Toward Formalizing LLM-Based Agent Designs through Structural Context Modeling and Semantic Dynamics Analysis

2026-02-09

AI Summary

Current research on large language model (LLM) agents lacks a unified formal framework, resulting in conceptual and methodological ambiguity that hinders implementation-agnostic analysis and comparison. To address this gap, this work proposes the Structural Context Model—a novel formalism that integrates a declarative implementation framework with a semantic dynamic analysis workflow. This approach yields, for the first time, an analyzable, self-consistent, and implementation-independent formal model of LLM agents, enabling systematic design and iteration across their entire lifecycle. Empirical validation on the highly challenging dynamic Monkey-and-Bananas problem demonstrates a 32-percentage-point improvement in task success rate for agents developed within this framework, underscoring both its theoretical rigor and practical engineering utility.

Technology Category

• Natural Language Processing: (Large) Language Models
• Machine Learning: Large Multimodal Models (LMMs)
• Multiagent Systems: Modeling other Agents

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems

Abstract

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.

Problem

• LLM-based agents
• formal modeling
• context structure
• agent comparison
• implementation independence

Innovation

• Structural Context Model
• Semantic Dynamics Analysis
• LLM-based agents
• formal modeling
• declarative implementation framework