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EP184: Defeating the AI consensus trap
Title: The Consensus Trap: Rescuing Multi-Agent LLMs from Adversarial Majorities via Token-Level Collaboration
Source: http://arxiv.org/abs/2604.17139v1
Summary:
This paper introduces Token-Level Round-Robin Collaboration, a novel multi-agent interaction paradigm that interleaves token generation to overcome the brittle aggregation of majority voting. It provides a mathematical proof that this non-linear approach creates a dynamic chain of logic more robust to adversarial corruption and intermediate reasoning failures than traditional response-level methods.
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By Yun WuTitle: The Consensus Trap: Rescuing Multi-Agent LLMs from Adversarial Majorities via Token-Level Collaboration
Source: http://arxiv.org/abs/2604.17139v1
Summary:
This paper introduces Token-Level Round-Robin Collaboration, a novel multi-agent interaction paradigm that interleaves token generation to overcome the brittle aggregation of majority voting. It provides a mathematical proof that this non-linear approach creates a dynamic chain of logic more robust to adversarial corruption and intermediate reasoning failures than traditional response-level methods.