2026
Counterfactual Planning for Generalizable Agents’ Actions
AAAI 2026technical
Large language models have revolutionized agent planning by serving as the engine of heuristic guidance. However, LLM-based agents often struggle to generalize across complex environments and to adapt to stochastic feedback arising from environment–action interactions. We propose Counterfactual Plan