Information Fidelity in Tool-Using LLM Agents: A Martingale Analysis of the Model Context Protocol
arXiv:2602.13320v1 Announce Type: new Abstract: As AI agents powered by large language models (LLMs) increasingly use external tools for high-stakes decisions, a critical reliability question arises: how do errors propagate across sequential tool calls? We introduce the first theoretical framework for analyzing error accumulation in Model Context Protocol (MCP) agents, proving that cumulative distortion exhibits linear growth and high-probability deviations bounded by $O(\sqrt{T})$. This concentration property ensures predictable system behavior and rules out exponential failure modes. We develop a hybrid distortion metric combining discrete fact matching with continuous semantic similarity, then establish martingale concentration bounds on error propagation through sequential tool interactions. Experiments across Qwen2-7B, Llama-3-8B, and Mistral-7B validate our theoretical predictions, showing empirical distortion tracks the linear trend with deviations consistently within $O(\sqrt{
arXiv:2602.13320v1 Announce Type: new Abstract: As AI agents powered by large language models (LLMs) increasingly use external tools for high-stakes decisions, a critical reliability question arises: how do errors propagate across sequential tool calls? We introduce the first theoretical framework for analyzing error accumulation in Model Context Protocol (MCP) agents, proving that cumulative distortion exhibits linear growth and high-probability deviations bounded by $O(\sqrt{T})$. This concentration property ensures predictable system behavior and rules out exponential failure modes. We develop a hybrid distortion metric combining discrete fact matching with continuous semantic similarity, then establish martingale concentration bounds on error propagation through sequential tool interactions. Experiments across Qwen2-7B, Llama-3-8B, and Mistral-7B validate our theoretical predictions, showing empirical distortion tracks the linear trend with deviations consistently within $O(\sqrt{T})$ envelopes. Key findings include: semantic weighting reduces distortion by 80\%, and periodic re-grounding approximately every 9 steps suffices for error control. We translate these concentration guarantees into actionable deployment principles for trustworthy agent systems.
Executive Summary
The article 'Information Fidelity in Tool-Using LLM Agents: A Martingale Analysis of the Model Context Protocol' presents a groundbreaking theoretical framework for analyzing error propagation in AI agents that use large language models (LLMs) and external tools. The study introduces a hybrid distortion metric that combines discrete fact matching with continuous semantic similarity, establishing martingale concentration bounds on error propagation through sequential tool interactions. Empirical validation across various LLMs confirms the theoretical predictions, demonstrating that semantic weighting significantly reduces distortion and periodic re-grounding effectively controls errors. The findings offer actionable principles for deploying trustworthy agent systems, ensuring predictable behavior and mitigating exponential failure modes.
Key Points
- ▸ Introduction of a theoretical framework for analyzing error accumulation in Model Context Protocol (MCP) agents.
- ▸ Proof that cumulative distortion exhibits linear growth with high-probability deviations bounded by O(√T).
- ▸ Development of a hybrid distortion metric combining discrete fact matching and continuous semantic similarity.
- ▸ Empirical validation across Qwen2-7B, Llama-3-8B, and Mistral-7B showing distortion tracks linear trend within O(√T) envelopes.
- ▸ Key findings include semantic weighting reducing distortion by 80% and periodic re-grounding every 9 steps for error control.
Merits
Theoretical Rigor
The article provides a robust theoretical framework that rigorously analyzes error propagation in LLM agents, offering a novel approach to understanding and mitigating errors in sequential tool interactions.
Empirical Validation
The study validates its theoretical predictions through experiments on multiple LLMs, demonstrating the practical applicability of the framework.
Actionable Insights
The findings translate into actionable deployment principles for trustworthy agent systems, providing guidelines for reducing distortion and controlling errors.
Demerits
Limited Scope
The study focuses on specific LLMs and may not be generalizable to all types of LLM agents or tool interactions.
Complexity of Metrics
The hybrid distortion metric, while comprehensive, may be complex to implement and interpret, potentially limiting its immediate practical adoption.
Assumptions and Simplifications
The theoretical framework relies on certain assumptions and simplifications that may not hold in all real-world scenarios, affecting the accuracy of the predictions.
Expert Commentary
The article presents a significant advancement in the field of AI reliability, offering a comprehensive theoretical and empirical analysis of error propagation in LLM agents. The introduction of a hybrid distortion metric and the establishment of martingale concentration bounds provide a novel approach to understanding and mitigating errors in sequential tool interactions. The empirical validation across multiple LLMs lends credibility to the theoretical predictions, demonstrating the practical applicability of the framework. However, the study's focus on specific LLMs and the complexity of the metrics may limit its immediate adoption. Despite these limitations, the actionable insights derived from the study are invaluable for developers and policymakers aiming to deploy trustworthy AI systems. The findings contribute to the broader discourse on AI reliability and safety, highlighting the need for robust frameworks and regulatory guidelines to ensure the safe and effective use of LLM agents in high-stakes decision-making scenarios.
Recommendations
- ✓ Further research should explore the generalizability of the theoretical framework to a broader range of LLM agents and tool interactions.
- ✓ Developers should implement the recommended practices, such as periodic re-grounding and semantic weighting, to enhance the reliability of their LLM agents.
- ✓ Policymakers should consider the findings in developing regulatory frameworks that ensure the reliability and safety of AI systems, particularly those involved in high-stakes decisions.