new Abstract: Medical image segmentation traditionally relies on fully supervised 3D architectures that demand a large amount of dense, voxel-level annotations from c

The Unreasonable Effectiveness of Data for Recommender Systems

arXiv:2604.06420v2 Announce Type: cross Abstract: In recommender systems, collecting, storing, and processing large-scale interaction data is increasingly costly in terms of time, energy, and computat

researcharxiv-cs-lg

Toward a Tractability Frontier for Exact Relevance Certification

arXiv:2604.07349v1 Announce Type: cross Abstract: Exact relevance certification asks which coordinates are necessary to determine the optimal action in a coordinate-structured decision problem. The tr

researcharxiv-cs-ai

Toward Memory-Aided World Models: Benchmarking via Spatial Consistency

model-releasesarxiv-cs-ai

arXiv:2505.22976v2 Announce Type: replace-cross Abstract: The ability to simulate the world in a spatially consistent manner is a crucial requirements for effective world models. Such a model enables

Towards Privacy-Preserving Large Language Model: Text-free Inference Through Alignment and Adaptation

arXiv:2604.06831v1 Announce Type: cross Abstract: Current LLM-based services typically require users to submit raw text regardless of its sensitivity. While intuitive, such practice introduces substan

Towards Robust Content Watermarking Against Removal and Forgery Attacks

arXiv:2604.06662v1 Announce Type: cross Abstract: Generated contents have raised serious concerns about copyright protection, image provenance, and credit attribution. A potential solution for these p

researcharxiv-cs-lg

Towards the Development of an LLM-Based Methodology for Automated Security Profiling in Compliance with Ukrainian Cybersecurity Regulations

arXiv:2604.06274v1 Announce Type: cross Abstract: In recent years, the pace of development of information technology in various areas has increased drastically, forcing cybersecurity specialists to co

Training Data Size Sensitivity in Unsupervised Rhyme Recognition

arXiv:2604.08156v1 Announce Type: new Abstract: Rhyme is deceptively intuitive: what is or is not a rhyme is constructed historically, scholars struggle with rhyme classification, and people disagree

Video Parallel Scaling: Aggregating Diverse Frame Subsets for VideoLLMs

arXiv:2509.08016v2 Announce Type: replace Abstract: Video Large Language Models (VideoLLMs) face a critical bottleneck: increasing the number of input frames to capture fine-grained temporal detail le

Vision-Language Foundation Models for Comprehensive Automated Pavement Condition Assessment

arXiv:2604.08212v1 Announce Type: new Abstract: General-purpose vision-language models demonstrate strong performance in everyday domains but struggle with specialized technical fields requiring preci

applicationsarxiv-cs-cv

Visual prompting reimagined: The power of the Activation Prompts

model-releasesarxiv-cs-lg

arXiv:2604.06440v1 Announce Type: cross Abstract: Visual prompting (VP) has emerged as a popular method to repurpose pretrained vision models for adaptation to downstream tasks. Unlike conventional mo

WASD: Locating Critical Neurons as Sufficient Conditions for Explaining and Controlling LLM Behavior

arXiv:2603.18474v2 Announce Type: replace Abstract: Precise behavioral control of large language models (LLMs) is critical for complex applications. However, existing methods often incur high training

WebExpert: domain-aware web agents with critic-guided expert experience for high-precision search

arXiv:2604.06177v1 Announce Type: cross Abstract: Specialized web tasks in finance, biomedicine, and pharmaceuticals remain challenging due to missing domain priors: queries drift, evidence is noisy,

What Makes an Ideal Quote? Recommending 'Unexpected yet Rational' Quotations via Novelty

arXiv:2602.22220v2 Announce Type: replace-cross Abstract: Quotation recommendation aims to enrich writing by suggesting quotes that complement a given context, yet existing systems mostly optimize sur

3DTCR: Vortex-following generative reconstruction improves tropical cyclone intensity prediction from global AI weather forecasts

arXiv:2603.13049v3 Announce Type: replace Abstract: Global numerical weather prediction (NWP) and artificial intelligence (AI) models often underestimate tropical cyclone (TC) intensity, partly becaus

safetyarxiv-cs-lg

Local Ai

A Black-Box Positioning Solution for Effortless Integration in Wearable Navigation

arXiv:2610.21353v1 Announce Type: new Abstract: Accurate localization is critical for a wide range of applications, including navigation, augmented reality, safety management, and healthcare. However,

local-aiarxiv-cs-ro

A Distribution-to-Distribution Neural Probabilistic Forecasting Framework for Dynamical Systems

model-releasesarxiv-cs-lg

arXiv:2603.25370v2 Announce Type: replace-cross Abstract: Probabilistic forecasts provide a principled representation of forecast uncertainty through predictive distributions. Directly propagating pre

A large-scale climate-aware satellite image dataset for domain adaptive land-cover semantic segmentation

arXiv:2610.21238v1 Announce Type: new Abstract: A few well-annotated datasets for land-cover semantic segmentation have recently been introduced to advance the field of earth observation technologies.

researcharxiv-cs-cv

A Large-Scale Dataset for Multi-Class Financial News Classification in Brazilian Portuguese

arXiv:2610.20630v1 Announce Type: new Abstract: The development of Financial Natural Language Processing (NLP) systems for Brazilian Portuguese is hindered by the scarcity of domain-specific datasets.

researcharxiv-cs-cl

A Multi-Tiered AI Accessibility Model (MAIAM): Proportionate Access and Capability Limits for Comprehensive Frontier AI Safety

arXiv:2610.20921v1 Announce Type: new Abstract: Most debate about releasing capable AI systems treats the choice as binary: keep the model behind an API, or publish the weights. That framing runs toge

AdaLoop: Thinking with the Vision Encoder

arXiv:2610.21323v1 Announce Type: new Abstract: Most vision-language models (VLMs) treat the vision encoder as a static feature extractor: it encodes an image once, independently of the question, and

researcharxiv-cs-cv

AgentEval: Evidence-Bounded Assessment of Stateful Agents

model-releasesarxiv-cs-ai

arXiv:2610.21177v1 Announce Type: new Abstract: Task completion alone does not determine whether a stateful agent satisfied its obligations, and a recorded judgment does not guarantee that its support

Agents

AI Scientists for Building Virtual-Cell Models

arXiv:2508.02276v3 Announce Type: replace-cross Abstract: Autonomous agents, from virtual laboratories to biological data-analysis agents and co-scientists, have begun to turn scientific goals into hy

agentsarxiv-cs-ai

AlignSAE: Concept-Aligned Sparse Autoencoders

arXiv:2512.02004v4 Announce Type: replace-cross Abstract: Large Language Models (LLMs) encode factual knowledge within hidden parametric spaces that are difficult to inspect or control. While Sparse A

researcharxiv-cs-cl

Agents

An Agentic Framework for Proactive Robots with Procedural Assistance Memory

arXiv:2610.21211v1 Announce Type: new Abstract: Proactive robots should anticipate user needs and provide assistance before being explicitly asked. A straightforward approach is to infer human intenti

agentsarxiv-cs-ro