Same Pieces, Different Servers: A Tetris Benchmark for AI Agents as Served

Abstract:An agent meets a model as served: through an endpoint with a price card, a shared cache and other tenants, or on whatever hardware a self-hosted model runs. Benchmarks rank the weights. We introduce a Tetris benchmark that measures what agents get from models as served: every move is scored against an oracle, and every agent receives the same pieces. In five pre-specified experiments with nine open-weight models on one serverless provider, plus an open decision model self-hosted on a CPU, we find that price and size do not predict decision quality; that resending history costs almost nothing when cached input is free, would cost eleven to twelve times more if it were not, and makes play worse; that deployments keep between 4 and more than 64 agent contexts warm, in line with their throughput rather than the model's KV-cache size; that an agent's own long requests slow its slowest short decisions more than tenfold, which a simple admission rule cuts by 59% without hurting play; and that the decision model plays mid-pack but takes 27 s per move on a CPU, about 650 times longer than reported on a GPU. Architecture predicts some of what an agent sees; the deployment sets the rest.

11 pages, 7 figures, 3 tables. Code, raw logs and animations: this https URL

Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Robotics (cs.RO)

I.2.6; I.2.8; F.2.2

arXiv:2603.02348 [cs.LG]

(or arXiv:2603.02348v2 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2603.02348 Focus to learn more arXiv-issued DOI via DataCite

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