Delivering AI-generated content (AIGC) services fundamentally relies on the reasoning capabilities of generative AI (GenAI) models. Chain-of-Thought (CoT) enhances such reasoning by guiding models through intermediate steps, while Tree-of-Thoughts (ToT) further extends CoT by exploring multiple candidate reasoning paths simultaneously, thereby greatly improving AIGC service quality. However, generating diverse reasoning paths requires separate calls to computationally intensive GenAI models, posing significant challenges for resource constrained user devices. In this paper, we investigate mobile edge computing-enabled AIGC service provisioning with ToT prompting. Specifically, using creative writing AIGC tasks as a case study, we first characterize the number of output tokens as a measure of computational resources in GenAI models and establish its relationship with generation delay and quality through experiments with Qwen 2.5-7B-Instruct. Afterward, we introduce a directed acyclic graph (DAG) model to accurately characterize the reasoning process of ToT prompting, where each vertex represents a thought and each directed edge denotes a transition between consecutive thoughts. We then formulate a DAG-based thought assignment problem aimed at minimizing generation delay subject to a user-adjustable quality constraint. To address this problem, we propose a diffusion-based soft actor-critic (DSAC) algorithm that innovatively integrates diffusion models to determine optimal thought assignment decisions. Through extensive simulations, we demonstrate that the proposed DSAC achieves total generation delay reductions of up to 8.32% over PPO, 11.57% over SAC, and 36.09% over DDQN across various simulation settings, while reducing latency by over 80% compared to the fully local generation baseline even under stringent quality requirements.

翻译：交付AI生成内容（AIGC）服务从根本上依赖于生成式AI（GenAI）模型的推理能力。思维链（CoT）通过引导模型经历中间步骤来增强此类推理，而思维树（ToT）则进一步扩展了CoT，通过同时探索多个候选推理路径，从而大幅提升AIGC服务质量。然而，生成多样化的推理路径需要对计算密集型的GenAI模型进行多次独立调用，这对资源受限的用户设备构成了重大挑战。本文研究了采用ToT提示的移动边缘计算赋能AIGC服务供给问题。具体而言，以创意写作AIGC任务为案例，我们首先将输出令牌数量表征为GenAI模型计算资源的度量，并通过使用Qwen 2.5-7B-Instruct进行的实验，建立其与生成延迟及质量之间的关系。随后，我们引入有向无环图（DAG）模型来精确表征ToT提示的推理过程，其中每个顶点代表一个思维，每条有向边代表连续思维之间的转换。接着，我们提出一个基于DAG的思维分配问题，目标是在满足用户可调质量约束的前提下最小化生成延迟。为解决该问题，我们提出了一种基于扩散的软演员-评论家（DSAC）算法，该算法创新性地融合了扩散模型以确定最优的思维分配决策。通过大量仿真，我们证明所提出的DSAC在各种仿真设置下，相比PPO、SAC和DDQN，总生成延迟分别最多降低8.32%、11.57%和36.09%，同时即使在严格的质量要求下，相较于完全本地生成的基线方案，延迟也降低了超过80%。