Language Model Planning from an Information Theoretic Perspective

2025-09-27

AI Summary

This work investigates whether decoder-only language models possess implicit “planning” capabilities—i.e., internally organizing intermediate computations to support long-range semantic coherence in text generation. We propose a VQ-VAE-based latent state compression framework, enabling the first information-theoretic quantification of internal planning behavior: abstract codebooks extract redundant hidden states, and mutual information analysis systematically characterizes information retention, preservation of multiple continuation candidates, and cross-layer computational dependencies. Experiments reveal three key findings: (1) planning horizon is task-dependent; (2) models implicitly retain semantically correct but currently unused continuations; and (3) while prediction relies predominantly on recent layers, early layers still encode significant long-range semantic information. Our approach establishes a measurable, principled paradigm for analyzing the inferential architecture of large language models.

Technology Category

• Natural Language Processing: (Large) Language Models
• Planning, Routing, and Scheduling: Planning with Language Models
• Machine Learning: Large Multimodal Models (LMMs)

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Large language models for search
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation

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 open and important question, with implications for interpretability, reliability, and principled model design. Planning involves structuring computations over long horizons, considering multiple possible continuations, and selectively reusing past information, but how effectively transformer-based LMs realize these capabilities is still 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 often 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, allowing systematic analysis of the computational structure underlying model behavior. Using this framework, we study planning in LMs across synthetic grammar, path-finding tasks, and natural language datasets, focusing on three key aspects: (i) the planning horizon of pre-output computations, (ii) the extent to which the model considers alternative valid continuations, and (iii) the reliance of new predictions on earlier computations. By answering these questions, we advance the understanding of how planning is realized in LMs and contribute a general-purpose pipeline for probing the internal dynamics of LMs and deep learning systems. Our results reveal that the effective planning horizon is task-dependent, that models implicitly preserve information about unused correct continuations, and that predictions draw most on recent computations, though earlier blocks remain informative.

Problem

• Analyzing planning capabilities in decoder-only language models for long-range generation
• Developing a pipeline to compress hidden states and measure mutual information
• Investigating planning horizon, alternative continuations, and computational reliance in LMs

Innovation

• Compress hidden states using vector-quantized variational autoencoders
• Measure mutual information via compact summary codes
• Analyze planning horizon and alternative continuations systematically