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Solving Inverse Problems with Flow-based Models via Model Predictive Control

arXiv:2601.23231v2 Announce Type: replace-cross Abstract: Flow-based generative models provide strong unconditional priors for inverse problems, but guiding their dynamics for conditional generation remains challenging. Recent work casts training-free conditional generation in flow models as an optimal control problem; however, solving the resulting trajectory optimisation is computationally and memory intensive, requiring differentiation through the flow dynamics or adjoint solves. We...

arXiv CS 1d ago

Unifying Model-Free Efficiency and Model-Based Representations via Latent Dynamics

Announce Type: replace Abstract: We present Unified Latent Dynamics (ULD), a novel reinforcement learning algorithm that unifies the efficiency of model-free methods with the representational strengths of model-based approaches, without incurring planning overhead. By embedding state-action pairs into a latent space in which the true value function is approximately linear, our method supports a single set of hyperparameters across diverse domains -- from continuous control with...

arXiv CS 6d ago

WorldFly: A World-Model-Based Vision-Language-Action Model for UAV Navigation

arXiv:2606.06147v1 Announce Type: new Abstract: End-to-end Vision-Language-Action (VLA) models have shown promise in UAV navigation. However, existing approaches typically rely on historical observations to directly predict actions, often struggling in dense urban environments where severe occlusions and sharp turns result in drastic viewpoint transitions. We argue that the ability to "imagine" future states -- inherent in World Models -- is critical for robust decision-making under such...

arXiv CS 5d ago

Conditional Latent Diffusion Model with Fourier-based Motion Modelling for Virtual Population Synthesis

arXiv:2606.03827v1 Announce Type: new Abstract: In-silico trials of medical devices require the generation of virtual populations of anatomies. In cardiovascular applications, virtual anatomy is typically represented as a 3D+t mesh sampled from a generative model. However, most existing mesh generators focus on static anatomy, while sequence models often lack explicit periodicity.

arXiv CS 7d ago

MATraM: A Multi-Activity Transport and Mobility Agent-Based Model for Activity Modifications

arXiv:2605.30547v1 Announce Type: new Abstract: This paper introduces the Multi-Activity Transport & Mobility (MATraM) Agent-Based Model (ABM), a novel framework designed to advance activity-based transport modelling by incorporating dynamic activity adaptation. Traditional transport models simulate system performance using varying levels of abstraction, including flow-based, queue-based, and interaction-based mobility representations. While these approaches differ in their treatment of...

arXiv CS 9d ago

Continuous Temporal Representations of Event-Based Signals via Interference-Based Wave Modeling

arXiv:2605.01270v2 Announce Type: replace Abstract: Spatio-temporal signals arising from event-driven biological processes, such as surface electromyography (sEMG), exhibit asynchronous and highly structured activation patterns that are challenging to model using conventional discrete or purely real-valued representations. In this work, we propose a continuous temporal modeling framework based on interference-based wave representations. The approach maps event-like input signals into a...

arXiv CS 8d ago

IDDMBSE: Integrating Data-Driven and Model-Based Systems Engineering for Trusted Autonomous Cyber-Physical Systems

arXiv:2606.06727v1 Announce Type: new Abstract: Autonomous cyber-physical systems (CPS) sit at the intersection of Model-Based Systems Engineering (MBSE) and data-driven Machine Learning and Artificial Intelligence (ML/AI), yet no integrated Systems Engineering (SE) methodology natively spans both. We address this gap with IDDMBSE, an Integrated Data-Driven and Model-Based Systems Engineering methodology that extends the rigorous MBSE V-process with a data-driven loop at every step, anchored...

arXiv CS 2d ago

A Unified Geometric Space for Topological Alignment Between Transformer-Based Models and Human Brain Networks

arXiv:2510.24342v2 Announce Type: replace Abstract: Prior brain-AI alignment studies are typically constrained by specific inputs and tasks, limiting their ability to capture organizational properties across models with different modalities. In this work, we focus on Transformer-based models and introduce a brain-model topological alignment space.

arXiv CS 6d ago

A systematic investigation of molecular encoding methods for drug property predictions across neural network and Transformer encoder-based model

arXiv:2606.08973v1 Announce Type: cross Abstract: Fundamental investigations into how different molecular encoding methods affect molecular property prediction remain relatively limited. In this study, we extensively examined the optimal molecular encoding methods for molecular properties prediction using two prevalent structure designs: a classical neural network model (MLP) and a Transformer encoder-based model (MLP+TL). For molecular encoding methods, we investigated several types of...

arXiv CS 1d ago

Hong Kong launches DeepSeek-based AI model designed to run on domestic chips

Hong Kong launches DeepSeek-based AI model designed to run on domestic chips Agent Workshop operated stably for up to 28 hours without interruption in a single session to produce a research report The Hong Kong Generative AI Research and Development Centre (HKGAI) has officially launched a new DeepSeek-based large language model that can run on domestic chips, as the government-backed lab seeks to commercialise its products and export Chinese AI overseas. The HKGAI-V3 model, built on...

South China Morning Post 7d ago