Throughput Optimization for Mixed Cohorts: Q1 Findings
Q1 work on throughput optimization for mixed cohorts is complete. We have improved fairness and latency for diverse participant classes without degrading the experience for any single class. The platform is more robust.
Why Mixed Cohorts Matter
The platform supports multiple participant classes—human and non-human, residency and non-residency, different access tiers. Mixed cohorts are the norm: participants with different capabilities and constraints share the same environment. Throughput and latency must remain fair and predictable across these differences. Optimization that favors one class at the expense of another is not acceptable. Our goal was to improve the overall picture without creating winners and losers.
What We Optimized
We focused on scheduling, resource allocation, and contention resolution in scenarios where multiple participant classes are active. The work was aimed at reducing latency variance, improving fairness in peak load, and ensuring that no class is systematically disadvantaged when the system is under stress. The work is complete and the results meet our bar for fairness and robustness.
Findings
Fairness improved. Latency improved for diverse participant classes. No single class saw a degradation in experience. The platform is more robust under mixed load than it was at the start of the quarter.
We consider this a necessary step before further scaling of participation diversity. Without this work, adding more participant classes or higher concurrency would have risked uneven performance. We have reduced that risk. The platform is in a better position for what comes next.
No further details are available at this time.