用大模型整合推理与执行，让自然语言指令自动完成服务组合。
Initial Steps in Integrating Large Reasoning and Action Models for Service Composition
- 构建LRM与LAM协同架构，融合语义推理与动态执行能力。
- 实现从自然语言意图到自动化工作流的端到端转化，提升系统适应性。
- 适合智能系统开发、自动化运维等需高阶编排的场景。
服务组合仍是构建自适应智能软件系统的核心挑战，常受限于推理能力不足或执行机制脆弱。本文探讨由大语言模型推动的两种新兴范式——大推理模型（LRMs）与大动作模型（LAMs）的整合。我们认为，LRMs擅长处理语义推理与生态复杂性，而LAMs在动态动作执行与系统互操作性方面表现优异。但二者存在互补局限：LRMs缺乏具体动作执行能力，而LAMs常难以进行深度推理。为此，我们提出一种集成的LRM-LAM架构框架，有望推进自动化服务组合的发展。该系统可在理解服务需求与约束的同时动态执行工作流，弥合意图与执行之间的鸿沟。这种整合具有将服务组合转变为完全自动化、用户友好的过程的潜力，由高层次自然语言意图驱动。
原文摘要 · Abstract (English)
Service composition remains a central challenge in building adaptive and intelligent software systems, often constrained by limited reasoning capabilities or brittle execution mechanisms. This paper explores the integration of two emerging paradigms enabled by large language models: Large Reasoning Models (LRMs) and Large Action Models (LAMs). We argue that LRMs address the challenges of semantic reasoning and ecosystem complexity while LAMs excel in dynamic action execution and system interoperability. However, each paradigm has complementary limitations - LRMs lack grounded action capabilities, and LAMs often struggle with deep reasoning. We propose an integrated LRM-LAM architectural framework as a promising direction for advancing automated service composition. Such a system can reason about service requirements and constraints while dynamically executing workflows, thus bridging the gap between intention and execution. This integration has the potential to transform service composition into a fully automated, user-friendly process driven by high-level natural language intent.
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