arXiv:2604.23057v2 Announce Type: replace Abstract: We investigate whether explicit belief graphs improve LLM performance in cooperative multi-agent reasoning. Through 3,000+ controlled trials across four LLM families in the cooperative card game Hanabi, we establish four findings. First, integration architecture determines whether belief graphs provide value: as prompt context, graphs are decorative for strong models and beneficial only for weak models on 2nd-order Theory of Mind (80% vs 10%, p<0.0001, OR=36.0); when graphs gate action selection through ranked shortlists, they become structur
The proliferation of LLMs creates an immediate need to enhance their reasoning capabilities, especially in complex, multi-agent environments, making research into architectural improvements timely.
This research provides crucial insights into how to integrate structured knowledge, like belief graphs, with LLMs to significantly improve their performance, particularly for weaker models and higher-order reasoning, which is vital for developing robust AI agents.
The understanding that integration architecture, not just the presence of external knowledge, is paramount for LLM performance, specifically highlighting the value of structured graphs in gating action selection rather than merely serving as prompt context.
- · AI agent developers
- · LLM researchers focused on reasoning
- · Companies building multi-agent systems
- · LLM providers relying solely on scale for reasoning
- · Applications requiring complex, cooperative multi-agent reasoning without struct
More efficient and capable AI agents emerge due to improved reasoning architectures.
The competitive landscape for AI models shifts, favoring those with superior integration of structured knowledge for reasoning.
Complex, cooperative tasks in various industries become increasingly amenable to AI automation, accelerating market disruption.
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Read at arXiv cs.AI