SYNAPSE-Bench: Structured Multi-Agent Coordination under Multimodal Partial Observability

Embodied agents are increasingly expected to operate in multi-agent environments, where effective collaboration requires not only perception and control, but also communication and shared reasoning under partial observability. However, how state representation affects heterogeneous collaboration under distributed observations remains insufficiently understood. In this work, we introduce SYNAPSE-Bench, a new benchmark for evaluating high-level decentralized coordination in heterogeneous multi-agent systems. SYNAPSE-Bench features teams of agents with different embodiments, including humanoid and quadruped robots, operating in indoor environments with both egocentric and exocentric visual inputs. Agents interact over multiple timesteps, generating both high-level actions from embodiment-conditioned action spaces and communication messages to accomplish shared tasks under decentralized execution. A key design principle of SYNAPSE-Bench is using structured state representations to study coordination under partial observability. We incorporate scene graphs as an explicit intermediate representation of objects and their relation, and formulate an evaluation setting that compares unstructured histories, local scene graphs, and fused scene graphs as decision interfaces. To support systematic study, we provide GRAPHSTITCH as a reference approach for aligning and fusing scene graphs across agents. We conduct extensive zero-shot evaluations and controlled ablations over communication and representation design. Our experimental results show that local structured representations improve task success for several VLM backbones, while graph fusion yields model-dependent gains and regressions. SYNAPSE-Bench provides a common evaluation framework for studying how state representation, communication, and model assignment shape embodied multi-agent coordination.