Reflection AI has published Beam’s weights on Hugging Face along with post-training recipes and tooling for customization. The release covers a model with about 500 billion parameters trained over two months on several thousand high-end GPUs. Developers can now fine-tune and deploy Beam using ready-to-use serving configurations, quantized variants, and reliability-focused training workflows, with Reflection AI planning more recipes soon.
A consortium led by Resolvent and Volvo Group Trucks Operations used multiscale simulation and a custom simulation app to model how laser welding affects electric truck battery lifetime and robustness during manufacturing. The LaserBATMAN project ran from May 2022 to May 2025. The result is an app that lets Volvo’s R&D test and optimize busbar weld parameters and predict battery pack performance, including temperature, state of charge, and SEI thickness.
Skild AI is trying to make robotic intelligence transfer across different robot bodies instead of rebuilding it for each machine, by linking motor adaptation, video-prompted task learning, and self-play. The decisive test in Issue #952 is whether those capabilities can meet deployment constraints around speed, reliability, and safety. As a result, the focus shifts from engineering a robot-specific model to training systems that learn how to use whatever body they’re given.
Robot training is presented as a data problem tied to the scaling idea that better AI comes from bigger models, more training data, and more compute. The article notes Epoch AI’s estimate that the existing stock of quality, accessible text data is about 300 trillion tokens, while there is no comparable stock of public robot training data. As a result, it emphasizes that robotics research must rely on methods like teleoperation, reinforcement learning with robots or simulations, and simulation-to-real approaches to generate enough training data.
Google Quantum AI demonstrated that magic state cultivation works on its Willow quantum processor, addressing a long-standing resource bottleneck for universal, error-corrected quantum computing. The team reported a 40-fold reduction in errors versus using un-cultivated states and achieved 99.99% logical fidelity for T-states. The result changes the outlook by providing the first real-hardware proof that this approach can efficiently produce high-quality magic states and support scaling efforts.
Meta’s updated platform terms appear to allow it to crawl and scrape websites associated with businesses that advertise or promote content on its platforms. The terms take effect on October 30th and explicitly permit Meta to use outside content to develop and train its AI products. This could change how publisher content is used for AI training by potentially reducing the need for separate licensing.
AI4Bharat and NVIDIA released Nemotron-pretraining-Indic-v1 and an open GPU-accelerated synthetic data generation pipeline for 22 Indian languages. The corpus is approximately 2.8 trillion tokens and includes 2,601.0 billion native-script tokens plus 224.6 billion romanized tokens. Model builders can now reuse the data and the full prompt/quality/repair recipe to scale Indic pretraining and expect improved benchmark performance on Nemotron 3.5 Super VL after adding an Indic continued-pretraining stage.
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