Rachel St. Clair | Searcharxiv

arXiv2026

The .serva Standard: One Primitive for All AI Cost Reduced, Barriers Removed

Artificial Intelligence (AI) infrastructure faces two compounding crises. Compute payload - the unsustainable energy and capital costs of training and inference - threatens to outpace grid capacity and concentrate capability among a handful of organizations. Data chaos - the 80% of project effort consumed by preparation, conversion, and preprocessing - strangles development velocity and locks datasets to single model architectures. Current approaches treat these as separate problems, managing each with incremental optimization while increasing ecosystem complexity. This paper presents ServaStack: a universal data format (.serva) paired with a universal AI compute engine (Chimera). The .serva format achieves lossless compression by encoding information using laser holography principles, while Chimera converts compute operations into a representational space where computation occurs directly on .serva files without decompression. The result is automatic data preprocessing. The Chimera engine enables any existing model to operate on .serva data without retraining, preserving infrastructure investments while revamping efficiency. Internal benchmarks demonstrate 30-374x energy efficiency improvements (96-99% reduction), 4x-34x lossless storage compression, and 68x compute payload reduction without accuracy loss when compared to RNN, CNN, and MLP models on FashionMNIST and MNIST datasets. At hyperscale with one billion daily iterations, these gains translate to $4.85M savings per petabyte per training cycle. When any data flows to any model on any hardware, the AI development paradigm shifts. The bottleneck moves from infrastructure to imagination.

cs.IT↗

arXiv2020

Using Conditional Generative Adversarial Networks to Reduce the Effects of Latency in Robotic Telesurgery

The introduction of surgical robots brought about advancements in surgical procedures. The applications of remote telesurgery range from building medical clinics in underprivileged areas, to placing robots abroad in military hot-spots where accessibility and diversity of medical experience may be limited. Poor wireless connectivity may result in a prolonged delay, referred to as latency, between a surgeon's input and action a robot takes. In surgery, any micro-delay can injure a patient severely and in some cases, result in fatality. One was to increase safety is to mitigate the effects of latency using deep learning aided computer vision. While the current surgical robots use calibrated sensors to measure the position of the arms and tools, in this work we present a purely optical approach that provides a measurement of the tool position in relation to the patient's tissues. This research aimed to produce a neural network that allowed a robot to detect its own mechanical manipulator arms. A conditional generative adversarial networks (cGAN) was trained on 1107 frames of mock gastrointestinal robotic surgery data from the 2015 EndoVis Instrument Challenge and corresponding hand-drawn labels for each frame. When run on new testing data, the network generated near-perfect labels of the input images which were visually consistent with the hand-drawn labels and was able to do this in 299 milliseconds. These accurately generated labels can then be used as simplified identifiers for the robot to track its own controlled tools. These results show potential for conditional GANs as a reaction mechanism such that the robot can detect when its arms move outside the operating area within a patient. This system allows for more accurate monitoring of the position of surgical instruments in relation to the patient's tissue, increasing safety measures that are integral to successful telesurgery systems.

cs.CV↗