Group Think: Multiple Concurrent Reasoning Agents Collaborating at Token
  Level Granularity
Authors: name: Chan-Jan Hsu, name: Davide Buffelli, name: Jamie McGowan, name: Feng-Ting Liao, name: Yi-Chang Chen, name: Sattar Vakili, name: Da-shan Shiu

Recent advances in large language models (LLMs) have demonstrated the power
of reasoning through self-generated chains of thought. Multiple reasoning
agents can collaborate to raise joint reasoning quality above individual
outcomes. However, such agents typically interact in a turn-based manner,
trading increased latency for improved quality. In this paper, we propose Group
Think--a single LLM that acts as multiple concurrent reasoning agents, or
thinkers. With shared visibility into each other's partial generation progress,
Group Think introduces a new concurrent-reasoning paradigm in which multiple
reasoning trajectories adapt dynamically to one another at the token level. For
example, a reasoning thread may shift its generation mid-sentence upon
detecting that another thread is better positioned to continue. This
fine-grained, token-level collaboration enables Group Think to reduce redundant
reasoning and improve quality while achieving significantly lower latency.
Moreover, its concurrent nature allows for efficient utilization of idle
computational resources, making it especially suitable for edge inference,
where very small batch size often underutilizes local~GPUs. We give a simple
and generalizable modification that enables any existing LLM to perform Group
Think on a local GPU. We also present an evaluation strategy to benchmark
reasoning latency and empirically demonstrate latency improvements using
open-source LLMs that were not explicitly trained for Group Think. We hope this
work paves the way for future LLMs to exhibit more sophisticated and more
efficient collaborative behavior for higher quality generation.