I have been trying to install isaaclab on windows for the past few days and i always end up with the same error everytime. conflicting packages while installing and dll load error while running import h5py i followed pip installation https://isaac-sim.github.io/IsaacLab/main/source/setup/installation/pip_installation.html here i installed recommended nvidia driver, installed vc tools C++ dev, added long path, but still end up with the same error over and over again. Please help while executing pip install “isaacsim[all,extscache]==5.1.0” –extra-index-url https://pypi.nvidia.com ERROR: pip’s […]

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StepFun today released Step 3.7 Flash, a multimodal Mixture-of-Experts model targeting agentic use cases. It adds native vision input and improved tool-use reliability over Step 3.5 Flash. What is Step 3.7 Flash? Step 3.7 Flash is a 198B-parameter sparse Mixture-of-Experts (MoE) vision-language model. It pairs a 196B-parameter language backbone with a 1.8B-parameter vision encoder (ViT) for native image understanding. The model activates approximately 11B parameters per token during inference. In MoE architectures, only a subset of “expert” sub-networks […]

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arXiv:2603.12463v1 Announce Type: new Abstract: With digital learning environments becoming more prevalent, the ease with which generative AI enables the scalable production of real-time, automated feedback holds the potential to reshape learning and teaching experiences. This meeting report synthesizes the interdisciplinary perspectives of 50 scholars from educational psychology, computer science, science education, and the learning sciences on the use of generative AI for feedback and its promises and risks in educational practice. We highlight points of convergence in […]

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arXiv:2512.17131v3 Announce Type: replace-cross Abstract: We propose Generalized Primal Averaging (GPA), an extension of Nesterov’s method that unifies and generalizes recent averaging-based optimizers like single-worker DiLoCo and Schedule-Free, within a non-distributed setting. While DiLoCo relies on a memory-intensive two-loop structure to periodically aggregate pseudo-gradients using Nesterov momentum, GPA eliminates this complexity by decoupling Nesterov’s interpolation constants to enable smooth iterate averaging at every step. Structurally, GPA resembles Schedule-Free but replaces uniform averaging with exponential moving averaging. Empirically, GPA […]

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Engineering teams often think about infrastructure only when something breaks. A remote connection fails. A server becomes unstable. A workstation loses access to a critical system. A field team cannot synchronize data. An industrial environment starts showing communication issues that no one can explain clearly. At that point, infrastructure suddenly becomes visible. But by then, the real problem has usually been present for a long time. In my experience working with networks, servers, secure connectivity, technical support, and […]

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arXiv:2402.06223v3 Announce Type: replace-cross Abstract: Directed Acyclic Graphs (DAGs) are a standard tool in causal modeling, but their suitability for capturing the complexity of large-scale multimodal data is questionable. In practice, real-world multimodal datasets are often collected from heterogeneous generative processes that do not conform to a single DAG. Instead, they may involve multiple, and even opposing, DAG structures with inverse causal directions. To address this gap, in this work, we first propose a novel latent partial causal […]

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arXiv:2605.10959v1 Announce Type: new Abstract: There is currently no unified metric for evaluating the efficiency of quantized neural networks. We propose QuIDE, built around the Intelligence Index I = (C x P)/log_2(T+1), which collapses the compression-accuracy-latency trade-off into a single score. Experiments across six settings — SimpleCNN (MNIST, CIFAR), ResNet-18 (ImageNet-1K), and Llama-3-8B — show a task-dependent Pareto Knee. 4-bit quantization is optimal for MNIST and large LLMs, while 8-bit is the sweet spot for complex CNN tasks […]

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arXiv:2601.01069v1 Announce Type: cross Abstract: Non-stationary parametric bandits have attracted much attention recently. There are three principled ways to deal with non-stationarity, including sliding-window, weighted, and restart strategies. As many non-stationary environments exhibit gradual drifting patterns, the weighted strategy is commonly adopted in real-world applications. However, previous theoretical studies show that its analysis is more involved and the algorithms are either computationally less efficient or statistically suboptimal. This paper revisits the weighted strategy for non-stationary parametric bandits. In […]

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arXiv:2603.28780v1 Announce Type: new Abstract: In this paper, we study the problem of distributed training (DT) under Byzantine attacks with communication constraints. While prior work has developed various robust aggregation rules at the server to enhance robustness to Byzantine attacks, the existing methods suffer from a critical limitation in that the solution error does not diminish when the local gradients sent by different devices vary considerably, as a result of data heterogeneity among the subsets held by different […]

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