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[2509.25260] Internal Planning in Language Models: Characterizing Horizon and Branch Awareness
Summary
This article explores how decoder-only language models engage in internal planning, focusing on their ability to organize computations for coherent long-range generation and the implications for model interpretability.
Why It Matters
Understanding internal planning in language models is crucial for enhancing their reliability and interpretability. This research provides insights into how models manage computations over extended contexts, which can inform better model design and application in various AI tasks.
Key Takeaways
- Language models exhibit task-dependent planning horizons.
- Models retain information about alternative valid continuations.
- Recent computations are prioritized in predictions, but earlier layers still provide valuable information.
- The study introduces a new pipeline for analyzing internal model dynamics.
- Findings have implications for improving model interpretability and design.
Computer Science > Artificial Intelligence arXiv:2509.25260 (cs) [Submitted on 28 Sep 2025 (v1), last revised 15 Feb 2026 (this version, v2)] Title:Internal Planning in Language Models: Characterizing Horizon and Branch Awareness Authors:Muhammed Ustaomeroglu, Baris Askin, Gauri Joshi, Carlee Joe-Wong, Guannan Qu View a PDF of the paper titled Internal Planning in Language Models: Characterizing Horizon and Branch Awareness, by Muhammed Ustaomeroglu and 4 other authors View PDF HTML (experimental) Abstract:The extent to which decoder-only language models (LMs) engage in planning, that is, organizing intermediate computations to support coherent long-range generation, remains an important question, with implications for interpretability, reliability, and principled model design. Planning involves structuring computations over long horizons, and considering multiple possible continuations, but how far transformer-based LMs exhibit them without external scaffolds, e.g., chain-of-thought prompting, is unclear. We address these questions by analyzing the hidden states at the core of transformer computations, which capture intermediate results and act as carriers of information. Since these hidden representations are redundant and encumbered with fine-grained details, we develop a pipeline based on vector-quantized variational autoencoders that compresses them into compact summary codes. These codes enable measuring mutual information and analyzing the computational structure of...
