DeliberationBench: Multi-LLM Deliberation Underperforms Baseline, Raising Questions on Complexity
Published:Jan 15, 2026 05:00
•1 min read
•ArXiv NLP
Analysis
This research provides a crucial counterpoint to the prevailing trend of increasing complexity in multi-agent LLM systems. The significant performance gap favoring a simple baseline, coupled with higher computational costs for deliberation protocols, highlights the need for rigorous evaluation and potential simplification of LLM architectures in practical applications.
Key Takeaways
- •Multi-LLM deliberation protocols were benchmarked against a single-output baseline.
- •The baseline significantly outperformed all deliberation protocols in terms of accuracy.
- •Deliberation protocols incurred higher computational costs than the baseline.
Reference
“the best-single baseline achieves an 82.5% +- 3.3% win rate, dramatically outperforming the best deliberation protocol(13.8% +- 2.6%)”