DeReAct: Decomposed Reasoning and Acting for Reliable AI Agents

A new arXiv paper (2610.02351v1) introduces DeReAct, a modular agent architecture that externalizes action validation and completion control into two gating policies: a Critic that validates proposed actions before execution and a Context Manager that reconstructs an environment-supported State and certifies task completion. Across GAIA and SWE-bench Verified, DeReAct improved Pass@1 by 6.5–7.0 points for Qwen3-Coder-480B and 4.2–5.2 points for Claude Sonnet 4.5, with gains diminishing as Brain capability increases; with Claude Opus 4.5, Pass@1 remained comparable to ReAct while producing more evidence-complete and constraint-satisfying trajectories.

arXiv:2610.02351v1 Announce Type: new Abstract: ReAct-based agents typically rely on a single LLM policy to propose actions, interact with the environment, and decide when a task is complete. This coupling makes action authorization and completion control difficult to enforce independently, allowing errors to propagate and unsupported completion claims to terminate execution. We introduce DeReAct, a modular agent architecture that externalizes two gating policies: a Critic that validates proposed actions before execution, and a Context Manager that reconstructs an environment-supported \textsc{State} and certifies task completion. Across GAIA and SWE-bench Verified, DeReAct improves Pass@1 most for weaker Brain models, with gains of 6.5--7.0 points for Qwen3-Coder-480B and 4.2--5.2 points for Claude Sonnet~4.5; gains diminish as Brain capability increases. Trajectory and ablation analyses show that external gating is effective when targeted failures are sufficiently prevalent and the gating policy is itself sufficient. With Claude Opus~4.5, Pass@1 remains comparable to ReAct, while DeReAct produces more evidence-complete and constraint-satisfying trajectories, indicating that completion control can trade earlier termination for stronger grounding. Overall, DeReAct improves weaker agents while retaining grounding benefits as models strengthen.