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Predicting Task Difficulty Without Rollouts

Research LLM Agents

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TL;DR - An arXiv study on predicting agentic task difficulty ex ante — directly from a task description, before running expensive rollouts — evaluated across 17 agentic benchmarks. It matters because trial-and-error evaluation is a major compute bottleneck for long-horizon agents, and reliable forecasts would let designers calibrate benchmarks and build progressive training curricula.

  • Scope spans 17 agentic benchmarks covering coding, mathematics, machine learning, web navigation, and function calling, going beyond prior work limited to static tasks or isolated coding environments.
  • Warns that AUC as an evaluation metric can mask poor difficulty estimates, arguing prior work relied on inaccurate metrics and narrow features.
  • Identifies token-level entropy as a useful predictive signal for forecasting success likelihood.
  • Residuals between expected and observed difficulty surface hidden environment flaws such as data contamination and infeasible tasks.

Sources (1)

Predicting Task Difficulty Without Rollouts

arXiv cs.LG Stefan Krsteski, Charlotte Meyer 2026-08-06 arXiv:2608.05797
Public signals Semantic Scholar citations 0 · Semantic Scholar influential citations 0
Providers: Hugging Face · N/A OpenAlex · N/A Publisher · N/A Semantic Scholar · Citations 0 · Influential citations 0 X · N/A Fetched 2026-08-17 09:46:06.408908 UTC

TL;DR - An arXiv study on predicting agentic task difficulty ex ante — directly from a task description, before running expensive rollouts — evaluated across 17 agentic benchmarks. It matters because trial-and-error evaluation is a major compute bottleneck for long-horizon agents, and reliable forecasts would let designers calibrate benchmarks and build progressive training curricula.

  • Scope spans 17 agentic benchmarks covering coding, mathematics, machine learning, web navigation, and function calling, going beyond prior work limited to static tasks or isolated coding environments.
  • Warns that AUC as an evaluation metric can mask poor difficulty estimates, arguing prior work relied on inaccurate metrics and narrow features.
  • Identifies token-level entropy as a useful predictive signal for forecasting success likelihood.
  • Residuals between expected and observed difficulty surface hidden environment flaws such as data contamination and infeasible tasks.
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