The Interaction Tax: When Communication Erases Diversity in Multi-Agent Teams
Does multi-agent LLM interaction help or hurt? Some work reports gains from debate (Du et al., 2024), critique loops (Chen et al., 2025), and mixture-of-agents synthesis (Wang et al., 2025), while other work finds that interaction adds cost without improving quality under equal budgets (Tran & Kiela, 2026; Xu et al., 2026; Jarrett et al., 2025), or that independent sampling already captures multi-agent gains (Li et al., 2024). We argue this contradiction partly reflects a missing distinction, because not all multi-agent communication is equal. Different model families find structurally differe
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- FuzzySimilar title/name (fuzzy) · 59%AgentCore-8B →
“Fuzzy title match (0.73): “The Interaction Tax: When Communication Erases Diversity in ” ≈ “AgentCore-8B””
- PossiblePossibly related (embedding) · 51%Capable language models can outgrow the benefits of collaboration →
- PossiblePossibly related (embedding) · 47%New LLM Coordination Benchmark - Benchmarking Open-Ended Multi-Agent Coordination in Language Agents [R] →
- PossiblePossibly related (embedding) · 46%Most AI agents have no concept of opportunity cost [D] →
- PossiblePossibly related (embedding) · 45%Patterns and problems in emerging multi-agent systems →
- FuzzySimilar title/name (fuzzy) · 87%SWE-agent/SWE-agent →
“Fuzzy title match (0.94): “The Interaction Tax: When Communication Erases Diversity in ” ≈ “SWE-agent/SWE-agent””
- FuzzySimilar title/name (fuzzy) · 87%zhayujie/CowAgent →
“Fuzzy title match (0.94): “The Interaction Tax: When Communication Erases Diversity in ” ≈ “zhayujie/CowAgent””
- FuzzySimilar title/name (fuzzy) · 66%open-multi-agent/open-multi-agent →
“Fuzzy title match (0.78): “The Interaction Tax: When Communication Erases Diversity in ” ≈ “open-multi-agent/open-multi-agent””