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[2602.19281] Limited Reasoning Space: The cage of long-horizon reasoning in LLMs
Summary
This article discusses the 'Limited Reasoning Space' hypothesis in large language models (LLMs), proposing that over-planning can impair reasoning capabilities during complex task execution. It introduces Halo, a new framework designed to optimize reasoning through dynamic pla...
Why It Matters
Understanding the limitations of LLMs in long-horizon reasoning is crucial for improving their performance in complex tasks. This research highlights the importance of optimal compute budgets and introduces innovative strategies to enhance LLM capabilities, which could influence future AI development.
Key Takeaways
- Increased compute budgets can lead to reasoning failures in LLMs.
- The 'Limited Reasoning Space' hypothesis suggests static planning methods are inadequate.
- Halo, a new framework, offers a dynamic approach to LLM planning.
- Optimal compute budgets are essential for effective reasoning.
- Experimental results show Halo outperforms traditional static methods.
Computer Science > Artificial Intelligence arXiv:2602.19281 (cs) [Submitted on 22 Feb 2026] Title:Limited Reasoning Space: The cage of long-horizon reasoning in LLMs Authors:Zhenyu Li, Guanlin Wu, Cheems Wang, Yongqiang Zhao View a PDF of the paper titled Limited Reasoning Space: The cage of long-horizon reasoning in LLMs, by Zhenyu Li and 3 other authors View PDF HTML (experimental) Abstract:The test-time compute strategy, such as Chain-of-Thought (CoT), has significantly enhanced the ability of large language models to solve complex tasks like logical reasoning. However, empirical studies indicate that simply increasing the compute budget can sometimes lead to a collapse in test-time performance when employing typical task decomposition strategies such as CoT. This work hypothesizes that reasoning failures with larger compute budgets stem from static planning methods, which hardly perceive the intrinsic boundaries of LLM reasoning. We term it as the Limited Reasoning Space hypothesis and perform theoretical analysis through the lens of a non-autonomous stochastic dynamical system. This insight suggests that there is an optimal range for compute budgets; over-planning can lead to redundant feedback and may even impair reasoning capabilities. To exploit the compute-scaling benefits and suppress over-planning, this work proposes Halo, a model predictive control framework for LLM planning. Halo is designed for long-horizon tasks with reason-based planning and crafts an entro...
