Rollback the World, Keep the Reflection: Rollback-Induced Reflection for Long-Horizon LLM Agents

Excerpt

arXiv:2609.18304v2 Announce Type: replace Abstract: Large language model (LLM) agents increasingly tackle long-horizon tasks through multi-step environment interaction, yet a single erroneous action can alter subsequent states and observations, causing errors to compound over time. Existing methods either correct the context without repairing altered environment states or restore earlier states while discarding useful experience, making it difficult to both eliminate failure conditions and avoid