A frontier AI model is trained in an AI data center, on tens of thousands of accelerators joined by a dedicated network, and every training step ends with all of them exchanging data at once. Between
Halley is a predictive control framework for AI data center optical networks that improves effective throughput and reduces capacity needs by forecasting failures and controlling resources with formal guarantees.
A frontier AI model is trained in an AI data center, on tens of thousands of accelerators joined by a dedicated network, and every training step ends with all of them exchanging data at once. Between racks, that network is optical, and each of its four layers, from lasers to switches, runs a fixed or after-the-fact policy. Lasers sharing an enclosure, cooling, and power can fail together. At a fixed spare ratio, correlated failure provably caps a pool's availability however large it grows. In this paper, we present a comprehensive predictive control plane framework, called Halley, that reads each resource's next state and acts before the failure occurs. It has four components, each…
Summary and abstract excerpt via Hugging Face Papers ↗ · arXiv.
Halley: A Predictive Control Plane for Optical AI Data Center Networks was first seen on Hugging Face · Daily papers on Nov 5, 2026. Since then it has appeared in 37 checks on 4 different days. Its best position was #5 of 30, reached on Nov 6, 05:28 UTC. It is #10 right now, holding steady at #10 over the last 6 checks.
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Yes. As of Nov 8, 17:28 UTC it is #10, holding steady at #10 over the last 6 checks.
#5 on Hugging Face · Daily papers, on Nov 6, 05:28 UTC. Trendoday checks this list every 120 minutes.
It was first seen on Nov 5, 2026 and has been on the list on 4 days (37 checks) since.