Toward Measuring Structural Drift in LLM Communication Loops
Quick summary
arXiv:2604.13061v3 Announce Type: replace-cross Abstract: 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
Key takeaways
- arXiv:2604.13061v3 Announce Type: replace-cross Abstract: 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.
Why it matters
“Toward Measuring Structural Drift in LLM Communication Loops” illustrates how changes in the AI ecosystem can affect products, workflows and user expectations together. Its lasting significance depends on measurable adoption, cost and safety outcomes.
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