arXiv:2506.09171v2 Announce Type: replace-cross Abstract: Large Language Models (LLMs) are increasingly capable, but LLM agents still struggle to plan effectively in interactive, partially observable, long-horizon environments when search is unguided or recent history is insufficient. We introduce LWM-Planner, a fact-augmented lookahead planning framework that improves agent behavior purely through in-context learning. After each episode, the agent extracts task-critical atomic facts from its trajectories, validates candidates with a lightweight predictive-consistency filter (and optionally co
The rapid advancement of LLMs necessitates more sophisticated planning frameworks to enable their reliable deployment in complex, real-world environments.
Improved planning capabilities for LLM agents will significantly accelerate their utility and deployment, moving them closer to autonomous functionality across various domains.
LLM agents can now leverage fact-augmented lookahead planning, reducing reliance on unguided search and recent history, thus enabling them to operate effectively in more challenging environments.
- · AI Agent development platforms
- · Companies deploying autonomous systems
- · Researchers in AI planning and control
- · SaaS providers integrated with AI agents
- · Manual process industries
- · Traditional software development without agentic integration
Increased reliability and complexity of tasks executable by LLM agents in interactive environments.
Acceleration of 'super-worker' AI agents automating multi-step, partially observable workflows.
Systemic restructuring of white-collar work roles as AI agents handle increasingly complex planning and execution.
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Read at arXiv cs.CL