Denny Zhou: Least-to-Most Prompting Was an Early Agent Harness, Hitting 99% on SCAN
denny_zhou · x · 2026-09-08
Google researcher Denny Zhou reframes his 2022 least-to-most prompting work in today's language: it was an early agent harness — decompose a complex task into smaller ones, then repeatedly call an LLM to solve them one by one ("Decompose. Solve. Repeat.").
- On the compositional generalization benchmark SCAN, least-to-most prompting solved any split (including the length split) with at least 99% accuracy using just 14 exemplars, versus only 16% for chain-of-thought prompting.
- The retrospective connects 2022-era prompting research directly to today's agent orchestration ideas: multi-step decomposition and repeated LLM calls are the core of modern agent frameworks.
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