GateTree: Gated Context Sharing for Adaptive Multi-Agent Collaboration

Parallel multi-agent systems share intermediate findings to coordinate different solution paths. As these paths evolve, however, shared information can become outdated or inapplicable, propagating unsupported assumptions and causing redundant work. Effective collaboration requires selecting information that remains relevant to each branch and adapting its plan accordingly. We introduce GateTree, a multi-agent framework that couples shared-context management with adaptive planning. GateTree maintains two concurrent branches with complementary conservative and exploratory strategies. A GateAgent checks the provenance and scope of shared records and selects context for each branch. An adaptive planner then uses this context and execution feedback to revise branch objectives and verification targets, preserving useful progress while addressing unresolved requirements. Across SWE-bench Pro, text-only HLE, and GAIA2, GateTree achieves the highest average Pass@2 under both backbone models while reducing cost by an average of 30.17% relative to each setting's strongest Pass@2 baseline. Replacing active context management with passive sharing reduces Pass@2 by 11.50 points in the component ablation. Further analysis shows that selective delivery is most beneficial for long contexts with sparse relevant evidence, whereas full-context delivery can be more effective when relevant evidence is dense.