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This paper proposes Hierarchical Federated Transfer Learning (HFTL) for Digital Twin-based Vehicular Ad hoc Networks, addressing data heterogeneity and sparsity via vehicle clustering and a data quality score mechanism to defend against malicious vehicles.
Min is an AI product that builds digital versions of everyone you work with.
Backflip AI unveils a second-generation AI model that turns physical parts into native parametric CAD models in minutes for about $10, drastically reducing the cost and time of reverse engineering and aiming to digitize entire factories.
Mirage Avatar X by Captions creates a realistic digital twin from just 10 seconds of video, preserving identity, expressions, and mannerisms without quality degradation.
A tweet discussing the concept of a 'Codyfied' version of yourself and your business, likely referencing an AI-powered tool for personal or business representation.
A Verge senior reviewer details the extensive tracking regimen she uses to evaluate wearables and metabolic health tech, including devices like continuous glucose monitors, smart rings, and scales.
This paper presents a novel framework for zero-shot Digital Twins that integrates real-time visual perception with a geometry-agnostic, physics-informed Graph Neural Network. The approach uses a Thermodynamics-Informed GNN to enforce energy conservation and entropy production, achieving physically accurate simulations on unseen geometries without retraining.
Niantic Spatial, Flexion, and NVIDIA demonstrate a sim2real pipeline for humanoid robots using digital twins and RL, achieving zero-shot transfer from simulation to real office navigation.
A developer built a digital twin of NYC with real-time data tracking air traffic, subways, buses, and more, powered by DevinAI.
The user used the Kimi K3 model to reconstruct a digital version of the Yingxian Wooden Pagoda, containing 10,611 components and four lighting scenes (morning, noon, dusk, night), demonstrating the model's 3D reconstruction capability.
This essay proposes 'Guardian Angels,' highly personalized LLMs that emulate a user's values and preferences to boost productivity and provide security against advanced AI threats, using techniques like dynamic evaluation and active learning.
This paper introduces EHR-MPC, a framework that decouples learning patient dynamics from treatment optimization by training a generative digital twin of patients using electronic health records, then applying model predictive control at inference time, achieving comparable or improved performance over RL baselines on a multi-hospital sepsis cohort.
This paper proposes LDT-Coord, a lightweight digital-twin coordination framework for heterogeneous LLM embodied agents over computing power networks, achieving a task success rate comparable to conventional methods while reducing communication overhead by over 70×.
This paper presents the Large Behavioral Model (LBM), which learns customer decision-making from retail transactions using a Person–Environment formulation, retrieval-augmented generation, and reinforcement learning. It outperforms frontier LLMs on retail tasks and shows strong zero-shot transfer.
This paper introduces HALE, a scalable hybrid agent-based and language-driven epidemic modeling framework that leverages LLMs to predict human decision-making in ABM simulations, demonstrating improved accuracy in modeling COVID-19 spread in Salt Lake County.
Presents Onnes, a physics-grounded multi-agent LLM simulator for cryogenic fault diagnosis in quantum computing infrastructure. With curated few-shot demonstrations and self-consistency voting, a zero-shot LLM agent panel achieves 0.990 fault-classification accuracy, matching a supervised classifier without parameter updates.
This paper investigates whether everyday speech from older adults can be used as a personalized cognitive monitoring tool, finding that AI models can detect subtle language patterns indicative of cognitive decline, unlike standard GPT responses.
This paper proposes a reinforcement learning-driven adaptive sim-to-real alignment method for vibration-based bearing health monitoring, addressing data scarcity and heterogeneous fault-type gaps via proximal policy optimization.
This paper introduces DigiTurbine, a synthetic reliability-aware Physics-Informed Neural Network (PINN) benchmark for offshore wind turbine monopile support-structure monitoring, combining forward and inverse PINN with Bayesian prior-informed identification and FORM-based reliability screening.
Eco Wave Power uses NVIDIA AI infrastructure and digital twins to convert ocean wave energy into clean electricity, leveraging existing marine infrastructure to address growing AI energy demands.