Decision Protocols in Multi-Agent Large Language Model Conversations
Improving the task performance of Large Language Models (LLMs) is essential, yet scaling these models faces significant challenges such as diminishing returns and high costs. Multi-Agent Systems (MAS) offer a promising solution by distributing tasks among specialized agents to improve the overall task performance. This can reduce training costs at the expense of increased test time due to the discussion and decision-making process. The decision protocol is a critical component of MAS because it specifies how multiple agents collaborate to create a final solution. This thesis introduces the Multi-Agent LLM (MALLM) framework, which implements and evaluates various decision protocols, namely...
| Authors | Lars Benedikt Kaesberg |
|---|---|
| Primary category | cs.MA |
| Published | 6 July 2026 |
| Last updated at source | 6 July 2026 |
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