ReflAct: World-Grounded Decision Making in LLM Agents via Goal-State
  Reflection
Authors: name: Jeonghye Kim, name: Sojeong Rhee, name: Minbeom Kim, name: Dohyung Kim, name: Sangmook Lee, name: Youngchul Sung, name: Kyomin Jung

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.