Search: “models”

Fine-tuning language encoding models on slow fMRI improves prediction for fast ECoG

arXiv:2605.19224v1 Announce Type: new Abstract: Neuroscientists have recently turned to intracranial brain recording methods, like electrocorticography (ECoG), for human experiments because of the fin

researcharxiv-cs-cl

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Hybrid Training for Vision-Language-Action Models

DGX agent

arXiv:2510.00600v2 Announce Type: replace-cross Abstract: Using Large Language Models to produce intermediate thoughts, a.k.a. Chain-of-thought (CoT), before providing an answer has been a successful

agentsarxiv-cs-ai

20 May 2026

Tutorials

Language models struggle with compartmentalization

arXiv:2605.19284v1 Announce Type: new Abstract: In the training data used by large language models (LLMs), the same latent concept is often presented in multiple distinct ways: the same facts appear i

tutorialsarxiv-cs-cl

Lightweight and Fast Backdoor Model Detection

arXiv:2605.18907v1 Announce Type: cross Abstract: Deep neural networks (DNN), despite their remarkable performance, are highly vulnerable to backdoor attacks. Existing defenses mainly rely on activati

Markov Chain Decoders Overcome the Heavy-Tail Limitations of Lipschitz Generative Models

arXiv:2605.18931v1 Announce Type: cross Abstract: Heavy-tailed distributions are prevalent in performance evaluation, network traffic, and risk modeling. This behavior poses a fundamental challenge fo

MSAlign: Aligning Molecule and Mass Spectra Foundation Models for Metabolite Identification

model-releasesarxiv-cs-lg

arXiv:2605.19752v1 Announce Type: new Abstract: Accurately identifying metabolites i.e. small molecules from mass spectrometry data remains a core challenge in metabolomics, with broad applications in

Quantized Machine Learning Models for Medical Imaging in Low-Resource Healthcare Settings

arXiv:2605.19207v1 Announce Type: cross Abstract: Deep learning models have shown strong performance in medical image analysis, but deploying them in low-resource clinical environments remains difficu

applicationsarxiv-cs-ai

Scaling Evaluation-time Compute with Reasoning Models as Evaluators

arXiv:2503.19877v2 Announce Type: replace Abstract: As language model (LM) outputs get more and more natural, it is becoming more difficult than ever to evaluate their quality. Simultaneously, increas

researcharxiv-cs-cl

Transformers Linearly Represent Highly Structured World Models

arXiv:2605.18847v1 Announce Type: cross Abstract: Do transformers, when trained on sequential reasoning traces, build internal models of the underlying task? And if so, does the structure of those int

When Preference Labels Fall Short: Aligning Diffusion Models from Real Data

arXiv:2605.19839v1 Announce Type: new Abstract: Preference alignment aims to guide generative models by learning from comparisons between preferred and non-preferred samples. In practice, most existin

safetyarxiv-cs-cv

When Tabular Foundation Models Meet Strategic Tabular Data: A Prior Alignment Approach

arXiv:2605.19662v1 Announce Type: new Abstract: Tabular foundation models based on pretrained prior-data fitted networks~(PFNs) have shown strong generalization on diverse tabular tasks, but they are

safetyarxiv-cs-ai

Adversarial Attacks on Downstream Weather Forecasting Models: Application to Tropical Cyclone Trajectory Prediction

arXiv:2510.10140v2 Announce Type: replace Abstract: Deep learning-based weather forecasting (DLWF) models leverage past weather observations to generate future forecasts, supporting a wide range of do

Attention Hijacking: Response Manipulation Across Queries in Vision-Language Models

arXiv:2605.17310v1 Announce Type: cross Abstract: Existing adversarial attacks on vision-language models (VLMs) can steer model outputs toward attacker-specified target responses, but their effectiven

Tutorials

CBT-Audio: Evaluating Audio Language Models for Patient-Side Distress Intensity Estimation in CBT Session Recordings

arXiv:2605.17370v1 Announce Type: new Abstract: Cognitive behavioural therapy is widely used to help patients understand and manage psychological distress. It is often delivered through spoken convers

tutorialsarxiv-cs-ai

CodeScaler: Scaling Code LLM Training and Test-Time Inference via Reward Models

arXiv:2602.17684v2 Announce Type: replace-cross Abstract: Reinforcement Learning from Verifiable Rewards (RLVR) has driven recent progress in code large language models by leveraging execution-based f

Cracks in the Foundation: A Civil Infrastructure Dataset to Challenge Vision Foundation Models

arXiv:2605.18413v1 Announce Type: new Abstract: Automated structural health monitoring is essential to prevent catastrophic infrastructure failures. Precise, pixel-level defect segmentation is needed

safetyarxiv-cs-cv

DACA-GRPO: Denoising-Aware Credit Assignment for Reinforcement Learning in Diffusion Language Models

arXiv:2605.16342v1 Announce Type: cross Abstract: Diffusion large language models are a compelling alternative to autoregressive models, yet existing RL methods for diffusion treat all denoising steps

safetyarxiv-cs-ai

DAD4TS: Data-Augmentation-Oriented Diffusion Model for Time-Series Forecasting with Small-Scale Data

arXiv:2605.17866v1 Announce Type: new Abstract: Small-scale data is a critical problem in time-series forecasting tasks. Data augmentation is an effective strategy for this task, but it has a limitati

applicationsarxiv-cs-lg

Data Presentation Over Architecture: Resampling Strategies for Credit Risk Prediction with Tabular Foundation Models

arXiv:2605.18635v1 Announce Type: cross Abstract: Credit default prediction is a tabular learning problem with severe class imbalance, heterogeneous features, and tight latency budgets. Tabular Founda

Designing streetscapes from street-view imagery using diffusion models

model-releasesarxiv-cs-cv

arXiv:2605.17527v1 Announce Type: new Abstract: Street-view imagery (SVI) is widely used to quantify key indicators of urban environment, such as green- ery, sky, or road view indices. However, existi

DeTrack: A Benchmark and Altitude-Aware Dual World Model for Drone-embodied Tracking

model-releasesarxiv-cs-cv

arXiv:2605.17451v1 Announce Type: new Abstract: Aerial object tracking has broad applications in public safety, emergency rescue, wildlife monitoring, and related fields. However, existing aerial trac

DocReward: A Document Reward Model for Structuring and Stylizing

arXiv:2510.11391v3 Announce Type: replace-cross Abstract: Recent agentic workflows automate professional document generation but focus narrowly on textual quality, overlooking structural and stylistic

Dynamic robotic cloth folding with efficient Koopman operator-based model predictive control

arXiv:2605.18373v1 Announce Type: cross Abstract: Robotic cloth folding is a challenging task, particularly when considering dynamic folding tasks, which aim at folding cloth by fast motions that leve

Employing Vision-Language Models for Face Image Quality Assessment

model-releasesarxiv-cs-cv

arXiv:2605.17489v1 Announce Type: new Abstract: Face Image Quality Assessment (FIQA) is a crucial control step in biometric pipelines. It ensures only reliable samples are processed to maintain system

EvoQRE: Modeling Bounded Rationality in Safety-Critical Traffic Simulation via Evolutionary Quantal Response Equilibrium

model-releasesarxiv-cs-ro

arXiv:2601.05653v2 Announce Type: replace Abstract: Existing traffic simulation frameworks for autonomous vehicles typically rely on imitation learning or game-theoretic approaches that solve for Nash

FAM-HRI: Foundation-Model Assisted Multi-Modal Human-Robot Interaction Combining Gaze and Speech

model-releasesarxiv-cs-ro

arXiv:2503.16492v3 Announce Type: replace-cross Abstract: ffective Human-Robot Interaction (HRI) is crucial for enhancing accessibility and usability in real-world robotics applications. However, exis

FediLoRA: Practical Federated Fine-Tuning of Foundation Models Under Missing-Modality Constraints

arXiv:2509.06984v3 Announce Type: replace-cross Abstract: Federated Learning with LoRA fine-tuning offers an efficient and privacy-aware solution for institutions to collaboratively leverage their lar

Agents

GEM: Gaussian Evolution Model for Occupancy Forecasting and Motion Planning

arXiv:2605.17682v1 Announce Type: new Abstract: Future 3D semantic occupancy forecasting and motion planning are central to autonomous driving, as they require models to reason about how surrounding s

agentsarxiv-cs-cv

GRaD-Nav++: Vision-Language Model Enabled Visual Drone Navigation with Gaussian Radiance Fields and Differentiable Dynamics

model-releasesarxiv-cs-ro

arXiv:2506.14009v2 Announce Type: replace Abstract: Autonomous drones capable of interpreting and executing high-level language instructions in unstructured environments remain a long-standing goal. Y

MCQ Difficulty Prediction via Modeling Learner Heterogeneity Using Data-Driven Cognitive Profiling

arXiv:2605.16290v1 Announce Type: cross Abstract: Predicting the difficulty of multiple-choice questions (MCQs) is important for effective assessment, yet current methods typically assume a unimodal s

Metric-Guided Feature Fusion of Visual Foundation Models for Segmentation Tasks

arXiv:2605.16864v1 Announce Type: cross Abstract: Although large-scale visual foundation models (VFMs) achieve remarkable performance in semantic understanding, they still underperform in instance-awa

Hardware

NanoQuant: Efficient Sub-1-Bit Quantization of Large Language Models

arXiv:2602.06694v2 Announce Type: replace Abstract: Weight-only quantization has become a standard approach for efficiently serving large language models (LLMs). However, existing methods fail to effi

hardwarearxiv-cs-lg

OrbiSim: World Models as Differentiable Physics Engines for Embodied Intelligence

arXiv:2605.16395v1 Announce Type: cross Abstract: We present OrbiSim, a novel robotic simulation paradigm that redefines world models as a fully differentiable physics engine for embodied intelligence

safetyarxiv-cs-lg

Self-supervised Hierarchical Visual Reasoning with World Model

arXiv:2605.17537v1 Announce Type: new Abstract: 3D open-world environments with adversarial opponents remain a core challenge for reinforcement learning due to their vast state spaces. Effective reaso

Agents

Task-Level AI Readiness Assessment for Business Process Management:The T-IPO Model and LARA Matrix in Financial-Services IT Operations

arXiv:2605.16297v1 Announce Type: cross Abstract: Which tasks inside an enterprise workflow can a large-language-model agent reliably handle, and under what conditions? Most business process modeling

agentsarxiv-cs-ai

Test-Time Hinting for Black-Box Vision-Language Models

arXiv:2605.16410v1 Announce Type: new Abstract: Test-time scaling (TTS) methods have proven highly effective for LLMs, yet their application to vision-language models (VLMs) remains relatively underex

researcharxiv-cs-cv

Traces of Social Competence in Large Language Models

arXiv:2603.04161v2 Announce Type: replace Abstract: The False Belief Test (FBT) has been the main method for assessing Theory of Mind (ToM) and related socio-cognitive competencies. For Large Language

applicationsarxiv-cs-cl

Training data attribution in diffusion models via mirrored unlearning and noise-consistent skew

arXiv:2605.17938v1 Announce Type: cross Abstract: Training data attribution (TDA) should enable generative model interpretability and foster a variety of related downstream tasks. Nonetheless, current

applicationsarxiv-cs-ai

Vision Foundation Models as Generalist Tokenizers for Image Generation

arXiv:2605.18390v1 Announce Type: new Abstract: In this work, we explore the largely unexplored direction of building a generalist image tokenizer directly on top of a frozen vision foundation model (

researcharxiv-cs-cv

WinQ: Accelerating Quantization-Aware Training of Language Models Around Saddle Points

arXiv:2605.17471v1 Announce Type: new Abstract: Quantization-aware training (QAT) is widely adopted to quantize language models by training full-precision weights using gradients from the quantized mo

ASRU: Activation Steering Meets Reinforcement Unlearning for Multimodal Large Language Models

arXiv:2605.15687v1 Announce Type: cross Abstract: Multimodal large language models (MLLMs) may memorize sensitive cross-modal information during pretraining, making machine unlearning (MU) crucial. Ex

safetyarxiv-cs-ai

Can Large Language Models Imitate Human Speech for Clinical Assessment? LLM-Driven Data Augmentation for Cognitive Score Prediction

model-releasesarxiv-cs-cl

arXiv:2605.16077v1 Announce Type: new Abstract: Accurate assessment of cognitive decline from spontaneous speech remains challenging due to limited dataset size and class imbalance. In this work, we p

CG-MLLM: Captioning and Generating 3D content via Multi-modal Large Language Models

arXiv:2601.21798v2 Announce Type: replace Abstract: Large Language Models(LLMs) have revolutionized text generation and multimodal perception,but their capabilities in 3D content generation remain und

researcharxiv-cs-cv

EgoExo-WM: Unlocking Exo Video for Ego World Models

arXiv:2605.15477v1 Announce Type: new Abstract: Egocentric world models present a promising direction for enabling agents to predict and plan, but their performance is constrained by the limited avail

safetyarxiv-cs-cv

Extrapolation Guarantees for Perturbation Modeling Under the Additive Latent Shift Assumption

arXiv:2504.18522v3 Announce Type: replace-cross Abstract: We consider the problem of modeling the effects of perturbations like gene knockouts on measurements such as single-cell RNA counts. Given dat

Few-Shot Large Language Models for Actionable Triage Categorization of Online Patient Inquiries

model-releasesarxiv-cs-cl

arXiv:2605.15680v1 Announce Type: new Abstract: Online patient inquiries are often informal, incomplete, and written before professional assessment, yet they must still be routed to an appropriate lev

Few-Step Diffusion Language Models via Trajectory Self-Distillation

arXiv:2602.12262v3 Announce Type: replace Abstract: Diffusion large language models (DLLMs) have emerged as powerful generative models with the promise of fast text generation through parallel decodin

researcharxiv-cs-cl

Large Language Models Could Be Rote Learners

arXiv:2504.08300v5 Announce Type: replace-cross Abstract: Benchmark-based evaluation, e.g., multiple-choice questions (MCQs) and open-ended questions (OEQs), is widely used for evaluating Large Langua