ATEST: Adaptive Resource-Aware Test-Time Scaling for LLM Inference
New preprint: arXiv:2610.25519 — ATEST: Adaptive Resource-Aware Test-Time Scaling for LLM Inference by Mobina Kashaniyan and Ali Jannesari.
ATEST is a resource-aware test-time scaling method, also called RATTS (Resource-Aware Test-Time Scaling). It targets self-consistency: the model draws several answers and keeps the plurality vote. Standard self-consistency uses a fixed sample count whether the GPU is idle or busy. ATEST reads the live GPU state and skips extra self-consistency samples when they are not worth their current energy cost.
On an NVIDIA A100, the same four-sample query uses up to 4.1× more energy when other jobs load the GPU. A GSM8K query that costs 121.7 J on an idle GPU can cost 455.6 J on a busy one.
ATEST-Online, the default mode, needs no offline calibration. It starts with one sample, checks whether the plurality answer is stable, predicts the energy of the next step, and stops when another sample is not worth the cost. ATEST-Calibrated is an offline mode for stable deployments: it profiles a dataset once and picks the whole budget per query.
On GSM8K at high load, ATEST-Online keeps 60.0% accuracy and answers every query. ATEST-Calibrated drops to 44.0% because it defers 42.4% of queries. Including profiling, ATEST-Online uses 42× less GPU time and 48× less energy than ATEST-Calibrated. The same pattern holds on an NVIDIA L40S and with Qwen2.5-3B.
This work follows our earlier result that candidate count alone is not enough to describe the systems cost of test-time scaling. ATEST asks the next question: what does one more sample cost on the GPU right now?
Keywords: ATEST, RATTS, self-consistency, self consistency, resource-aware self-consistency, adaptive test-time scaling, LLM inference, GPU load, energy-aware systems, adaptive sampling, A100, L40S, Phi-3-mini, Qwen2.5-3B, GSM8K, arXiv:2610.25519.
Self-Consistency under GPU Load
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How to Cite
@misc{kashaniyan2026atest,
title = {ATEST: Adaptive Resource-Aware Test-Time Scaling for {LLM} Inference},
author = {Kashaniyan, Mobina and Jannesari, Ali},
year = {2026},
eprint = {2610.25519},
archivePrefix = {arXiv},
primaryClass = {cs.LG},
doi = {10.48550/arXiv.2610.25519},
url = {https://arxiv.org/abs/2610.25519}
}
Kashaniyan, M., & Jannesari, A. (2026). ATEST: Adaptive resource-aware test-time scaling for LLM inference. arXiv preprint arXiv:2610.25519. https://doi.org/10.48550/arXiv.2610.25519