Reward-Driven LLM Agent Workflows: Synthesizing POMDP Routing and Self-Correction for Autonomous Decision-Making
Amez Amanj Ali, Kuo-Kun Tseng
Why It Matters
What makes this one worth your time
This research could enhance the autonomy and efficiency of AI agents in complex environments, which is crucial for developing more capable and reliable AI systems.
A new architecture for LLM agents combines POMDP routing and self-correction to improve decision-making.
Summary
The paper proposes a novel architecture for large language model agents by integrating a POMDP routing mechanism with a self-correcting reward model to enhance decision-making in dynamic environments. It demonstrates improvements in task success and efficiency over existing models through experiments in simulated environments.
Key contributions
- Introduction of a POMDP routing mechanism for LLM agents.
- Development of a self-correcting reward model to evaluate decision trajectories.
- Demonstrated improvement in task success rate and trajectory efficiency in simulated environments.
Notable insights
- The integration of a self-correcting reward model with POMDP routing is a novel approach to improving decision trajectories.
- The use of graph-based memory and reinforcement learning principles helps maintain long-term structural memory and adapt reasoning pathways.
Possible limitations
- Not stated in the abstract
Abstract
arXiv:2607.17038v1 Announce Type: new Abstract: This paper addresses key technical challenges in current large language model (LLM) agent applications, including long-horizon planning, sparse reward attribution, and dynamic environmental interaction, by designing and optimizing an intelligent agent workflow. The proposed architecture is based on the synthesis of core AI paradigms: Visual, Language, Generative, Graph, Multimodal, Reinforcement, and Agent Intelligence. Unlike conventional baseline models that rely on static prompting and lack robust perception-action loops, our approach introduces a Partially Observable Markov Decision Process (POMDP) routing mechanism. This mechanism is augmented with an internal, self-correcting reward model that evaluates decision trajectories before execution. By integrating multimodal inputs and advanced reinforcement learning principles (such as proximal policy optimization and value function approximation), the agent maintains long-term structural memory and dynamically adapts its reasoning pathways to mitigate error accumulation. Empirical experiments on the ALFWorld embodied simulation environment and the WebShop online navigation benchmark demonstrate a 24.5% absolute improvement in task success rate and trajectory efficiency over mainstream baselines like the standard ReAct framework. Comprehensive ablation studies confirm the significant contribution of the reward-driven critique module in suppressing hallucination rates. This research bridges theoretical foundations of reinforcement learning and graph-based memory with autonomous agent workflows. Ultimately, the resulting architecture offers a practical, scalable reference framework for developing artificial intelligence technologies in complex, multi-step autonomous systems. Code is available at https://github.com/01Amez/RLAW_Implementation.