The Shadow Price of Reasoning: Economic Perspective on Optimal Budget Allocation for LLMs
The article discusses a new approach to budget allocation for Large Language Models (LLMs) based on economic principles. It introduces a method called Constrained Latent-utility Equilibrium Allocation for Reasoning (CLEAR), which optimizes resource distribution for improved performance. The findings indicate that CLEAR can significantly enhance accuracy while managing computational costs effectively.
- ▪Inference-time scaling is crucial for enhancing LLM performance but is limited by computational budgets.
- ▪CLEAR reallocates resources from insolvent queries to solvable ones, improving overall efficiency.
- ▪In resource-scarce environments, CLEAR can achieve up to a 3x improvement in global accuracy compared to uniform allocation.
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| Original publisher | arXiv cs.AI |
| Canonical URL | https://arxiv.org/abs/2606.03092 |
| Publication time | Wed, 03 Jun 2026 00:00:00 -0400 |
| Retrieval time | 2026-06-03T04:11:55.408Z |
| Last seen | 2026-06-03T04:11:55.408Z |
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| Commercial reuse | May the content be reused commercially? | Not permitted |
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Computer Science > Artificial Intelligence arXiv:2606.03092 (cs) [Submitted on 2 Jun 2026] Title:The Shadow Price of Reasoning: Economic Perspective on Optimal Budget Allocation for LLMs Authors:Xu Wan, Speed Zhu, Jianwei Cai, Guang Chen, XiMing Huang, Wiggin Zhou, Mingyang Sun View a PDF of the paper titled The Shadow Price of Reasoning: Economic Perspective on Optimal Budget Allocation for LLMs, by Xu Wan and 6 other authors View PDF HTML (experimental) Abstract:Inference-time scaling has emerged as a critical avenue for enhancing Large Language Models' performance, yet real-world deployment is constrained by strict computational budgets. In this work, we formulate inference budget allocation as a global constrained optimization problem governed by economic principles.
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