How Multi-Model AI Pipelines Lose the Truth at Handoffs
Consider a hypothetical but representative handoff. A research model returns a careful finding: an association observed in one dataset, in a controlled setting, with limited generalization beyond the domain tested. Correlation, not cause. A preprint, not a settled result.
Then a writing model receives a compressed summary and produces a clean sentence: Studies show that X causes Y.
Nothing in the pipeline threw an error. The JSON parsed. The next stage ran. The prose reads well. And yet the sentence is now wrong in a way the original was not. The handoff succeeded syntactically and failed epistemically.
Here is the thesis: in a multi-model system, the boundaries between models are a critical and often underdesigned reliability surface alongside model choice. Evidence can degrade at those seams without triggering a syntax or runtime error. Reliable pipelines therefore need explicit interface design, not only capable agents.
Two abstract robotic runners exchange a...
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