title: ReflAct: World-Grounded Decision Making in LLM Agents via Goal-State
    Reflection
summary: Recent advances in LLM agents have largely built on reasoning backbones like
  ReAct, which interleave thought and action in complex environments. However,
  ReAct often produces ungrounded or incoherent reasoning steps, leading to
  misalignment between the agent's actual state and goal. Our analysis finds that
  this stems from ReAct's inability to maintain consistent internal beliefs and
  goal alignment, causing compounding errors and hallucinations. To address this,
  we introduce ReflAct, a novel backbone that shifts reasoning from merely
  planning next actions to continuously reflecting on the agent's state relative
  to its goal. By explicitly grounding decisions in states and enforcing ongoing
  goal alignment, ReflAct dramatically improves strategic reliability. This
  design delivers substantial empirical gains: ReflAct surpasses ReAct by 27.7%
  on average, achieving a 93.3% success rate in ALFWorld. Notably, ReflAct even
  outperforms ReAct with added enhancement modules (e.g., Reflexion, WKM),
  showing that strengthening the core reasoning backbone is key to reliable agent
  performance.
paper_authors._id: 68305a53b687672ae37437df
paper_authors.name: Jeonghye Kim
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-05-26T08:14:07.810Z
paper_authors._id: 63e48f6d9db5da2dc1f6288e
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/1676046878664-63e48f6d9db5da2dc1f6288e.png
paper_authors.fullname: JeonghyeKim
paper_authors.type: user
paper_authors.user: beanie00
paper_authors._id: 68305a53b687672ae37437e0
paper_authors.name: Sojeong Rhee
paper_authors._id: 68305a53b687672ae37437e1
paper_authors.name: Minbeom Kim
paper_authors._id: 68305a53b687672ae37437e2
paper_authors.name: Dohyung Kim
paper_authors._id: 68305a53b687672ae37437e3
paper_authors.name: Sangmook Lee
paper_authors._id: 68305a53b687672ae37437e4
paper_authors.name: Youngchul Sung
paper_authors._id: 68305a53b687672ae37437e5
paper_authors.name: Kyomin Jung
paper_title: ReflAct: World-Grounded Decision Making in LLM Agents via Goal-State
    Reflection
paper_summary: Recent advances in LLM agents have largely built on reasoning backbones like
  ReAct, which interleave thought and action in complex environments. However,
  ReAct often produces ungrounded or incoherent reasoning steps, leading to
  misalignment between the agent's actual state and goal. Our analysis finds that
  this stems from ReAct's inability to maintain consistent internal beliefs and
  goal alignment, causing compounding errors and hallucinations. To address this,
  we introduce ReflAct, a novel backbone that shifts reasoning from merely
  planning next actions to continuously reflecting on the agent's state relative
  to its goal. By explicitly grounding decisions in states and enforcing ongoing
  goal alignment, ReflAct dramatically improves strategic reliability. This
  design delivers substantial empirical gains: ReflAct surpasses ReAct by 27.7%
  on average, achieving a 93.3% success rate in ALFWorld. Notably, ReflAct even
  outperforms ReAct with added enhancement modules (e.g., Reflexion, WKM),
  showing that strengthening the core reasoning backbone is key to reliable agent
  performance.
paper_ai_summary: ReflAct, a new reasoning backbone for LLM agents, improves goal alignment and reduces hallucinations by continuously reflecting on the agent's state, surpassing ReAct and other enhanced variants.
paper_ai_keywords: ReAct ReflAct reasoning backbone thought-action interweaving goal alignment internal beliefs state reflection strategic reliability ALFWorld Reflexion WKM