Large Language Models Can Take False First Steps at Inference-time Planning

• URL: http://arxiv.org/abs/2602.02991v1
• Date: Tue, 03 Feb 2026 01:54:55 GMT
• Title: Large Language Models Can Take False First Steps at Inference-time Planning
• Authors: Haijiang Yan, Jian-Qiao Zhu, Adam Sanborn,
• Abstract summary: Large language models (LLMs) have been shown to acquire sequence-level planning abilities during training.<n>Planing behavior exhibited at inference time often appears short-sighted and inconsistent with these capabilities.<n>We propose a Bayesian account for this gap by grounding planning behavior in the evolving generative context.
• Score: 2.6100783621884625
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large language models (LLMs) have been shown to acquire sequence-level planning abilities during training, yet their planning behavior exhibited at inference time often appears short-sighted and inconsistent with these capabilities. We propose a Bayesian account for this gap by grounding planning behavior in the evolving generative context: given the subtle differences between natural language and the language internalized by LLMs, accumulated self-generated context drives a planning-shift during inference and thereby creates the appearance of compromised planning behavior. We further validate the proposed model through two controlled experiments: a random-generation task demonstrating constrained planning under human prompts and increasing planning strength as self-generated context accumulates, and a Gaussian-sampling task showing reduced initial bias when conditioning on self-generated sequences. These findings provide a theoretical explanation along with empirical evidence for characterizing how LLMs plan ahead during inference.

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