title: Group Think: Multiple Concurrent Reasoning Agents Collaborating at Token
    Level Granularity
summary: 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.
paper_authors._id: 682ad96cdc6d7453624831b9
paper_authors.name: Chan-Jan Hsu
paper_authors.status: claimed_verified
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paper_authors.avatarUrl: /avatars/930ac20daf640ca31fab713bf00c3268.svg
paper_authors.fullname: 許湛然
paper_authors.type: user
paper_authors.user: Splend1dchan
paper_authors._id: 682ad96cdc6d7453624831ba
paper_authors.name: Davide Buffelli
paper_authors._id: 682ad96cdc6d7453624831bb
paper_authors.name: Jamie McGowan
paper_authors.status: admin_assigned
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paper_authors._id: 633ee7834be90e06da1ea244
paper_authors.avatarUrl: /avatars/c981cb4250e9646fe8bf11045cfa715d.svg
paper_authors.fullname: Jamie McGowan
paper_authors.type: user
paper_authors.user: jamie-mcg
paper_authors._id: 682ad96cdc6d7453624831bc
paper_authors.name: Feng-Ting Liao
paper_authors.status: admin_assigned
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paper_authors._id: 643fb7332397d8eef5b844cd
paper_authors.avatarUrl: /avatars/e403a19fc13e478d5929c67028230b0e.svg
paper_authors.fullname: Feng-Ting Liao
paper_authors.type: user
paper_authors.user: FengTing
paper_authors._id: 682ad96cdc6d7453624831bd
paper_authors.name: Yi-Chang Chen
paper_authors.status: claimed_verified
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paper_authors.fullname: Yi-Chang (YC) Chen
paper_authors.type: user
paper_authors.user: YC-Chen
paper_authors._id: 682ad96cdc6d7453624831be
paper_authors.name: Sattar Vakili
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-05-19T14:21:12.208Z
paper_authors._id: 64c242ce129617dbaba8d439
paper_authors.avatarUrl: /avatars/947cf4330ed1ce2cbdd5252cd91c9024.svg
paper_authors.fullname: Sattar Vakili
paper_authors.type: user
paper_authors.user: sattar-vakili
paper_authors._id: 682ad96cdc6d7453624831bf
paper_authors.name: Da-shan Shiu
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-05-19T14:21:22.352Z
paper_authors._id: 6811b1294119e4ecc92fc93b
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/no-auth/srmY2yyzOg9KRDSLYXJKf.png
paper_authors.fullname: Dashan Shiu
paper_authors.type: user
paper_authors.user: dsshiu
paper_title: Group Think: Multiple Concurrent Reasoning Agents Collaborating at Token
    Level Granularity
paper_summary: 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.
paper_ai_summary: A concurrent reasoning paradigm using a single LLM, called Group Think, enhances reasoning quality and reduces latency by allowing multiple reasoning threads to dynamically collaborate at the token level.
paper_ai_keywords: large language models (LLMs) reasoning through self-generated chains of thought reasoning agents concurrent-reasoning paradigm token-level collaboration