Abstract
Modern distributed computing systems generate extremely high-volume log, metric, and trace data characterized by noise dominance, weak signal sparsity, and non-stationary behavior. Traditional observability systems rely on thresholding, heuristics, or supervised anomaly detection, which degrade under scale and evolving system dynamics.
This paper introduces AugurNet, a generative AI-based diagnostic framework designed for high-entropy telemetry environments. The system replaces conventional filtering pipelines with a multi-hypothesis reasoning architecture that preserves ambiguity, models causal uncertainty explicitly, and performs generative reconstruction of latent failure structures.
Key contributions include:
- Polysemantic latent embeddings for multi-hypothesis state representation
- Distributed generative hypothesis engines for causal reasoning
- Iterative reinterpretation pipelines for ambiguity-preserving inference
- Adversarial noise inversion for extracting weak signals from high-noise streams
I describe architecture, training methodology, and a commercialization pathway toward autonomous infrastructure diagnosis systems.
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Recommended Citation
Manuel-Devadoss, Johny, "AugurNet: A Polysemantic Gen AI Architecture for Noise Suppression and Causal Diagnosis in High-Volume Log Systems", Technical Disclosure Commons, ()
https://www.tdcommons.org/dpubs_series/9958