Improving LLM Agent Planning with In-Context Learning via Atomic Fact Augmentation and Lookahead Search (2506.09171v1)

Abstract: LLMs are increasingly capable but often require significant guidance or extensive interaction history to perform effectively in complex, interactive environments. Existing methods may struggle with adapting to new information or efficiently utilizing past experiences for multi-step reasoning without fine-tuning. We introduce a novel LLM agent framework that enhances planning capabilities through in-context learning, facilitated by atomic fact augmentation and a recursive lookahead search. Our agent learns to extract task-critical ``atomic facts'' from its interaction trajectories. These facts dynamically augment the prompts provided to LLM-based components responsible for action proposal, latent world model simulation, and state-value estimation. Planning is performed via a depth-limited lookahead search, where the LLM simulates potential trajectories and evaluates their outcomes, guided by the accumulated facts and interaction history. This approach allows the agent to improve its understanding and decision-making online, leveraging its experience to refine its behavior without weight updates. We provide a theoretical motivation linking performance to the quality of fact-based abstraction and LLM simulation accuracy. Empirically, our agent demonstrates improved performance and adaptability on challenging interactive tasks, achieving more optimal behavior as it accumulates experience, showcased in tasks such as TextFrozenLake and ALFWorld.

Improving LLM Agent Planning with In-Context Learning via Atomic Fact Augmentation and Lookahead Search

The paper presents a novel framework for enhancing the planning capabilities of LLM-based agents through the integration of in-context learning, atomic fact augmentation, and recursive lookahead search. The research addresses the inherent challenge faced by LLM agents in adapting to new information and efficiently leveraging past experiences in complex environments without extensive fine-tuning.

Summary of Key Contributions

1. Atomic Fact Augmentation: The introduction of atomic facts represents a pivotal advancement in the agent's ability to distill and utilize critical task-specific knowledge. These atomic facts are succinct verbal articulations extracted from an agent's episodic trajectories. Importantly, they capture minimal yet significant units of knowledge—such as environmental properties or state dynamics—that the agent uses to enrich its understanding and steer its decision-making process. These enhancements allow the LLM agent to react and adapt dynamically, ensuring that the environment-specific contextual nuances are effectively grounded within the agent's reasoning framework.
2. Recursive Lookahead Planning: This novel planning approach employs a depth-limited recursive lookahead search mechanism, where the agent simulates potential trajectories and evaluates prospective outcomes. The process is contingent upon accumulated atomic facts and the interaction history. Three critical components drive the lookahead planning: the action proposer, latent world model simulator, and state-value estimator—all receiving input enriched with atomic facts. This setup leverages the inherent simulation capabilities of LLMs more deeply, enabling improved trajectory simulations and more precise state evaluation.
3. Empirical and Theoretical Motivations: The paper offers a theoretical framework correlating the agent's performance with the abstraction quality of fact-based knowledge and the accuracy of LLM simulation, aligning with established principles in the field of reinforcement learning. On empirical grounds, the proposed agent demonstrates superior adaptability and planning competence on interactive tasks like TextFrozenLake and ALFWorld, achieving optimal behavior as it accumulates experiential learning.

Numerical Results and Claims

The empirical findings within simulated environments are compelling, showcasing improved task performance metrics as the agent accrues more interaction experience. Notably, in environments such as ALFWorld, the LLM agent, augmented with atomic facts, consistently achieved high rewards, demonstrating its efficacy in adapting and refining its policies purely through in-context learning mechanisms. The reported numerical improvements across challenging benchmarks substantiate the claims of the framework enabling more optimal behavior as experiential knowledge is synthesized.

Implications and Future Directions

The practical implications of this research are significant, particularly in enhancing the robustness and adaptability of autonomous agents in real-world applications. By exploiting in-context learning and symbolic knowledge augmentation, agents can achieve efficient planning and problem-solving capabilities without requiring comprehensive model retraining on new task settings.

Furthermore, the paper posits exciting future directions, suggesting exploration into advanced fact extraction techniques such as causal discovery for identifying influential facts. There is also a potential for dynamically adjusting search parameters based on task complexity or environmental uncertainty, pushing the boundaries of in-context learning applicability in AI.

Conclusion

This research provides crucial insights into advancing the capabilities of LLM-based agents for sequential decision-making in complex environments. By amalgamating atomic fact augmentation and lookahead planning, the framework moves a step closer to realizing truly adaptive AI agents capable of sophisticated planning through efficient experiential learning.