Search: “models”

The Rank-One Corner: How Much Value Equivalence Does a Task Need from a World Model?

arXiv:2607.06640v1 Announce Type: cross Abstract: A learned world model is usually judged by how faithfully it reconstructs its observations or predicts reward, as though quality were something the mo

9 Jul 2026

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A Definition and Roadmap for World Models

DGX agent

arXiv:2607.06401v1 Announce Type: new Abstract: World models -- internal simulators that learn the structure and dynamics of an environment -- have become one of the most actively debated concepts in

tutorialsarxiv-cs-ai

8 Jul 2026

CAIRN: Cross-Room 3D Scene Understanding with Topology-Aware Large Multimodal Models

arXiv:2607.06534v1 Announce Type: new Abstract: Existing 3D scene-grounded Large Language Models (3D-LLMs) focus on answering questions grounded in simplified single-room 3D scenes, lacking the abilit

Data Analysis in the Wild: Benchmarking Large Language Models Against Real-World Data Complexities

arXiv:2607.06482v1 Announce Type: cross Abstract: Current benchmarks for evaluating Large Language Models (LLMs) in data analysis often fail to reflect real-world settings. They typically focus on fac

Multi-Teacher Contrastive Distillation for Edge-Efficient Pathology Foundation Models

arXiv:2607.05533v1 Announce Type: new Abstract: Computational pathology foundation models (PFMs) have advanced whole-slide image analysis. However, their size and inference cost hinder local deploymen

Nemotron-Labs-Diffusion: A Tri-Mode Language Model Unifying Autoregressive, Diffusion, and Self-Speculation Decoding

arXiv:2607.05722v1 Announce Type: new Abstract: We introduce Nemotron-Labs-Diffusion, a tri-mode language model (LM) that unifies AR, diffusion, and self-speculation decoding within a single architect

SpanUQ: Span-Level Uncertainty Quantification for Large Language Model Generation

arXiv:2607.05721v1 Announce Type: new Abstract: Uncertainty estimation is essential not only for the trustworthy deployment of large language models (LLMs) but also as a foundation for self-refinement

ZeroBench: An Impossible Visual Benchmark for Contemporary Large Multimodal Models

arXiv:2502.09696v3 Announce Type: replace Abstract: Large Multimodal Models (LMMs) exhibit shortfalls when interpreting images and, by some measures, have poorer spatial cognition than young children

A Fair Benchmarking of Deep Relational Database Learning Models

arXiv:2607.03659v1 Announce Type: cross Abstract: Relational databases (RDBs) are the primary data infrastructure in many enterprises, yet recent deep learning methods designed for RDBs have been eval

A Technical Survey of Reinforcement Learning Techniques for Large Language Models

arXiv:2507.04136v2 Announce Type: replace Abstract: This survey offers a comprehensive foundation on the integration of RL with language models, highlighting prominent algorithms such as Proximal Poli

A Unified Framework for In-Context Learning with Causal and Masked Language Models

arXiv:2607.04081v1 Announce Type: new Abstract: In-context learning (ICL) has emerged as a central capability of pretrained language models, yet its theoretical analysis has focused primarily on causa

researcharxiv-cs-lg

Attention Dynamics in Diffusion Models: A Visual Analytics Framework for Human-AI Collaboration

arXiv:2607.02563v1 Announce Type: cross Abstract: Diffusion-based text-to-image models can synthesize complex and highly structured visual content, yet the emergence and evolution of semantic structur

CL-Anomaly: Layer-Adaptive Mixture-of-Experts with Multimodal Large Language Model for Continual Learning in Anomaly Detection

arXiv:2607.02930v1 Announce Type: new Abstract: Multimodal Large Language Models (MLLMs) excel in diverse vision tasks, but full-parameter retraining is computationally expensive as real-world knowled

Do Medical Vision Language Models Actually See? A Counterfactual Grounding Framework and Hard-Negative Contrastive Training for Visually-Reliant Medical VLMs

arXiv:2607.03647v1 Announce Type: new Abstract: Large vision language models (VLMs) report strong accuracy on medical question-answering, yet it remains unclear whether they reason from visual evidenc

DREAMSTEER: Latent World Models Can Steer VLA Policies During Deployment Without Any Finetuning

model-releasesarxiv-cs-ro

arXiv:2607.02865v1 Announce Type: new Abstract: Pretrained vision-language-action (VLA) policies show promising zero-shot generalization, but often fail under deployment-time distribution shift, leadi

DynaVieW: Schema-Guided World Modeling for Understanding Hierarchical Visual Dynamics

arXiv:2607.04112v1 Announce Type: cross Abstract: Multimodal LLMs struggle to systematically model the temporal evolution of visual scenes in videos or multi-image sequences. Such inputs require model

Safety

Enhancing the Forecasting Capability of Multi-Model Blending Algorithms for Extreme Precipitation via Joint Use of Station and Gridded Observations

arXiv:2607.04862v1 Announce Type: new Abstract: Accurate extreme precipitation forecasting is critical for disaster mitigation but remains challenging for numerical weather prediction (NWP) models due

safetyarxiv-cs-lg

Erasing Without Collateral Damage: Precise Concept Removal in Diffusion Models

arXiv:2607.05274v1 Announce Type: new Abstract: Training-free concept erasure is an attractive mechanism for controlling text-to-image diffusion models, but precise erasure often comes at the cost of

Exploring the Rashomon Set for Concept-Based Models

arXiv:2511.19636v2 Announce Type: replace-cross Abstract: In many machine learning problems, there may exist multiple models that achieve nearly identical predictive performance while relying on funda

Local Ai

Framework for Grouping Local Process Models

arXiv:2607.04856v1 Announce Type: new Abstract: Local Process Models (LPMs) are an underexplored concept in process mining. LPMs describe patterns in event data considering sequence, choice, concurren

local-aiarxiv-cs-lg

Geographic Diversity Beats Data Volume for Cross-Domain Generalization in Zero-Label JEPA Driving World Models

arXiv:2607.04500v1 Announce Type: new Abstract: Self-supervised latent world models can assign a surprise score to driving scenarios without any human labels. A natural follow-up question is whether s

researcharxiv-cs-cv

Geometry of Ordinal Representations in Language Models

arXiv:2607.04167v1 Announce Type: new Abstract: Recent work showed that language models represent character counts on curved 1D manifolds, with attention heads performing geometric transformations to

Safety

How Utilitarian Are OpenAI's Models Really? Replicating and Reinterpreting Pfeffer, Krugel, and Uhl (2025)

arXiv:2603.22730v2 Announce Type: replace Abstract: Pfeffer, Krugel, and Uhl (2025) report that OpenAI's reasoning model o1-mini produces more utilitarian responses to the trolley problem and footbrid

safetyarxiv-cs-cl

Integrating Neural Encoders in Bayesian Generalized Linear Mixed Models for Multimodal Data

arXiv:2607.04647v1 Announce Type: cross Abstract: Scalable Bayesian inference for generalized linear mixed models (GLMMs) provides uncertainty-aware analysis of correlated longitudinal data, but exist

Modular Foundation Models for Time-Series Perception in Digital Twins

arXiv:2607.03585v1 Announce Type: new Abstract: Engineering Digital Twins and Prognostics and Health Management (PHM) systems rely on robust perception modules to extract actionable information from h

OmniFocus: Query-Guided Modality-Balanced Token Compression for Omni-Modal Large Language Models

arXiv:2607.03050v1 Announce Type: cross Abstract: Omni modal large language models (OmniLLMs) have attracted wide attention for their ability to jointly process audio and video, but they generate larg

Tutorials

Revealing Hidden Model Behaviors with Task-Specific Self-Reports

arXiv:2607.03640v1 Announce Type: cross Abstract: Fine-tuning can give a language model a hidden behavior--it may give false answers under a narrow condition, or give harmful advice only when a prompt

tutorialsarxiv-cs-ai

Applications

Signal or Noise? Understanding Generative Models for Real-World Sensor Time Series

arXiv:2607.04245v1 Announce Type: cross Abstract: Generative models have changed how machine learning represents complex data distributions, especially in language and vision, yet many real-world syst

applicationsarxiv-cs-ai

Stacked LoRA for Subject-Adaptive EEG Foundation Models in Motor Imagery Decoding

arXiv:2607.03094v1 Announce Type: new Abstract: Electroencephalography (EEG) decoding for brain-computer interfaces (BCIs) faces a major challenge: substantial inter-subject variability limits effecti

Training Hybrid Block Diffusion Language Models with Partial Bidirectionality

arXiv:2607.02805v1 Announce Type: cross Abstract: High-throughput long-context generation is one of the central challenges for large language models. Generation is typically memory-bandwidth-bound rat

Agents

UNIVERSE: Unified Video Action Models for Autonomous Driving with Flexible Mask-Modulated Modality Generation

arXiv:2607.05133v1 Announce Type: new Abstract: World Action Models (WAMs) have shown strong potential for improving action generalization in autonomous driving by using future video prediction as den

agentsarxiv-cs-cv

VCB Bench: An Evaluation Benchmark for Audio-Grounded Large Language Model Conversational Agents

arXiv:2510.11098v5 Announce Type: replace-cross Abstract: Recent advances in large audio language models (LALMs) have greatly enhanced multimodal conversational systems. However, existing benchmarks r

VISTA: Auditing Semantic Divergence in Vision-Language Models

arXiv:2607.02995v1 Announce Type: cross Abstract: Vision-language models can exhibit visual concept-conditioned divergence: given images containing demographic features, corporate logos, or ideologica

Applications

WAM4D: Fast 4D World Action Model via Spatial Register Tokens

arXiv:2606.14048v2 Announce Type: replace Abstract: World action models (WAMs) have recently shown promise in jointly modeling future observations and executable robot actions. However, most existing

applicationsarxiv-cs-cv

Which Algorithm Specification Formats Help Language Models Implement Machine Learning Algorithms?

arXiv:2607.03158v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly used to implement algorithms from research manuscripts, but papers often leave implementation choices im

Wrong Before Right: Late Rescue and Interface Failure in Aligned Language Models

arXiv:2607.04640v1 Announce Type: new Abstract: We study how correctness is assembled inside aligned language models, not only whether the final answer is right. Using layer-wise difference-in-differe

Interpreting Global Perturbation Robustness of Image Models using Axiomatic Spectral Importance Decomposition

arXiv:2408.01139v4 Announce Type: replace Abstract: Perturbation robustness evaluates the vulnerabilities of models, arising from a variety of perturbations, such as data corruptions and adversarial a

MMBench-Live: A Continuously Evolving Benchmark for Multimodal Models

arXiv:2607.01813v1 Announce Type: cross Abstract: Evaluation benchmarks are essential for assessing vision-language models (VLMs), but most multimodal benchmarks are static, making them vulnerable to

OntoLearner: A Modular Python Library for Ontology Learning with Large Language Models

arXiv:2607.01977v1 Announce Type: new Abstract: Ontology learning (OL) aims to automatically construct structured knowledge models from text, yet progress remains fragmented across methods, domains, a

Psychological Imagination Networks Show Cross-Population Centrality and Clustering Alignment in Humans That Large Language Models Fail to Replicate

arXiv:2510.04391v5 Announce Type: replace Abstract: Mental imagery vividness is a stable individual trait, yet whether imagined scenarios share relational structure across human and synthetic large la

The Wiola Architecture for Efficient Small Language Models

arXiv:2607.01394v1 Announce Type: new Abstract: We present Wiola, a fully original Small Language Model (SLM) architecture built from first principles, sharing no structural lineage with any existing

3D Point World Models: Point Completion Enables More Accurate Dynamics Learning

arXiv:2607.00148v1 Announce Type: cross Abstract: Learning predictive models of the world enables robotic control through planning, potentially allowing robots to improvise solutions on new tasks. How

researcharxiv-cs-cv

AD-MPCC: Adaptive Differentiable Model Predictive Contouring Control for Autonomous Racing

model-releasesarxiv-cs-ro

arXiv:2607.00141v1 Announce Type: new Abstract: This paper presents Adaptive Differentiable Model Predictive Contouring Control (AD-MPCC), a framework for autonomous racing that integrates differentia

Device Passport: Enabling Spatio-Temporal Pretrained Models to Generalize Across Input Layouts

arXiv:2607.00249v1 Announce Type: new Abstract: New device layouts pose a challenging modeling problem due to the lack of large datasets for each specific layout. Biosignal foundation models offer a p

researcharxiv-cs-lg

DriveVA: Video Action Models are Zero-Shot Drivers

arXiv:2604.04198v2 Announce Type: replace Abstract: Generalization is a central challenge in autonomous driving, as real-world deployment requires robust performance under unseen scenarios, sensor dom

EmbodimentSemantic: A Spatial Scene-Graph Dataset and Benchmark for Vision-Language Models on Embodied Manipulation Trajectories

model-releasesarxiv-cs-ro

arXiv:2607.00020v1 Announce Type: new Abstract: Spatial grounding remains a key limitation of vision-language-action (VLA) systems for robotic manipulation. While current models can recognize objects

Foundation Models vs. Radiomics for Lung Computed Tomography: A Benchmark of Feature Extractors, Classification Heads, and Segmentation Choices

arXiv:2607.01001v1 Announce Type: new Abstract: Radiomics is the established approach for CT-based lung cancer phenotyping, yet comparisons with foundation models rarely isolate contributions of featu

Tutorials

Generative Modeling of Quantum Distribution with Functional Flow Matching

arXiv:2607.00301v1 Announce Type: new Abstract: The emergence of powerful deep generative models based on diffusion and flow matching has enabled the learning and modeling of complex distributions. Le

tutorialsarxiv-cs-lg