Failure-Aware Routing Framework for Multi-Agent Spatiotemporal Reasoning
The STAR (Spatio-Temporal Agent Router) framework introduces a novel solution for failure-aware routing in multi-agent spatiotemporal reasoning systems. It externalizes control among agents by utilizing a state-conditioned transition policy based on the current agent, task type, and execution status. At the heart of STAR lies an agent routing matrix that merges expert-defined nominal routes with recovery transitions derived from execution traces. This framework is designed for complex spatiotemporal reasoning tasks that involve various specialists, including geometric, temporal, topological, and trajectory agents. Current multi-agent LLM systems often leave routing decisions implicit, complicating recovery efforts. STAR enhances recovery interpretability and optimization by focusing on specific failure types. This research is available on arXiv, under ID 2605.10057.
Key facts
- STAR stands for Spatio-Temporal Agent Router
- Framework externalizes inter-agent control as a state-conditioned transition policy
- Agent routing matrix combines expert-specified nominal routes with learned recovery transitions
- Targets compositional spatiotemporal reasoning with heterogeneous specialists
- Addresses failures in geometric, temporal, topological, and trajectory agents
- Existing systems leave routing implicit in language generation
- STAR conditions on current agent, task type, and typed execution status
- Published on arXiv with ID 2605.10057
Entities
Institutions
- arXiv