Curve-1: A Reasoning Architecture for Cross-Layer Infrastructure Intelligence

Abstract

Modern AI and datacenter infrastructure has grown too complex, too interdependent, and too fast-moving for human operators or conventional single-layer monitoring tools to reason about reliably. Curve-1 is Raincurve’s four-stage pipeline—pre-filter scoring, contrastive trajectory encoding, failure-hypothesis generation, and cross-signal incident clustering—for converting fragmented operational telemetry into ranked, explainable root-cause hypotheses in near real time. In production evaluation across more than 16,000 CI/CD trajectories, it contributed to a 50% reduction in mean time to resolution.

Method

An IQL-based scorer continuously prioritizes high-risk telemetry before a contrastively trained temporal encoder maps cross-layer event sequences into a shared latent space. A latent diffusion model then generates calibrated root-cause hypotheses from incomplete evidence, while Hawkes-process clustering gathers related network, compute, cloud, and application symptoms into one incident narrative.

Data and validation

Curve-1 was trained across more than 34 repositories using 95 confirmed failure trajectories in 13 categories. A subsequent multi-language pass achieved approximately 0.88 AUC on held-out data, while a Cosmed deployment achieved an AUC-ROC of 0.897 and reduced MTTR by 50%.

Conclusion

The architecture treats cross-layer reliability as a structured reasoning problem rather than a single-model scaling exercise: each stage remains inspectable, and the pre-filter keeps continuous operation economically practical.