多智能体框架让大模型推理图数据更准更快，节省95%以上计算量。
Scaling Graph Chain-of-Thought Reasoning: A Multi-Agent Framework with Efficient LLM Serving
- 拆分任务为分类、推理等专用智能体，减少重复计算
- 准确率最高提升38%，令牌消耗降低95.7%
- 适合需要高效复杂推理的工业级应用
图链式思维（Graph-CoT）使大语言模型能在图结构知识上进行逐步推理，但现有方法因单智能体提示过长、上下文重复编码和低效服务执行，导致准确率低、令牌使用多、延迟高、吞吐量低。我们提出GLM，首个与优化大模型服务架构协同设计的多智能体Graph-CoT系统。GLM将推理分解为分类、推理、动作生成和图检索等专用智能体，支持分支与选择性上下文共享，减少提示长度和推理轮次，同时保持推理质量，从而提升准确率并降低整体令牌消耗。为实现推理扩展，引入图感知的大模型推理机制，包含图专用键值缓存管理、基于优先级的淘汰策略和流水线执行，显著提升服务效率。实验表明，GLM相比最先进基线，答案准确率最高提升38%，令牌成本降低95.7%，推理延迟下降90.3%，吞吐量最高达15.1倍，支持复杂现实推理的规模化应用。
原文摘要 · Abstract (English)
Graph Chain-of-Thought (Graph-CoT) enables large language models (LLMs) to perform step-by-step reasoning over graph-structured knowledge, but existing pipelines suffer from low accuracy, excessive token usage, high latency, and low throughput due to single-agent monolithic prompts, repeated context re-encoding, and inefficient serving execution. We present GLM, the first multi-agent Graph-CoT system co-designed with an optimized LLM serving architecture. GLM decomposes reasoning into specialized agents for classification, reasoning, action generation, and graph retrieval, enabling branching and selective context sharing to reduce prompt length and reasoning iterations while preserving reasoning quality, thereby improving accuracy and reducing overall token consumption. To scale inference, we introduce a Graph-CoT-aware LLM inference mechanism with graph-specific KV-cache management, priority-based eviction, and pipelined execution to improve serving efficiency. Experiments demonstrate that GLM improves answer accuracy by up to 38%, reduces token cost by up to 95.7%, lowers inference latency by 90.3%, and achieves up to 15.1x higher throughput compared to state-of-the-art Graph-CoT baselines, enabling efficient adoption for complex real-world reasoning at scale.
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