为大模型智能体设计提供形式化框架，提升可分析性与迭代效率。
Toward Formalizing LLM-Based Agent Designs through Structural Context Modeling and Semantic Dynamics Analysis
- 提出结构化上下文模型，从上下文结构角度形式化智能体设计。
- 在猴子香蕉问题中实现成功率提升32个百分点，最复杂场景效果显著。
- 适合研究智能体机制、追求系统化开发的科研与工程人员。
当前大语言模型（LLM）智能体研究呈现碎片化：概念框架与方法论原则常与底层实现细节混杂，导致读者和作者难以把握核心。我们指出，这一现象主要源于缺乏可分析、自洽的形式化模型，无法实现智能体的无依赖表征与比较。为此，我们提出 exttt{Structural Context Model}，一种从上下文结构视角分析和比较LLM智能体的形式化模型。在此基础上，构建两个互补组件：(1) 声明式实现框架；(2) 可持续的智能体工程流程 exttt{Semantic Dynamics Analysis}。该流程提供对智能体机制的原理性洞察，支持快速系统化的设计迭代。我们在动态变体的猴子香蕉问题上验证了该框架的有效性，基于此方法设计的智能体在最复杂场景中成功率提升达32个百分点。
原文摘要 · Abstract (English)
Current research on large language model (LLM) agents is fragmented: discussions of conceptual frameworks and methodological principles are frequently intertwined with low-level implementation details, causing both readers and authors to lose track amid a proliferation of superficially distinct concepts. We argue that this fragmentation largely stems from the absence of an analyzable, self-consistent formal model that enables implementation-independent characterization and comparison of LLM agents. To address this gap, we propose the \texttt{Structural Context Model}, a formal model for analyzing and comparing LLM agents from the perspective of context structure. Building upon this foundation, we introduce two complementary components that together span the full lifecycle of LLM agent research and development: (1) a declarative implementation framework; and (2) a sustainable agent engineering workflow, \texttt{Semantic Dynamics Analysis}. The proposed workflow provides principled insights into agent mechanisms and supports rapid, systematic design iteration. We demonstrate the effectiveness of the complete framework on dynamic variants of the monkey-banana problem, where agents engineered using our approach achieve up to a 32 percentage points improvement in success rate on the most challenging setting.
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