ReflAct: World-Grounded Decision Making in LLM Agents via Goal-State
  Reflection
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.
68305a53b687672ae37437df
Jeonghye Kim
claimed_verified
2025-05-26T08:14:07.810Z
63e48f6d9db5da2dc1f6288e
https://cdn-avatars.huggingface.co/v1/production/uploads/1676046878664-63e48f6d9db5da2dc1f6288e.png
JeonghyeKim
user
beanie00
68305a53b687672ae37437e0
Sojeong Rhee
68305a53b687672ae37437e1
Minbeom Kim
68305a53b687672ae37437e2
Dohyung Kim
68305a53b687672ae37437e3
Sangmook Lee
68305a53b687672ae37437e4
Youngchul Sung
68305a53b687672ae37437e5
Kyomin Jung
ReflAct: World-Grounded Decision Making in LLM Agents via Goal-State
  Reflection
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.
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.
ReAct ReflAct reasoning backbone thought-action interweaving goal alignment internal beliefs state reflection strategic reliability ALFWorld Reflexion WKM