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

Haoyu Jia, Kento Kawaharazuka, Kei Okada

TL;DR

The paper addresses fragmentation in LLM-based agent literature by introducing a formal, implementation-agnostic Structural Context Model (SCM) and a complementary Semantic Dynamics Analysis (SDA) to analyze, compare, and iteratively improve agent designs. By modeling agents as sequences of context patterns and employing dynamic semantics, the authors unify memory, RAG, ICL, and multi-agent collaboration within a single framework and demonstrate a closed-loop workflow via ContextCompose. A substantial case study on dynamic monkey–banana scenarios shows that the PlanORN design—combining planning, on-demand reasoning, and notes—achieves robust performance across increasing task difficulty, outperforming several baselines. The work highlights a path toward data-driven, reusable context patterns and principled agent engineering, with implications for scalable, implementation-independent agent science and practice.

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.

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

TL;DR

The paper addresses fragmentation in LLM-based agent literature by introducing a formal, implementation-agnostic Structural Context Model (SCM) and a complementary Semantic Dynamics Analysis (SDA) to analyze, compare, and iteratively improve agent designs. By modeling agents as sequences of context patterns and employing dynamic semantics, the authors unify memory, RAG, ICL, and multi-agent collaboration within a single framework and demonstrate a closed-loop workflow via ContextCompose. A substantial case study on dynamic monkey–banana scenarios shows that the PlanORN design—combining planning, on-demand reasoning, and notes—achieves robust performance across increasing task difficulty, outperforming several baselines. The work highlights a path toward data-driven, reusable context patterns and principled agent engineering, with implications for scalable, implementation-independent agent science and practice.

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.
Paper Structure (38 sections, 4 theorems, 19 equations, 13 figures, 8 tables, 1 algorithm)

This paper contains 38 sections, 4 theorems, 19 equations, 13 figures, 8 tables, 1 algorithm.

Table of Contents

  1. Introduction
  2. Structural Context Model for Agent Description
  3. Relationships with Automata Theory & Compositional Semantics
  4. Proposal: Structural Context Model
  5. Symbol Definitions
  6. Special Functions
  7. Unifying Advanced Concepts
  8. Agent Memory & Chat History
  9. Agent Action & Chat Tool
  10. In-context Learning
  11. Retrieval Augmented Generation
  12. Multi-agent Collaboration
  13. Comparing Agents at a Fundamental Level
  14. Engineering Efforts
  15. Semantic Dynamics Analysis for Pattern Discovery
  16. ...and 23 more sections

Key Result

Proposition 1

Let $\alpha$ and $\beta$ be two token spans. If $\alpha$ semantically includes $\beta$, then for any base text $\gamma \in \Omega$,

Figures (13)

  • Figure 1: We present a composable, explainable, and sustainable framework for LLM agent research and development. Starting from the Structural Context Model, agents are analyzed and compared at a fundamental level, enabling Semantic Dynamics Analysis to discover reusable context patterns. These patterns are then recomposed to rapidly iterate agent designs, which are systematically evaluated on controlled robotic planning benchmarks, forming a closed-loop workflow for principled agent improvement.
  • Figure 2: The looping architecture of reason–act agents consists of three stages: (1) the system observes the environment state and formats the observations as requests to the LLM; (2) the LLM reasons about the next action based on the current environment state, the LLM context, and the relevant knowledge; (3) the system executes the selected action and waits for the subsequent observations.
  • Figure 3: Disorder in current research on LLM-powered agents. Left: An overabundance of overlapping concepts is currently in use, unnecessarily increasing the cognitive burden on both authors and readers. Right: The tight coupling between engineering details and methodological descriptions obscures the essence of these innovations, leading to inaccurate positioning of the corresponding research. Representative works mentioned here include: ReActyao2023react, Recursive-LMzhang2025RecursiveLM, LLM-PDDLsilver2022LLM_PDDL, Retroformeryao2023retroformer, Reflexionshinn2023reflexion, ToolMakercai2023ToolMakers, ToTyao2023TreeOfThoughts, MLDTwu2024MLDT, InnerMonologuehuang2022InnerMonologue, AIOSmei2024aios, MemGPTpacker2023memgpt.
  • Figure 4: Intuitive description of key terms in Semantic Dynamics Analysis. $\Delta$Semantics measures the semantic distance between successive prefixes, capturing the semantic update potential introduced by the appended token. Global Drift measures the semantic distance between a prefix and the full text, indicating the extent to which the overall meaning would differ if the text were to end at the current prefix. Global $\Delta$Drift measures the rate at which the current prefix approaches the final meaning of the full text, reflecting the velocity of semantic convergence.
  • Figure 5: Results of Semantic Dynamics Analysis for a task-decomposition prompt excerpted from an open-source prompt library. The blue line represents Global Drift, the blue bars indicate $\Delta$Semantics, and the orange bars denote Global $\Delta$Drift. Red circles mark potential semantic separation points, where Global $\Delta$Drift exhibits relatively large values.
  • ...and 8 more figures

Theorems & Definitions (4)

  • Proposition 1: Sub-additivity of $\Delta$S under Semantic Inclusion
  • Proposition 2: Super-additivity of $\Delta$S under Semantic Orthogonality
  • Proposition 3: Approximate $\Delta$S Idempotence under Semantic Idempotence
  • Proposition 4: Approximate $\Delta$S Invariance under Semantic Order-Invariance