TGO-MAS: TEMPORAL GRAPH ORGANIZATION FOR MULTI-AGENT SYSTEMS

Large language model systems with multiple agents must decide both which agents interact and when each interaction becomes available during execution over multiple steps. Workflow descriptions and static communication graphs can encode endpoints or order, but they do not make the activation step of a typed interaction an explicit decision variable. We therefore formulate temporal graph organization that is aware of execution steps around the insight that interaction timing is part of organization rather than execution metadata. TGO-MAS instantiates this formulation as a closed scheduling loop. It represents candidates that pair typed interactions with activation steps, scores them from the observed execution prefix, selects a feasible set for the next step, and revises the next decision after delivery, failure, or completion. Structural analysis establishes several consequences of this construction. Static projections can discard temporal distinctions, static schedules are contained in the action space for the next step, and the optional accounting and event record extensions satisfy stated feasibility and reconstruction conditions. TGO-MAS outperforms the strongest baseline, the DyTopo reimplementation, by 2.94% on HumanEval-heldout-124 and 1.88% on MMLU-validation-1531.