arXiv:2606.03685v1 Announce Type: new Abstract: Supervised fine-tuning (SFT) improves end-to-end classical planning in large language models (LLMs), but do these models also learn to represent and reason about the planning problems they are solving? Due to the relative complexity of classical planning problems and the challenge that end-to-end plan generation poses for LLMs, it has been difficult to explore this question. In our work, we devise and perform a series of interpretability experiments that holistically interrogate world model recovery by examining both internal representations and
This research provides a deeper understanding of how LLMs interpret and plan, which is crucial as their capabilities rapidly expand.
Understanding world model recovery in LLMs is essential for developing more reliable, controllable, and truly intelligent AI agents, especially for complex tasks.
This research shifts our understanding from merely observing LLM planning performance to interrogating the underlying cognitive mechanisms, enabling better design and interpretability.
- · AI researchers
- · LLM developers
- · Robotics
- · AI explainability platforms
- · Black-box AI approaches
- · Inefficient AI planning models
Improved interpretability frameworks for Large Language Models.
Accelerated development of more robust and generalizable AI agents capable of complex reasoning.
Enhanced trust and adoption of AI in critical planning and decision-making roles in various industries.
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