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[ARTICLE · art-19026] src=arxiv.org pub= topic=large-language-models verified=true sentiment=↑ positive

Enhancing Multi-Agent Communication Through Attention Steering

Researchers have developed Agent-Radar, a training-free context management method that dynamically steers each agent's attention toward relevant information using a temporal and spatial decay mechanism. The method addresses performance degradation in LLM-based multi-agent systems caused by rapidly accumulating long conversation histories that dilute relevant context. Agent-Radar outperformed state-of-the-art methods across five benchmarks with gains of up to 7.64 absolute points, remaining effective as the number of agents and interaction rounds increased.

read2 min publishedMay 31, 2026
[Submitted on 28 May 2026]


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Abstract:LLM-based multi-agent systems have demonstrated remarkable performance on complex tasks through collaborative reasoning. However, these systems tend to rapidly accumulate extremely long conversation histories during interaction. As conversations lengthen, relevant information is increasingly diluted by irrelevant context, leading to degraded performance. In this work, we present Agent-Radar, a training-free context management method that dynamically steers each agent's attention toward relevant context with a novel temporal and spatial decay mechanism. Our experiments demonstrate that Agent-Radar outperforms state-of-the-art methods across five different benchmarks, yielding gains of up to 7.64 absolute points. Furthermore, our analysis shows that Agent-Radar remains effective and robust as the number of agents and interaction rounds increases. Finally, the ablation study shows that core components in Agent-Radar are crucial to performance and generalizable in different settings.

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