Before every command or code edit, the agent records a compact belief state: a summary of the current environment, a prediction of the action’s outcome, and a decision to trust that prediction or abstain.
Verified forecasts help coding agents choose safer, more effective actions
ANTE trains coding agents to predict each action’s outcome, checks those predictions through execution, and uses the resulting feedback for step-level reinforcement learning.
Big Tech
Yi Ge · Kangrui Ruan · Jifan Chen · Manish Prajapat · Christian Bock · Xiaoyang Wang · +3 more
University of Maryland, College Park · AWS AI Labs
Research Digest··3 min read
Thread:Agent Harness Optimization
Ge and colleagues introduce ANTE, a framework in which a coding agent states what it expects an action to do before executing it.
Why this paper
From AWS AI Labs and University of Maryland, College Park · Part of Agent Harness Optimization, now 133 papers
In one line
ANTE trains coding agents to predict action outcomes before executing, improving task success and world model accuracy.
What we could check
- ·No code link found
- ·No weights link found
- ·No dataset link found
- ·No compute details found
- ·No stated limitations found
- ✓Reports numbers on named benchmarks (3 benchmarks)
Observed from the paper text and links we have. Absence here means we did not find it, not that it does not exist.
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