Information Fidelity in Tool-Using LLM Agents: A Martingale Analysis of the Model Context Protocol

Flint Xiaofeng Fan, Cheston Tan, Roger Wattenhofer, Yew-Soon Ong

AAMAS 2026 · Poster

How does information change as an LLM agent makes sequential tool calls? We study cumulative distortion using a hybrid measure of fact preservation and semantic similarity. Under the stated assumptions, a martingale analysis gives concentration bounds around cumulative drift. The figure contrasts uncontrolled information loss with a workflow that periodically re-grounds the agent.

Uncontrolled drift versus a controlled-fidelity workflow with periodic re-grounding.
Uncontrolled drift versus a controlled-fidelity workflow with periodic re-grounding.

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