Search: “evaluation”

GKnow: Measuring the Entanglement of Gender Bias and Factual Gender

model-releasesarxiv-cs-cl

arXiv:2605.12299v1 Announce Type: new Abstract: Recent works have analyzed the impact of individual components of neural networks on gendered predictions, often with a focus on mitigating gender bias.

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Gradient-Free Noise Optimization for Reward Alignment in Generative Models

DGX agent

arXiv:2605.11347v1 Announce Type: cross Abstract: Existing reward alignment methods for diffusion and flow models rely on multi-step stochastic trajectories, making them difficult to extend to determi

safetyarxiv-cs-cv

13 May 2026

GRAFT-ATHENA: Self-Improving Agentic Teams for Autonomous Discovery and Evolutionary Numerical Algorithms

arXiv:2605.11117v1 Announce Type: new Abstract: Scientific discovery can be modeled as a sequence of probabilistic decisions that map physical problems to numerical solutions. Recent agentic AI system

HamBR: Active Decision Boundary Restoration Based on Hamiltonian Dynamics for Learning with Noisy Labels

arXiv:2605.11383v1 Announce Type: new Abstract: In large-scale visual recognition and data mining tasks, the presence of noisy labels severely undermines the generalization capability of deep neural n

applicationsarxiv-cs-cv

Hi-GaTA: Hierarchical Gated Temporal Aggregation Adapter for Surgical Video Report Generation

arXiv:2605.11208v1 Announce Type: new Abstract: Automated, clinician-grade assessment reports for surgical procedures could reduce documentation burden and provide objective feedback, yet remain chall

Holder Policy Optimisation

arXiv:2605.12058v1 Announce Type: new Abstract: Group Relative Policy Optimisation (GRPO) enhances large language models by estimating advantages across a group of sampled trajectories. However, mappi

Images in Sentences: Scaling Interleaved Instructions for Unified Visual Generation

arXiv:2605.12305v1 Announce Type: new Abstract: While recent advancements in multimodal language models have enabled image generation from expressive multi-image instructions, existing methods struggl

safetyarxiv-cs-cv

Improving the Accuracy of Amortized Model Comparison with Self-Consistency

arXiv:2508.20614v3 Announce Type: replace-cross Abstract: Amortized Bayesian model comparison (BMC) enables fast probabilistic ranking of models via simulation-based training of neural surrogates. How

In-Context Multi-Objective Optimization

arXiv:2512.11114v2 Announce Type: replace Abstract: Balancing competing objectives is omnipresent across disciplines, from drug design to autonomous systems. Multi-objective Bayesian optimization is a

Instruct-ICL: Instruction-Guided In-Context Learning for Post-Disaster Damage Assessment

arXiv:2605.11439v1 Announce Type: new Abstract: Rapid and accurate situational awareness is essential for effective response during natural disasters, where delays in analysis can significantly hinder

applicationsarxiv-cs-cv

Instructions shape Production of Language, not Processing

arXiv:2605.11206v1 Announce Type: new Abstract: Instructions trigger a production-centered mechanism in language models. Through a cognitively inspired lens that separates language processing and prod

applicationsarxiv-cs-cl

Intrinsic Vicarious Conditioning for Deep Reinforcement Learning

arXiv:2605.12224v1 Announce Type: new Abstract: Advancements in reinforcement learning have produced a variety of complex and useful intrinsic driving forces; crucially, these drivers operate under a

Is Child-Directed Language Optimized for Word Learning? A Computational Study of Verb Meaning Acquisition

arXiv:2605.12047v1 Announce Type: new Abstract: Is child-directed language (CDL) optimized to support language learning, and which aspects of linguistic development does it facilitate? We investigate

Is Monotonic Sampling Necessary in Diffusion Models?

arXiv:2605.11773v1 Announce Type: new Abstract: Diffusion models generate samples by iteratively denoising a Gaussian prior, traversing a sequence of noise levels that, in every published sampler, dec

Keeping Score: Efficiency Improvements in Neural Likelihood Surrogate Training via Score-Augmented Loss Functions

arXiv:2605.12118v1 Announce Type: cross Abstract: For stochastic process models, parameter inference is often severely bottlenecked by computationally expensive likelihood functions. Simulation-based

Large Language Models for Causal Relations Extraction in Social Media: A Validation Framework for Disaster Intelligence

arXiv:2605.11348v1 Announce Type: new Abstract: During disasters, extracting causal relations from social media can strengthen situational awareness by identifying factors linked to casualties, physic

Learning Minimally Rigid Graphs with High Realization Counts

arXiv:2605.12427v1 Announce Type: new Abstract: For minimally rigid graphs, the same edge-length data can admit multiple realizations (up to translations and rotations). Finding graphs with exceptiona

Tutorials

Learning What Can Be Picked: Active Reachability Estimation for Efficient Robotic Fruit Harvesting

arXiv:2603.23679v2 Announce Type: replace Abstract: Agriculture remains a cornerstone of global health and economic sustainability, yet labor-intensive tasks such as harvesting high-value crops contin

tutorialsarxiv-cs-ro

Learning What Matters: Adaptive Information-Theoretic Objectives for Robot Exploration

arXiv:2605.12084v1 Announce Type: cross Abstract: Designing learnable information-theoretic objectives for robot exploration remains challenging. Such objectives aim to guide exploration toward data t

Lite3R: A Model-Agnostic Framework for Efficient Feed-Forward 3D Reconstruction

arXiv:2605.11354v1 Announce Type: new Abstract: Transformer-based 3D reconstruction has emerged as a powerful paradigm for recovering geometry and appearance from multi-view observations, offering str

Looking and Listening Inside and Outside: Multimodal Artificial Intelligence Systems for Driver Safety Assessment and Intelligent Vehicle Decision-Making

arXiv:2602.07668v2 Announce Type: replace Abstract: The looking-in-looking-out (LILO) framework has enabled intelligent vehicle applications that understand both the outside scene and the driver state

safetyarxiv-cs-cv

LucidNFT: LR-Anchored Multi-Reward Preference Optimization for Flow-Based Real-World Super-Resolution

arXiv:2603.05947v3 Announce Type: replace Abstract: Generative real-world image super-resolution (Real-ISR) can synthesize visually convincing details from severely degraded low-resolution (LR) inputs

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Agents

LychSim: A Controllable and Interactive Simulation Framework for Vision Research

arXiv:2605.12449v1 Announce Type: new Abstract: While self-supervised pretraining has reduced vision systems' reliance on synthetic data, simulation remains an indispensable tool for closed-loop optim

agentsarxiv-cs-cv

M^4-SAM: Multi-Modal Mixture-of-Experts with Memory-Augmented SAM for RGB-D Video Salient Object Detection

arXiv:2605.11760v1 Announce Type: new Abstract: The Segment Anything Model 2 (SAM2) has emerged as a foundation model for universal segmentation. Owing to its generalizable visual representations, SAM

applicationsarxiv-cs-cv

Machine Learning for neutron source distributions

arXiv:2605.12165v1 Announce Type: cross Abstract: In light of the recent advancements in machine learning, we propose a novel approach to neutron source distribution estimation through the utilisation

MajinBook: An open catalogue of digitally mediated world literature

arXiv:2511.11412v5 Announce Type: replace Abstract: This data paper introduces MajinBook, an open catalogue designed to facilitate the use of shadow libraries-such as Library Genesis and Z-Library-for

safetyarxiv-cs-cl

Martingale-Consistent Self-Supervised Learning

arXiv:2605.11846v1 Announce Type: new Abstract: Self-supervised learning (SSL) is often deployed under changing information, such as shorter histories, missing features, or partially observed images.

Measuring Accuracy and Energy-to-Solution of Quantum Fine-Tuning of Foundational AI Models

arXiv:2605.02798v1 Announce Type: cross Abstract: We present an experimental study of energy-to-solution (ETS) of hybrid quantum-classical applications, enabled by direct instrumentation of power cons

applicationsarxiv-cs-lg

MIST: Reliable Streaming Decision Trees for Online Class-Incremental Learning via McDiarmid Bound

arXiv:2605.11617v1 Announce Type: new Abstract: Streaming decision trees are natural candidates for open-world continual learning, as they perform local updates, enjoy bounded memory, and static decis

Mobile Traffic Camera Calibration from Road Geometry for UAV-Based Traffic Surveillance

arXiv:2605.11900v1 Announce Type: new Abstract: Unmanned aerial vehicles (UAVs) can provide flexible traffic surveillance where fixed roadside cameras are unavailable, costly, or impractical. However,

researcharxiv-cs-cv

Model-based Bootstrap of Controlled Markov Chains

arXiv:2605.12410v1 Announce Type: cross Abstract: We propose and analyze a model-based bootstrap for transition kernels in finite controlled Markov chains (CMCs) with possibly nonstationary or history

Molecular Design beyond Training Data with Novel Extended Objective Functionals of Generative AI Models Driven by Quantum Annealing Computer

arXiv:2602.15451v3 Announce Type: replace-cross Abstract: Deep generative modeling to stochastically design small molecules is an emerging technology for accelerating drug discovery and development. H

Much of Geospatial Web Search Is Beyond Traditional GIS

arXiv:2605.11336v1 Announce Type: cross Abstract: Web search queries concern place far more often than existing labelling schemes suggest, yet the landscape of geospatial web search queries - what peo

MULTI: Disentangling Camera Lens, Sensor, View, and Domain for Novel Image Generation

arXiv:2605.12134v1 Announce Type: new Abstract: Recent text-to-image models produce high-quality images, yet text ambiguity hinders precise control when specific styles or objects are required. There

Multi-Timescale Conductance Spiking Networks: A Sparse, Gradient-Trainable Framework with Rich Firing Dynamics for Enhanced Temporal Processing

arXiv:2605.11835v1 Announce Type: cross Abstract: Spiking neural networks (SNNs) promise low-power event-driven computation for temporally rich tasks, but commonly used neuron models often trade off g

Local Ai

Muon is Not That Special: Random or Inverted Spectra Work Just as Well

arXiv:2605.11181v1 Announce Type: new Abstract: The recent empirical success of the Muon optimizer has renewed interest in non-Euclidean optimization, typically justified by similarities with second-o

local-aiarxiv-cs-lg

NavOL: Navigation Policy with Online Imitation Learning

model-releasesarxiv-cs-ro

arXiv:2605.11762v1 Announce Type: new Abstract: Learning robust navigation policies remains a core challenge in robotics. Offline imitation learning suffers from distribution shift and compounding err

Local Ai

Neural-Schwarz Tiling for Geometry-Universal PDE Solving at Scale

arXiv:2605.12343v1 Announce Type: new Abstract: Most learned PDE solvers follow a global-surrogate paradigm: a neural operator is trained to map full problem descriptions to full solution fields for a

local-aiarxiv-cs-lg

Neural Statistical Functions

arXiv:2605.11327v1 Announce Type: new Abstract: Classical deep learning typically operates on individual cases. Despite its success, real-world usage often requires repeated inference to estimate stat

applicationsarxiv-cs-lg

No More, No Less: Task Alignment in Terminal Agents

arXiv:2605.12233v1 Announce Type: new Abstract: Terminal agents are increasingly capable of executing complex, long-horizon tasks autonomously from a single user prompt. To do so, they must interpret

Not Worth Mentioning? A Pilot Study on Salient Proposition Annotation

arXiv:2603.27358v2 Announce Type: replace Abstract: Despite a long tradition of work on extractive summarization, which by nature aims to recover the most important propositions in a text, little work

Offline Constrained Reinforcement Learning under Partial Data Coverage

arXiv:2505.17506v2 Announce Type: replace-cross Abstract: We study offline constrained reinforcement learning with general function approximation in discounted constrained Markov decision processes. P

OmniThoughtVis: A Scalable Distillation Pipeline for Deployable Multimodal Reasoning Models

arXiv:2605.11629v1 Announce Type: new Abstract: Recent multimodal large language models (MLLMs) have shown strong chain-of-thought (CoT) reasoning ability on vision-language tasks, but their direct de

applicationsarxiv-cs-cl

On Predicting the Post-training Potential of Pre-trained LLMs

arXiv:2605.11978v1 Announce Type: new Abstract: The performance of Large Language Models (LLMs) on downstream tasks is fundamentally constrained by the capabilities acquired during pre-training. Howev

Tutorials

On What We Can Learn from Low-Resolution Data

arXiv:2605.12168v1 Announce Type: new Abstract: Artificial intelligence systems typically rely on large, centrally collected datasets, a premise that does not hold in many real-world domains such as h

tutorialsarxiv-cs-lg

One Turn Too Late: Response-Aware Defense Against Hidden Malicious Intent in Multi-Turn Dialogue

arXiv:2605.05630v2 Announce Type: replace Abstract: Hidden malicious intent in multi-turn dialogue poses a growing threat to deployed large language models (LLMs). Rather than exposing a harmful objec

safetyarxiv-cs-cl

Overcoming Dynamics-Blindness: Training-Free Pace-and-Path Correction for VLA Models

arXiv:2605.11459v1 Announce Type: cross Abstract: Vision-Language-Action (VLA) models achieve remarkable flexibility and generalization beyond classical control paradigms. However, most prevailing VLA

Overtrained, Not Misaligned

arXiv:2605.12199v1 Announce Type: new Abstract: Emergent misalignment (EM), where fine-tuning on a narrow task (like insecure code) causes broad misalignment across unrelated domains, was first demons