Toward Measuring Structural Drift in LLM Communication Loops
Large language models increasingly run in stateful pipelines that assemble each prompt from retrieval, memory, tools, and other agents. Such pipelines drift: information that should shape the next response is dropped, compressed, or misrouted while every component still reports success. Existing diagnostics miss this because they evaluate isolated prompts, responses, or task scores, whereas what decouples is the relation between a prompt and the response it draws. Here we show that treating the prompt to response to next prompt chain as the fundamental unit of analysis makes these relations measurable. We introduce structural communication coherence, quantified by two metrics: communicati...
| Authors | Wael Hafez, Amir Nazeri, Chenan Wei |
|---|---|
| Primary category | cs.CL |
| Published | 18 March 2026 |
| Last updated at source | 23 September 2026 |
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