January 2026

Urban Air Mobility (UAM) has emerged as a transformative solution to alleviate urban congestion by utilizing low-altitude airspace, thereby reducing pressure on ground transportation networks. To enable truly efficient and seamless door-to-door travel experiences, UAM requires close integration with existing ground transportation infrastructure. However, current research on optimal integrated routing strategies for passengers in air-ground mobility systems remains limited, with a lack of systematic exploration.To address this gap, we first propose a unified optimization model that integrates strategy […]

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Key Highlights: OpenAI has been among the key players in the AI industry, or let’s just say everyone’s favorite, except for Elon Musk. Many tech giants have invested in the company, including Microsoft in its initial days. Apparently, the bet taken by Satya Nadella, for which Bill Gates was skeptical, has been turning out fruitful for the Redmond giant. OpenAI reportedly set to receive massive investments from NVIDIA, Netflix, and Amazon Now, according to The Information, NVIDIA, Microsoft, […]

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Meta, as you may know, has been pouring billions into AI ($65 billion to be precise in 2025), as it looks to keep up with the intensifying competition. While the company hoped its Llama model would come out strong. Sadly, it never did. But, that setback alone doesn’t appear to have hampered Meta’s future AI goals. Meta is now reportedly getting ready to push its next wave of AI products, and it seems that users won’t have to […]

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Hi, i’ve done plenty of RL projects. AlphaZero (checkers), self driving racecar with SAC, some classic gymnasium environment with DQN. The problem is, always, the environment. Playing checkers ? Need to implement checkers environment racecar ? need to write a car simulator (really difficult actually) and so on I’d love to give a try to a (mini) RTS, like AlphaStar, but i’m not google and i don’t have a custom version of SC2 … MicroRTS is dead and […]

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With the rapid expansion of electric vehicles (EVs) and charging infrastructure, the effective management of Autonomous Electric Taxi (AET) fleets faces a critical challenge in environments with dynamic and uncertain charging availability. While most existing research assumes a static charging network, this simplification creates a significant gap between theoretical models and real-world operations. To bridge this gap, we propose GAT-PEARL, a novel meta-reinforcement learning framework that learns an adaptive operational policy. Our approach integrates a graph attention network […]

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This paper investigates search in model-based reinforcement learning (RL). Conventional wisdom holds that long-term predictions and compounding errors are the primary obstacles for model-based RL. We challenge this view, showing that search is not a plug-and-play replacement for a learned policy. Surprisingly, we find that search can harm performance even when the model is highly accurate. Instead, we show that mitigating distribution shift matters more than improving model or value function accuracy. Building on this insight, we identify […]

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We present the convergence rates of synchronous and asynchronous Q-learning for average-reward Markov decision processes, where the absence of contraction poses a fundamental challenge. Existing non-asymptotic results overcome this challenge by either imposing strong assumptions to enforce seminorm contraction or relying on discounted or episodic Markov decision processes as successive approximations, which either require unknown parameters or result in suboptimal sample complexity. In this work, under a reachability assumption, we establish optimal $widetilde{O}(varepsilon^{-2})$ sample complexity guarantees (up to […]

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Partial Least Squares (PLS) learns shared structure from paired data via the top singular vectors of the empirical cross-covariance (PLS-SVD), but multimodal datasets often have missing entries in both views. We study PLS-SVD under independent entry-wise missing-completely-at-random masking in a proportional high-dimensional spiked model. After appropriate normalization, the masked cross-covariance behaves like a spiked rectangular random matrix whose effective signal strength is attenuated by $sqrtρ$, where $ρ$ is the joint entry retention probability. As a result, PLS-SVD exhibits […]

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Diffusion models have emerged as powerful generative tools for modeling complex data distributions, yet their purely data-driven nature limits applicability in practical engineering and scientific problems where physical laws need to be followed. This paper proposes Physics-Informed Learning via Diffusion (PILD), a framework that unifies diffusion modeling and first-principles physical constraints by introducing a virtual residual observation sampled from a Laplace distribution to supervise generation during training. To further integrate physical laws, a conditional embedding module is incorporated […]

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