Predict Before You Execute: Faster, Smarter AI Agents

Kari Jaaskelainen

12 Jan 2026 — 1 min read

AI agents usually follow a slow cycle: generate an idea, execute it, then check the results. This study shows a faster path: teach agents to predict outcomes first, then verify only the most promising options.

They build “execution priors” (inspired by world models) so runtime checks can be replaced by instant predictive reasoning.
New benchmark: Data-centric Solution Preference with 18,438 pairwise comparisons to train/test solution picking without execution.
When primed with a Verified Data Analysis Report, large language models reached 61.5% accuracy with well-calibrated confidence.
FOREAGENT uses a Predict-then-Verify loop, converging ~6× faster and outperforming execution-only baselines by +6% in their tests.

Why it matters: Less waiting, less compute, and more informed choices—especially useful in scientific discovery and data analysis.

Paper: https://arxiv.org/abs/2601.05930 Code (coming soon): https://github.com/zjunlp/predict-before-execute

Paper: https://arxiv.org/abs/2601.05930v1

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