arXiv:2605.29116v1 Announce Type: new Abstract: When multiple LLM agents solve the same problem, standard practice compresses each agent's reasoning into a majority vote or layered synthesis, treating agreement as the finish line. We show this is unnecessarily lossy: an LLM aggregator that reads complete reasoning traces recovers correct solutions even when agents unanimously agree, with beneficial corrections consistently outweighing harmful ones -- the \emph{aggregation paradox}. Majority voting has a ceiling that perturbation diversity does not raise (error correlations are identical); the
Ongoing research into multi-agent systems and the limitations of current aggregation techniques is prompting novel approaches to improve LLM performance and reliability.
This development suggests a significant leap in how AI agents can collaboratively solve problems, potentially leading to more robust and accurate AI applications in complex domains.
The shift from simple consensus to trace-level synthesis fundamentally alters the aggregation strategy for multi-LLM outputs, allowing for error correction even in unanimous agreement scenarios.
- · AI developers
- · Enterprises deploying LLM agents
- · Users of AI-powered solutions
- · AI research institutions
- · Traditional majority voting systems
Improved accuracy and reliability of AI agent systems across various applications.
Accelerated adoption of complex multi-agent AI solutions for critical tasks currently requiring human oversight.
The development of new frameworks and standards for agent collaboration and trace analysis becomes a major area of AI engineering.
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Read at arXiv cs.AI