Search: “models”

Quantum Cinema: An Interactive Cinematic Exploration of Quantum Computing Hardware via Generative World Models

arXiv:2606.17102v2 Announce Type: replace-cross Abstract: Quantum computing promises transformative advances across science and industry, yet the physical hardware that enables these computations rema

researcharxiv-cs-ai

24 Jun 2026

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Hardware

TurboMPC: Fast, Scalable, and Differentiable Model Predictive Control on the GPU

DGX agent

arXiv:2606.24039v1 Announce Type: new Abstract: Robotics increasingly relies on GPUs for parallel simulation, large-scale learning, and neural-network inference. For model predictive control (MPC) to

hardwarearxiv-cs-ro

24 Jun 2026

Tutorials

A Hybrid TGN-SEAL Model for Dynamic Graph Link Prediction

arXiv:2602.14239v2 Announce Type: replace-cross Abstract: Predicting links in sparse, continuously evolving networks is a central challenge in network science. Conventional heuristic methods and deep

tutorialsarxiv-cs-lg

A Reduced Order Model for Emergent Mechanics in Woven Systems

arXiv:2606.23658v1 Announce Type: new Abstract: Woven structures exhibit rich mechanical behaviors including anisotropic stiffness, shear-induced locking, and crimp interchange that emerge purely from

Causal Reward World Models: Zero-shot Reward Design for Automated Skill Generation

arXiv:2606.23280v1 Announce Type: new Abstract: Automated Reward Design (ARD) aims to replace manual reward engineering in reinforcement learning with language-driven reward function synthesis. Howeve

Conceptual Design of an Ecosystem for Real Farm Data Collection toward Agricultural AI Foundation Models

arXiv:2606.23258v1 Announce Type: new Abstract: Data scarcity is a fundamental challenge in developing AI and foundation models for agricultural robots. Existing open-source data platforms do not prov

Delta-Diffusion: Modeling Longitudinal Brain Amyloid-PET Trajectories via Conditional Poisson Diffusion Bridge

arXiv:2606.22216v1 Announce Type: cross Abstract: While longitudinal brain PET imaging is the gold standard for quantifying the spatiotemporal accumulation of Beta-amyloid, its widespread clinical uti

safetyarxiv-cs-cv

Demystifying Training-Time Augmentation for Data-Constrained Language Model Pretraining

arXiv:2606.16246v2 Announce Type: replace Abstract: As AI labs approach a data ceiling where compute capacity outpaces the rate of new high-quality text generation, language model pretraining is shift

Efficient Network Inference via Hardware-Aware Architecture Search, Model Pruning & Quantization

arXiv:2606.23210v1 Announce Type: new Abstract: Embedded global navigation satellite system (GNSS) interference monitoring requires fast and memory-efficient inference to process large volumes of raw

EnTrust: Modeling Inter-Modal Conflict for Trustworthy Multimodal Medical Image Analysis

arXiv:2606.21384v1 Announce Type: new Abstract: Multimodal medical imaging fuses complementary anatomical and functional information, yet modalities frequently disagree in pathologically heterogeneous

local-aiarxiv-cs-cv

FlowTrain: Flow-Based Decoupled Training for Industrial-Grade Vision-Language Models

arXiv:2606.23087v1 Announce Type: new Abstract: Industrial-grade distributed training of vision-language models (VLMs) remains far less efficient than that of unimodal LLMs. Existing solutions either

applicationsarxiv-cs-lg

Foresight: Failure Detection for Long-Horizon Robotic Manipulation with Action-Conditioned World Model Latents

arXiv:2606.23085v1 Announce Type: new Abstract: Long-horizon tasks are common in real-world robotic deployments, yet failure detection for such tasks remains underexplored. Detecting failures in long-

applicationsarxiv-cs-ro

Foundation Models for Epileptogenic Zone Identification in Drug-Resistant Epilepsy

arXiv:2606.22657v1 Announce Type: new Abstract: Accurate identification of the epileptogenic zone (EZ) is essential for seizure freedom after resective surgery in drug-resistant epilepsy, yet seizure

From Pixels to Concepts: Growing Rich 3D Semantic Scene Graph Forests utilizing Foundation Models

arXiv:2606.23312v1 Announce Type: new Abstract: Operating in complex real-world environments requires robots to understand their surroundings on a functional semantic level. This demands a detailed mu

applicationsarxiv-cs-ro

GreenRFM: Learning a resource-efficient radiology vision-language foundation model via supervision-centric pre-training

arXiv:2603.06467v2 Announce Type: replace Abstract: Radiology foundation models (RFMs) have largely inherited the scale-first recipe of natural-image vision--language pre-training. This recipe is diff

local-aiarxiv-cs-cv

Learning with Multiple Correct Answers -- Regret Bounds under Different Feedback Models

arXiv:2602.09402v2 Announce Type: replace Abstract: We study the problem of learning with multiple correct answers, where each instance admits a set of valid labels. We primarily focus on the online s

Mind the Noise: Sensitivity of Transformer-based Interaction-Aware Trajectory Prediction Models to Noisy Data

arXiv:2606.21344v1 Announce Type: cross Abstract: Trajectory prediction allows autonomous vehicles to anticipate the future behavior of surrounding objects (or agents) and, accordingly, maximize the s

local-aiarxiv-cs-lg

Multi-Year-to-Decadal Temperature Prediction using a Machine Learning Model-Analog Framework

arXiv:2502.17583v2 Announce Type: replace-cross Abstract: Multi-year-to-decadal climate predictions are a key tool in understanding the range of potential regional climate futures. Here, we present a

Numerical stability analysis of large language models

arXiv:2503.10251v2 Announce Type: replace-cross Abstract: Transformers are the state-of-the-art architecture for large language models, and a key to their scalability is the strategic usage of low-pre

local-aiarxiv-cs-lg

Tutorials

Online World Modeling Enables Real-World Inverse Reinforcement Learning from Observation

arXiv:2602.24121v2 Announce Type: replace Abstract: Current methods in robot learning are fundamentally bottlenecked by one or more of: hand-designed rewards, simulation modeling, or action supervisio

tutorialsarxiv-cs-ro

Physics-Guided Spatiotemporal State Space Modeling for Lookahead Molten Pool Segmentation in Laser Wire-Feed Welding

arXiv:2606.23028v1 Announce Type: new Abstract: Real-time weld-pool perception is critical for closed-loop control in laser wire-feed welding, where sensing, computation, and actuator response introdu

Pre-Generation Hallucination Detection in Large Language Models via Soft-Target Attention Probing

arXiv:2606.21917v1 Announce Type: cross Abstract: Detecting hallucination risk before generation enables abstention, retrieval augmentation, and routing decisions without incurring the cost of decodin

RECALL: Recovery Experience Collection for Active Lifelong Learning in Vision-Language-Action Models

arXiv:2606.23617v1 Announce Type: cross Abstract: Vision-Language-Action (VLA) models are commonly fine-tuned through passive imitation learning, where additional demonstrations are collected for task

Recency/Frequency Adaptive KV Caching for Large Language Model Serving

arXiv:2606.21238v1 Announce Type: cross Abstract: Key-value (KV) caching is a powerful technique for accelerating large language model inference and generation. Inference workloads are large and diver

Rethinking the Adaptation of Vision Foundation Models for Efficient Cell Segmentation

arXiv:2606.21913v1 Announce Type: new Abstract: Cell segmentation is critical for computational pathology and biomedical discovery. While recent Vision Foundation Models (VFMs) have demonstrated remar

Robust Diffusion Models via Divergence-Induced Weighted Denoising

arXiv:2606.22521v1 Announce Type: cross Abstract: We show that replacing the standard MSE denoising loss in diffusion models with a nonlinear transformation induced by an f-divergence yields a simple

Semi-Supervised Vision-Language-Action Model

arXiv:2606.21493v1 Announce Type: new Abstract: Vision-Language-Action (VLA) models enable robots to predict actions directly from visual observations and language instructions, but adapting them to n

safetyarxiv-cs-cv

SignVLA: Real-Time Sign Language-Guided Robotic Manipulation via Attention LSTM and Vision-Language-Action Models

arXiv:2606.20857v1 Announce Type: cross Abstract: Vision-Language-Action (VLA) models enable robots to execute manipulation tasks from natural-language instructions grounded in visual observations. Ho

T-VSS: Test-Time Visual Subspace Steering for Adversarial Robustness of Vision-Language Models

arXiv:2606.23132v1 Announce Type: new Abstract: Vision-language models (VLMs) achieve strong zero-shot recognition, but they remain highly vulnerable to adversarial perturbations. Recent test-time ada

The Trilemma of Truth in Large Language Models

arXiv:2506.23921v5 Announce Type: replace-cross Abstract: The public often attributes human-like qualities to large language models (LLMs), assuming that they 'know' certain things. In reality, LLMs e

TraceMark-LDM: Authenticatable Watermarking for Latent Diffusion Models via Binary-Guided Rearrangement

arXiv:2503.23332v2 Announce Type: replace Abstract: Image generation algorithms are increasingly integral to diverse aspects of human society, driven by their practical applications. However, insuffic

Variance-Tilted Diffusion Models for Diverse Sampling

arXiv:2606.22239v1 Announce Type: cross Abstract: Diffusion models are typically sampled independently, even when the downstream objective is to obtain a diverse set of candidates. We introduce a vari

VegSim: A Geospatial World Model for Scenario-Conditioned Vegetation Simulation

arXiv:2606.21961v1 Announce Type: new Abstract: Vegetation monitoring under climate stress requires answering not only how it will evolve given the expected weather, but how it would respond to altern

applicationsarxiv-cs-lg

VLA-FAIL: Efficient Task Failure Detection for Finetuned Vision-Language-Action Models

arXiv:2606.21386v1 Announce Type: cross Abstract: Vision-language-action models (VLAs) achieve state-of-the-art performance on many robotic manipulation tasks, yet they can still behave unpredictably

applicationsarxiv-cs-cv

Tutorials

What Does a Chemical Language Model Know About Molecules?

arXiv:2606.23443v1 Announce Type: new Abstract: Chemical language models (cLMs) are widely assumed to learn surface-level syntactic patterns rather than learning meaningful molecular semantics. Here,

tutorialsarxiv-cs-lg

What if? Emulative Simulation with World Models for Situated Reasoning

arXiv:2603.06445v2 Announce Type: replace Abstract: Situated reasoning often relies on active exploration, yet in many real-world scenarios such exploration is infeasible due to physical constraints o

safetyarxiv-cs-cv

ConsistencyPlanner: Real-time Planning with Fast-Sampling Consistency Models

arXiv:2606.11569v1 Announce Type: cross Abstract: Closed-loop planning in complex, real-world driving scenarios presents a critical challenge for autonomous driving systems. While traditional rule-bas

safetyarxiv-cs-ai

DAM-VLA: Decoupled Asynchronous Multimodal Vision Language Action model

arXiv:2606.12105v1 Announce Type: cross Abstract: Vision-language-action (VLA) models inherit a shared synchronous clock from vision-language pretraining, processing every input at one rate. This is m

applicationsarxiv-cs-cv

From Architecture to Output: Structural Origins of Hallucination in Large Language Models and the Amplifying Role of Data

arXiv:2606.07537v1 Announce Type: cross Abstract: Large language models hallucinate--producing fluent, confident, factually wrong outputs--with a consistency that persists across generations and scale

safetyarxiv-cs-ai

Kalman Linear Attention: Parallel Bayesian Filtering For Efficient Language Modelling and State Tracking

arXiv:2602.10743v2 Announce Type: replace Abstract: State-space language models such as Mamba and gated linear attention (GLA) offer linear-complexity, parallelisable alternatives to transformers, but

Model-Based and Data-Driven Hierarchical Control and Topology Co-Design for Robust Networked Systems

arXiv:2606.11596v1 Announce Type: cross Abstract: In this paper, we consider a class of networked systems comprising an interconnected set of linear subsystems, disturbance inputs, and performance out

local-aiarxiv-cs-ai

Physics-Driven Spatiotemporal Modeling for AI-Generated Video Detection

arXiv:2510.08073v2 Announce Type: replace Abstract: AI-generated videos have achieved near-perfect visual realism (e.g., Sora), urgently necessitating reliable detection mechanisms. However, detecting

Towards Deep Learning Surrogate for the Forward Problem in Electrocardiology: A Scalable Alternative to Physics-Based Models

arXiv:2512.13765v2 Announce Type: replace-cross Abstract: The forward problem in electrocardiology, computing body surface potentials from cardiac electrical activity, is traditionally solved using ph

researcharxiv-cs-ai

Where Do Backdoors Live? A Component-Level Analysis of Backdoor Propagation in Speech Language Models

arXiv:2510.01157v4 Announce Type: replace Abstract: Speech language models (SLMs) are systems of systems: independent components that unite to achieve a common goal. Despite their heterogeneous nature

researcharxiv-cs-cl

A Navigable Manifold of Hypothesized Consciousness-Spectrum States in Language Model Representations

arXiv:2606.09894v1 Announce Type: cross Abstract: Across contemplative, philosophical, and psychological accounts, human consciousness is often described along a similar spectrum, ranging from reactiv

local-aiarxiv-cs-cl

A Unified Adaptive Feature Composition Framework for Multi-Task Generalization in Wireless Foundation Models

arXiv:2606.10277v1 Announce Type: new Abstract: Though wireless foundation models (WFMs) have shown strong potential in learning universal channel representations, their adaptation to various downstre

CAST: Counterfactual Labels Improve Instruction Following in Vision-Language-Action Models

arXiv:2508.13446v2 Announce Type: replace Abstract: Generalist robots should be able to understand and follow user instructions. Despite providing a powerful architecture for mapping open-vocabulary l

Democratising Camera Trap AI: An Open-Source Model for Detecting UK Mammals

arXiv:2606.10940v1 Announce Type: cross Abstract: Camera traps have become a cornerstone of biodiversity monitoring, but the artificial intelligence that turns vast quantities of images into usable ec

researcharxiv-cs-ai