5 accepted papers
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
Estimating counterfactual outcomes from observational data is critical for informed decision-making in domains such as personalized marketing, healthcare, and online platforms. In these contexts, decision processes frequently involve high-dimensional combinatorial interventions, including bundled ch
Sequential recommendation (SR) aims to predict users
Agent planning faces dynamic heterogeneity—nonstationary observations, dynamics, and objectives with sparse, delayed rewards—which dominant methods largely ignore, leading to poor generalization under environment shifts. We propose Flow-Matching for Agent Planning (FlowMAP), which formulates plannin…
In recent years, 3D spatial crowdsourcing platforms become popular, in which users and workers travel together to their assigned workplaces for services, such as InterestingSport and Nanguache. A typical problem over 3D spatial crowdsourcing platforms is to match users with suitable workers and work…