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This paper delineates the capability boundaries of multi-agent collaboration in LLM-based systems, showing benefits only in specific task structures like long-horizon tasks with sparse dependencies, and proposes SAIGE, a dynamic graph-based mechanism for efficient collaboration.
Introduces AgentRevive, a Markov state-aware framework for resilient multi-agent collaboration that uses soft state transitions (Active, Standby, Terminated) to prevent premature pruning of agents that may recover, reducing token consumption while improving performance on reasoning and domain tasks.