Accept, Challenge, or Amend? Receiver-Side Information Handling in Multi-Agent Systems

In multi-agent systems, specifying who communicates with whom does not determine how a receiving agent should handle incoming information. We formulate receiver-side information handling as a separately configurable system design dimension and study three representative policies: ACCEPT, in which the receiver uses the sender’s information as the basis for subsequent actions; CHALLENGE, in which the receiver returns local evidence and requests sender-side revision; and LOCAL AMEND, in which the receiver locally adjusts its subsequent actions within its own responsibility boundary. Using Qwen3-Max and GPT-5.6-Luna, we conduct paired end-to-end evaluations across ALFWorld, AppWorld, ToolSandbox, and Collab-Overcooked, together with controlled handoff experiments in ALFWorld to investigate the underlying mechanisms. Our results show that different fixed handling policies lead to distinct execution trajectories, realized intervention behavior, and computational costs, while no single fixed policy consistently dominates across the tested environments and model backbones. Controlled experiments further show that when receivers possess diagnostic local evidence, challenging or locally amending erroneous handoffs substantially improves immediate repair; in contrast, interventions without such evidence can incur additional cost without improving final task performance. Motivated by these findings, we introduce an evidence-gated adaptive receiver-side handling strategy that dynamically selects among ACCEPT, CHALLENGE, and LOCAL AMEND at each information handoff based on local conflict evidence, repair reliability, and responsibility boundaries. Across the eight model–environment combinations, the adaptive strategy attains or ties the highest primary task-performance point estimate in seven cases and remains close to the best fixed policy in the remaining case. While not always minimizing token usage, it exhibits more consistent relative performance across environments and model backbones and generally avoids the highest token costs associated with fixed policies. Overall, our findings establish receiver-side information handling as an important system design dimension that should be explicitly configured and evaluated in multi-agent systems.