Context Learning for Multi-Agent Discussion
Multi-Agent Discussion (MAD) has garnered increasing attention very recently, where multiple LLM instances collaboratively solve problems via structured discussion. However, we find that current MAD methods easily suffer from discussion inconsistency, LLMs fail to reach a coherent solution, due to the misalignment between their individual contexts.In this paper, we introduce a multi-LLM context learning method (M2CL) that learns a context generator for each agent, capable of dynamically generating context instructions per discussion round via automatic information organization and refinement. Specifically, inspired by our theoretical insights on the context instruction, M2CL train the generators to control context coherence and output discrepancies via a carefully crafted self-adaptive mechanism.It enables LLMs to avoid premature convergence on majority noise and progressively reach the correct consensus. We evaluate M2CL on challenging tasks, including academic reasoning, embodied tasks, and mobile control. The results show that the performance of M2CL significantly surpasses existing methods by 20%--50%, while enjoying favorable transferability and computational efficiency.
69832a259084cb4f0ecb597b
Xingyuan Hua
claimed_verified
2026-02-04T19:03:49.511Z
68ad8ff044c8254ac4b6ad6e
/avatars/a566b764e100edf1e2d633623e32f5cf.svg
Xingyuan Hua
hansenhua
user
hansenhua
69832a259084cb4f0ecb597c
Sheng Yue
69832a259084cb4f0ecb597d
Xinyi Li
69832a259084cb4f0ecb597e
Yizhe Zhao
69832a259084cb4f0ecb597f
Jinrui Zhang
69832a259084cb4f0ecb5980
Ju Ren
Context Learning for Multi-Agent Discussion
Multi-Agent Discussion (MAD) has garnered increasing attention very recently, where multiple LLM instances collaboratively solve problems via structured discussion. However, we find that current MAD methods easily suffer from discussion inconsistency, LLMs fail to reach a coherent solution, due to the misalignment between their individual contexts.In this paper, we introduce a multi-LLM context learning method (M2CL) that learns a context generator for each agent, capable of dynamically generating context instructions per discussion round via automatic information organization and refinement. Specifically, inspired by our theoretical insights on the context instruction, M2CL train the generators to control context coherence and output discrepancies via a carefully crafted self-adaptive mechanism.It enables LLMs to avoid premature convergence on majority noise and progressively reach the correct consensus. We evaluate M2CL on challenging tasks, including academic reasoning, embodied tasks, and mobile control. The results show that the performance of M2CL significantly surpasses existing methods by 20%--50%, while enjoying favorable transferability and computational efficiency.
Multi-Agent Discussion methods suffer from inconsistency due to individual context misalignment, which is addressed through a context learning approach that dynamically generates context instructions for each agent to improve consensus reaching and performance.
multi-LLM context learning context generator discussion consistency context coherence self-adaptive mechanism premature convergence consensus reaching