Repair the Amplifier, Not the Symptom: Stable World-Model Correction for Agent Rollouts
Long-horizon language agents increasingly maintain executable world models in the form of planning graphs, where tool calls, validators, memory updates, recovery branches, and final answers are connected by typed dependencies. When a rollout fails, repairing the most visible error can leave the underlying error-amplification path intact, while replaying the full graph is expensive and difficult for long-context models to use reliably. We study world-model correction: selecting a compact subgraph of a failed planning graph whose repair stabilizes subsequent rollouts. We first instantiate a strong family of engineering correctors, including pointwise error scans, TopK and window selection, ...
| Authors | Xinyuan Song, Zekun Cai |
|---|---|
| Primary category | cs.AI |
| Published | 2 July 2026 |
| Last updated at source | 5 July 2026 |
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