Progress in large language models is increasingly constrained by an evaluation bottleneck: benchmarks must be built and models run before iteration can begin. We investigate whether evaluation outcomes can be forecast before any experiments are conducted. Specifically, we study text-only performance prediction, where models estimate performance from task descriptions and experimental configurations alone, without access to dataset instances. To support systematic study, we curate PRECOG, a corpus of description-performance pairs spanning diverse tasks, domains, and metrics. We scrape task and configuration descriptions from arXiv, yielding 2,290 instances covering 1,519 papers, and construct a test split using papers published after the evaluated models' knowledge cutoff. Experiments show the task is challenging but feasible: reasoning models achieve a non-trivial forecasting skill reaching mean absolute error as low as 9.9 at high-confidence thresholds. Overall, our corpus and analyses offer an initial step toward open-ended anticipatory evaluation, supporting difficulty estimation and smarter resource allocation.
@article{arxiv.2509.20645,
title = {Anticipatory Evaluation of Language Models},
author = {Jungsoo Park and Ethan Mendes and Gabriel Stanovsky and Alan Ritter},
journal= {arXiv preprint arXiv:2509.20645},
year = {2026}
}