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Local curvature of potential energy surfaces is critical for predicting certain experimental observables of molecules and materials from first principles, yet it remains far beyond reach for complex systems. In this work, we introduce a Hessian-informed Machine Learning Interatomic Potential (Hi-MLIP) that captures such curvature reliably, thereby enabling accurate analysis of associated thermodynamic and kinetic phenomena. To make Hessian supervision practically viable, we develop a highly efficient training protocol, termed Hessian INformed Training (HINT), achieving two to four […]

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Here’s my take on teaching AI to play a video game, with the fun twist that this time nobody ever heard of it. DDNet (aka Teeworlds) is an open source retro multiplayer platformer with different game modes like pvp and race modes. Players can walk, jump, use grappling hook and various weapons. In this project, I focused on solo race mode. For the algorithm I chose PPO, but tried various reward shaping methods that I found interesting/promising, such […]

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Hi folks, Over the past few months, I built an efficient MCTS implementation in Python/numba. https://github.com/olivkoch/gumbel-mcts As I was building a self-play environment from scratch (for learning purposes), I realized that there were few efficient implementation of this algorithm. I spent a lot of time validating it against a golden standard baseline. My PUCT implementation is 2-15X faster than the baseline while providing the exact same policy. I also implemented a Gumbel MCTS, both dense and sparse. The […]

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Distributed intelligence in industrial networks increasingly integrates sensing, communication, and computation across heterogeneous and resource constrained devices. Federated learning (FL) enables collaborative model training in such environments, but its reliability is affected by inconsistent client behaviour, noisy sensing conditions, and the presence of faulty or adversarial updates. Trust based mechanisms are commonly used to mitigate these effects, yet most remain statistical and heuristic, relying on fixed parameters or simple adaptive rules that struggle to accommodate changing operating conditions. […]

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Low-altitude communications can promote the integration of aerial and terrestrial wireless resources, expand network coverage, and enhance transmission quality, thereby empowering the development of sixth-generation (6G) mobile communications. As an enabler for low-altitude transmission, 3D channel fingerprints (3D-CF), also referred to as the 3D radio map or 3D channel knowledge map, are expected to enhance the understanding of communication environments and assist in the acquisition of channel state information (CSI), thereby avoiding repeated estimations and reducing computational complexity. […]

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Before I got a laptop, I used to work on my phone with a keyboard connected via an OTG. It wasn’t a fancy phone – just a Galaxy J2 with a micro-USB port. I used a membrane keyboard with a cheap $0.4 OTG adapter, but it got the job done. I put the savings towards a laptop and once I bought my first laptop, forgot all about that OTG hack I was using. Fast forward to 2026 – […]

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Hey everyone, I recently built my first reinforcement learning agent to play Super Mario Bros and Super Mario World. I documented the whole process in a video, and would love any feedback from people who know RL. I’m still learning and I’m sure there are better approaches I missed. Happy to answer any questions about the process too. https://youtu.be/6FQKz-yAt5Y submitted by /u/Marcell0123 [link] [comments]

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When traditional software breaks, debugging usually follows a familiar path: You look at logs → You replay the request → You reproduce the issue → Eventually you find the bug. But when an AI system breaks, something strange happens. You try to reproduce the same request — and the system gives you a completely different answer. The Incident A user once reported a strange response from our AI API. They sent us a screenshot showing the output the […]

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