Where's the Plan? Locating Latent Planning in Language Models with Lightweight Mechanistic Interventions
arXiv:2605.07984v2 Announce Type: replace-cross Abstract: We study planning site formation in language models -- where internal representations of structurally-constrained future tokens form during the forward pass, and whether they causally drive generation. Using rhyming-couplet completion as a clean test of forward-looking constraint, we apply two lightweight methods (linear probing and activation patching) across Qwen3, Gemma-3, and Llama-3 at more than ten scales. Probing shows that future-rhyme information is linearly decodable at the line boundary, with signal that strengthens with scal
Ongoing research into LLM interpretability and mechanistic understanding is rapidly advancing, with new techniques allowing deeper insights into internal model workings.
Understanding how language models plan internally is crucial for developing more reliable, controllable, and advanced AI systems, moving beyond black-box operations.
This research provides a methodology to identify where and how planning capabilities emerge in LLMs, shifting the understanding from pure capability to mechanistic insight.
- · AI researchers
- · LLM developers
- · AI safety community
- · Companies using LLMs for complex tasks
- · Developers relying on black-box LLM operation
Improved debugging and fine-tuning techniques for LLMs based on their internal planning mechanisms.
Development of new LLM architectures explicitly designed to enhance or control planning capabilities.
More robust and less 'hallucinatory' AI agents capable of long-term, multi-step reasoning in real-world applications.
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Read at arXiv cs.AI