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Fudan University, Academy
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OrchBench reveals that preserving task-critical information is more crucial than simply increasing agent numbers, challenging conventional wisdom in multi-agent system design.
Watermarking LLMs by embedding the signal into the reasoning process itself proves surprisingly robust against fine-tuning and other post-training modifications.
