@inproceedings{wunderlich-etal-2026-multi,
    title = "Multi-Agent Reasoning Improves Compute Efficiency: {P}areto-Optimal Test-Time Scaling",
    author = "Wunderlich, Florian Valentin  and
      Kaesberg, Lars Benedikt  and
      Wahle, Jan Philip  and
      Ruas, Terry  and
      Gipp, Bela",
    editor = "T.Y.S.S., Santosh  and
      Rodriguez, Juan Diego  and
      de Gibert, Ona",
    booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics ({ACL} 2026)",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.acl-srw.1/",
    pages = "1--14",
    ISBN = "979-8-89176-393-7",
    abstract = "Advances in inference methods have enabled language models to improve their predictions without additional training. These methods often prioritize raw performance over cost-effective compute usage. However, computational efficiency is key for real-world applications with resource constraints. We provide a systematic analysis of the inference scaling strategies *self-consistency*, *self-refinement*, *multi-agent debate*, and *mixture-of-agents*, to study their computational performance tradeoffs. We evaluate methods on two reasoning benchmarks (MMLU-Pro, BBH) and include extensive parameter configurations (e.g., scaling the number of parallel predictions, agents, and debate rounds) across different model sizes. Across 34 configurations and over 100 evaluations, we compute the Pareto-optimal front to select methods that achieve the best accuracy with the lowest computational budget.Notably, inference scaling improves accuracy by up to +7.1{\%} points over chain-of-thought at the highest evaluated budgets (20{\texttimes} the CoT compute budget) on MMLU-Pro. With an equal computing budget, debate and mixture-of-agents outperform self-consistency by 1.3{\%} and 2.7{\%} points, respectively. While self-consistency saturates earlier, multi-agent gains persist, particularly on more complicated tasks. We identify a simple multi-agent design guideline: mixture-of-agents is most efficient when the number of parallel generations exceeds the number of sequential aggregations."
}