Revisiting Multi-Agent Debate as Test-Time Scaling: A Systematic Study
  of Conditional Effectiveness
The remarkable growth in large language model (LLM) capabilities has spurred
exploration into multi-agent systems, with debate frameworks emerging as a
promising avenue for enhanced problem-solving. These multi-agent debate (MAD)
approaches, where agents collaboratively present, critique, and refine
arguments, potentially offer improved reasoning, robustness, and diverse
perspectives over monolithic models. Despite prior studies leveraging MAD, a
systematic understanding of its effectiveness compared to self-agent methods,
particularly under varying conditions, remains elusive. This paper seeks to
fill this gap by conceptualizing MAD as a test-time computational scaling
technique, distinguished by collaborative refinement and diverse exploration
capabilities. We conduct a comprehensive empirical investigation comparing MAD
with strong self-agent test-time scaling baselines on mathematical reasoning
and safety-related tasks. Our study systematically examines the influence of
task difficulty, model scale, and agent diversity on MAD's performance. Key
findings reveal that, for mathematical reasoning, MAD offers limited advantages
over self-agent scaling but becomes more effective with increased problem
difficulty and decreased model capability, while agent diversity shows little
benefit. Conversely, for safety tasks, MAD's collaborative refinement can
increase vulnerability, but incorporating diverse agent configurations
facilitates a gradual reduction in attack success through the collaborative
refinement process. We believe our findings provide critical guidance for the
future development of more effective and strategically deployed MAD systems.
68390972be7fee960c0b07ee
Yongjin Yang
68390972be7fee960c0b07ef
Euiin Yi
claimed_verified
2025-06-02T07:47:31.722Z
66027cead56cd39a44992631
/avatars/3c8b679bc72f30c547782579e9592bb1.svg
Euiin Yi
user
euiin
68390972be7fee960c0b07f0
Jongwoo Ko
claimed_verified
2025-05-30T06:54:24.221Z
64b7628af902508f0d7ae112
/avatars/83c155254486e80c1dfd14676fdf9215.svg
Jongwoo Ko
user
jongwooko
68390972be7fee960c0b07f1
Kimin Lee
68390972be7fee960c0b07f2
Zhijing Jin
68390972be7fee960c0b07f3
Se-Young Yun
Revisiting Multi-Agent Debate as Test-Time Scaling: A Systematic Study
  of Conditional Effectiveness
The remarkable growth in large language model (LLM) capabilities has spurred
exploration into multi-agent systems, with debate frameworks emerging as a
promising avenue for enhanced problem-solving. These multi-agent debate (MAD)
approaches, where agents collaboratively present, critique, and refine
arguments, potentially offer improved reasoning, robustness, and diverse
perspectives over monolithic models. Despite prior studies leveraging MAD, a
systematic understanding of its effectiveness compared to self-agent methods,
particularly under varying conditions, remains elusive. This paper seeks to
fill this gap by conceptualizing MAD as a test-time computational scaling
technique, distinguished by collaborative refinement and diverse exploration
capabilities. We conduct a comprehensive empirical investigation comparing MAD
with strong self-agent test-time scaling baselines on mathematical reasoning
and safety-related tasks. Our study systematically examines the influence of
task difficulty, model scale, and agent diversity on MAD's performance. Key
findings reveal that, for mathematical reasoning, MAD offers limited advantages
over self-agent scaling but becomes more effective with increased problem
difficulty and decreased model capability, while agent diversity shows little
benefit. Conversely, for safety tasks, MAD's collaborative refinement can
increase vulnerability, but incorporating diverse agent configurations
facilitates a gradual reduction in attack success through the collaborative
refinement process. We believe our findings provide critical guidance for the
future development of more effective and strategically deployed MAD systems.
Multi-agent debate systems offer variable benefits over self-agent approaches in mathematical reasoning and safety tasks, with performance influenced by task difficulty, model scale, and agent diversity.
large language model multi-agent systems debate frameworks multi-agent debate collaborative refinement diverse exploration self-agent methods test-time computational scaling mathematical reasoning safety-related tasks agent diversity collaborative refinement process