Search: “models”

Model-Free Robust Average-Reward Reinforcement Learning with Sample Complexity Analysis

arXiv:2505.12462v3 Announce Type: replace Abstract: Robust reinforcement learning (RL) under the average-reward criterion is essential for long-term decision-making, particularly when the environment

safetyarxiv-cs-lg

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Multi-AUV Marine Life Tracking with Single Hydrophone Payloads via a Hidden Markov Model Equipped Particle Filter

DGX agent

arXiv:2606.22335v1 Announce Type: new Abstract: Researchers tag and track marine animals to study migration patterns, human impacts on behavior, and behavioral shifts due to climate change. Accurate d

local-aiarxiv-cs-ro

23 Jun 2026

PeLAP-A: Adaptive Latent Pruning for Lightweight Latent Diffusion Models

arXiv:2606.23086v1 Announce Type: new Abstract: Latent diffusion models achieve strong generative performance by operating in a compressed latent space produced by a variational autoencoder (VAE). How

Policy-as-Data: Learning Generalizable HOI Diffusion Models from Simulated Physics

arXiv:2606.22806v1 Announce Type: new Abstract: Synthesizing realistic Human-Object Interactions (HOI) is critical for creating embodied avatars and functional virtual environments. However, current d

Protocol-Aware Tokenization and Architecture Co-Design for Wireless Packet Foundation Models

arXiv:2606.20587v1 Announce Type: cross Abstract: What matters more for building foundation models for wireless packet traces: the tokenizer or the architecture or both? To answer this question, we bu

RelightAnyone: A Generalized Relightable 3D Gaussian Head Model

arXiv:2601.03357v2 Announce Type: replace Abstract: 3D Gaussian Splatting (3DGS) has become a standard approach to reconstruct and render photorealistic 3D head avatars. A major challenge is to religh

Revisiting OmniAnomaly for Anomaly Detection: performance metrics and comparison with PCA-based models

arXiv:2603.18985v2 Announce Type: replace-cross Abstract: Deep learning models have become the dominant approach for multivariate time series anomaly detection (MTSAD), often reporting substantial per

Tutorials

Scalable Training of Spatially Grounded 2D Vision-Language Models for Radiology

arXiv:2606.20477v2 Announce Type: replace Abstract: We study how to train visually grounded vision-language models (VLMs) for radiology without manual spatial annotations. We introduce RefRad2D, a lar

tutorialsarxiv-cs-cv

Token Factory: Efficiently Integrating Diverse Signals into Large Recommendation Models

arXiv:2606.19635v2 Announce Type: replace-cross Abstract: Large Recommendation Models (LRMs) have demonstrated promising capabilities in industry-scale recommendation tasks. However, holistically inte

applicationsarxiv-cs-lg

TooBad: Backdoor Diffusion Models with Ultra-Low Poison Rate and Imperceptible Trigger

arXiv:2606.23362v1 Announce Type: cross Abstract: Diffusion models (DMs), despite their impressive capabilities across a wide range of generative tasks, have been shown to be vulnerable to backdoor at

researcharxiv-cs-cv

Training-Free Semantic Correction for Autoregressive Visual Models

arXiv:2606.22550v1 Announce Type: new Abstract: Autoregressive visual models (AVMs) based on next-scale prediction have emerged as a prominent paradigm for image and video synthesis. However, decompos

Vesta: A Generalist Embodied Reasoning Model

arXiv:2606.20905v1 Announce Type: new Abstract: Robots operating in open-world environments must seamlessly integrate localization, spatial reasoning, navigation, and long-horizon planning. While spec

local-aiarxiv-cs-ro

When Does a Video-Language Model Stop Watching? Reward Strength Controls the Formation and Reversal of Visual Shortcuts in Multimodal RLVR

arXiv:2606.22043v1 Announce Type: cross Abstract: Reinforcement learning with verifiable rewards (RLVR) is increasingly applied to large vision-language models (LVLMs), yet outcome-only optimization c

researcharxiv-cs-cv

Adapting Vision-Language Models from Iconic to Inclusive for Multi-Label Recognition Without Labels

arXiv:2606.11626v1 Announce Type: new Abstract: Understanding multi-label images remains a challenging task in computer vision. With the rapid progress of vision-language multimodal learning, vision-l

Agents

Automated Creativity Evaluation of Language Models Across Open-Ended Tasks

arXiv:2606.11762v1 Announce Type: cross Abstract: Large language models (LLMs) have achieved remarkable progress in language understanding, reasoning, and generation, sparking growing interest in thei

agentsarxiv-cs-ai

AVIS: Adaptive Test-Time Scaling for Vision-Language Models

arXiv:2606.11576v1 Announce Type: cross Abstract: Modern Vision-Language Models (VLMs) benefit from chain-of-thought prompting and test-time scaling, but these gains often come with prohibitive infere

Can AI Reason Like an Urban Planner? Benchmarking Large Language Models Against Professional Judgment

arXiv:2606.11678v1 Announce Type: new Abstract: Problem, Research Strategy, and Findings: The rise of large language models (LLMs) raises a key question for urban planning: which forms of professional

Detecting AI-Generated Content on Social Media with Multi-modal Language Models

arXiv:2606.11200v1 Announce Type: new Abstract: Generative AI has enabled the creation of photorealistic images and videos that are increasingly disseminated on social media, often used for spam, misi

applicationsarxiv-cs-cl

Detecting Sensitive Personal Information in Japanese Pre-Training Corpora for Large Language Models

arXiv:2606.12114v1 Announce Type: new Abstract: Sensitive personal information can appear in large-scale pre-training corpora for large language models (LLMs). Detecting and filtering such information

researcharxiv-cs-cl

Diffusion-based Cumulative Adversarial Purification for Vision Language Models

arXiv:2506.03933v2 Announce Type: replace-cross Abstract: Vision Language Models (VLMs) have shown remarkable capabilities in multimodal understanding, yet their susceptibility to adversarial perturba

applicationsarxiv-cs-ai

Evaluating Bias in Phoneme-Based Automatic Speech Recognition Systems: An Analysis of IPA Transcription Models

arXiv:2606.11639v1 Announce Type: new Abstract: The popularization of automatic speech recognition (ASR) systems has increased exploration of the demographic biases related to race, age, gender, and a

Language Shapes Mental Health Evaluations in Large Language Models

arXiv:2603.06910v2 Announce Type: replace Abstract: Multilingual large language models (LLMs) are increasingly used in socially sensitive mental health contexts, including support chatbots, screening,

researcharxiv-cs-cl

On the Optimal Reasoning Length for RL-Trained Language Models

arXiv:2602.09591v3 Announce Type: replace-cross Abstract: Reinforcement learning substantially improves reasoning in large language models, but it also tends to lengthen chain-of-thought outputs and i

Prediction-Powered Risk Monitoring of Deployed Models for Detecting Harmful Distribution Shifts

arXiv:2602.02229v2 Announce Type: replace Abstract: We study the problem of monitoring model performance in dynamic environments where labeled data are limited. To this end, we propose prediction-powe

Steering Where to Listen: Instruction-Based Activation Steering Redirects Temporal Attention in Large Audio-Language Models

arXiv:2606.11400v1 Announce Type: cross Abstract: Large Audio-Language Models (LALMs) excel at audio understanding but expose little about where in an audio signal they attend. We introduce instructio

An LLM-Native Psychometric Instrument Does Not Predict LLM Behavior: Evidence Across 25 Models

arXiv:2606.09843v1 Announce Type: cross Abstract: Large language models (LLMs) produce stable self-reports on personality inventories, but these self-reports do not predict observed behavior. Whether

Attention-Discounted Adaptive Sampler for Masked Diffusion Language Models

arXiv:2606.10829v1 Announce Type: cross Abstract: Masked diffusion language models can reduce inference steps by revealing multiple tokens per denoising iteration, but this parallelism is fragile: pos

Blind denoising diffusion models and the blessings of dimensionality

arXiv:2602.09639v2 Announce Type: replace Abstract: Denoising diffusion models (DDMs) are state-of-the-art methods for learning densities from data across numerous domains, yet many aspects of the tra

Agents

Business World Model

arXiv:2606.10044v1 Announce Type: new Abstract: Businesses are increasingly adopting AI-enabled tools to improve productivity, reduce costs, and enhance products and services. However, the transformat

agentsarxiv-cs-ai

Model Releases

Can Multi-Agent LLMs Identify Their Peers? Stylometric Fingerprinting in Role-Constrained Political Analysis

model-releasesarxiv-cs-ai

arXiv:2606.09854v1 Announce Type: cross Abstract: Multi-agent large language model (LLM) pipelines for political statement analysis are vulnerable to peer-preservation bias: models tend to protect pee

Deep Generative Model for Human Mobility Behavior

arXiv:2510.06473v3 Announce Type: replace-cross Abstract: Understanding and modeling human mobility is central to challenges in transport planning, sustainable urban design, and public health. Despite

Improving PET/CT-Based Whole-Body Lesion Segmentation Using Prediction Uncertainty-Augmented Models

arXiv:2606.10115v1 Announce Type: new Abstract: Accurate lesion segmentation from whole-body Positron Emission Tomography (PET)/Computed Tomography (CT) scans is essential for cancer staging and treat

local-aiarxiv-cs-cv

Learning the Universe: Posterior Reliability of Neural Generative Models in High-Dimensional Field-Level Inference of Cosmic Initial Conditions

arXiv:2606.10023v1 Announce Type: cross Abstract: Accurate posterior estimation is central to scientific inference, as uncertainties determine what can be reliably learned from observational data. Whi

applicationsarxiv-cs-lg

Mean Flow Distillation: Robust and Stable Distillation for Flow Matching Models

arXiv:2606.11155v1 Announce Type: new Abstract: Flow Matching models have demonstrated strong performance across a wide range of generative tasks. However, their reliance on ODE-based iterative sampli

Mechanistic Analysis of Alignment Algorithms in Language Models

arXiv:2606.09850v1 Announce Type: cross Abstract: Post-training alignment algorithms are predominantly evaluated as black boxes, obscuring how they reshape language models' internal computations. We p

Null-Space Constrained Low-Rank Adaptation for Response-Specified Large Language Model Unlearning

arXiv:2606.10989v1 Announce Type: new Abstract: Large language model unlearning aims to suppress designated undesirable knowledge while preserving benign capabilities. Many unlearning objectives focus

local-aiarxiv-cs-ai

Routing-Aware Expert Calibration for Machine Unlearning in Mixture-of-Experts Language Models

arXiv:2606.10338v1 Announce Type: cross Abstract: Machine unlearning is increasingly important for large language models, yet unlearning in Mixture-of-Experts (MoE) architectures remains underexplored

Scaling Self-Supervised Speech Models Uncovers Deep Linguistic Relationships: Evidence from the Pacific Cluster

arXiv:2603.07238v2 Announce Type: replace Abstract: Similarities between language representations derived from Self-Supervised Speech Models (S3Ms) have been observed to primarily reflect geographic p

researcharxiv-cs-cl

SpeechJBB: Probing Safety Alignment and Comprehension in Large Audio Language Models under Code-Switched Speech

arXiv:2606.06037v2 Announce Type: cross Abstract: Large audio language models (LALMs) are increasingly deployed in real-world applications, yet their safety alignment is still primarily evaluated on m

TacForeSight: Force-Guided Tactile World Model for Contact-Rich Manipulation

arXiv:2606.11184v1 Announce Type: new Abstract: Contact-rich manipulation requires robots to continuously perceive and regulate evolving physical interactions under dynamic contact transitions or comp

local-aiarxiv-cs-ro

Uncovering Vulnerability of Vision-Language-Action Models under Joint-Level Physical Faults

arXiv:2606.10501v1 Announce Type: new Abstract: Deploying Vision-Language-Action (VLA) models in real robotic systems requires robustness not only to semantic and perceptual variations, but also to em

safetyarxiv-cs-ro

Using Probabilistic Programs to Train Inductive Reasoning in Large Language Models

arXiv:2606.09856v1 Announce Type: cross Abstract: Post-training Large Language Models (LLMs) for reasoning typically focuses on deductive tasks such as mathematics and coding where correctness is veri

What Should a Skill Remember? Quality--Cost Trade-offs in Cost-Aware Skill Rewriting for Language Model Agents

arXiv:2606.09421v2 Announce Type: replace Abstract: Large language model agents increasingly rely on skills: reusable procedural documents encoding workflows, tool use, implementation patterns, valida

Whisper-GPT -- Continuous Discrete Hybrid Representation Language Models For Speech And Music

arXiv:2412.11449v2 Announce Type: replace-cross Abstract: We propose WHISPER-GPT: A generative large language model (LLM) for speech and music that allows us to work with continuous audio representati

WorldKernel: A World Model is the Coupling Kernel of Admissible Possible Worlds

arXiv:2606.10934v1 Announce Type: new Abstract: A common assumption holds that enough observational and interventional data, given to a strong enough predictor, suffices. We report a failure mode that

A Theoretical Analysis of Memory and Overfitting Phenomena in Stochastic Interpolation Models

arXiv:2606.08554v1 Announce Type: new Abstract: This paper provides a theoretical account of memorization in stochastic interpolation models. By leveraging closed-form expressions for the optimal velo

9 Jun 2026

Active Learning with Foundation Model Priors: Efficient Learning under Class Imbalance

arXiv:2606.07630v1 Announce Type: cross Abstract: Real-world datasets across image and text domains are often characterized by skewed class distributions and noisy annotations, which jointly degrade m

applicationsarxiv-cs-ai

9 Jun 2026

AMix-1: A Pathway to Test-Time Scalable Protein Foundation Model

arXiv:2507.08920v4 Announce Type: replace-cross Abstract: We introduce AMix-1, a powerful protein foundation model built on Bayesian Flow Networks and empowered by a systematic training methodology, e