Capable language models can outgrow the benefits of collaboration

TL;DR - A controlled study across 260 configurations finds that multi-agent collaboration can help language models but may become counterproductive as individual models grow more capable. A predictive model selected the best architecture for 87% of held-out, within-domain configurations.

• Evaluates LLM-agent collaboration under 260 controlled configurations.
• Shows that collaboration’s benefits depend on model capability and system architecture.
• Finds that stronger models can outgrow the advantages of multi-agent setups.
• Introduces an architecture selector with 87% within-domain held-out accuracy.