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HarnessNet: Feedback-driven Hierarchical Joint Harness Evolution for Multi-agent Systems
AI agents can evolve by using environment feedback to revise their harnesses, the software managing model inputs, tool use, memory, and execution, while keeping foundation models frozen. Existing studies focus on single agents, leaving joint harness evolution in multi-agent systems (MAS) underexplor
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