When the Latest State Does Not Stick: STATEFLOW for Reliable Decisions over Evolving Records

The latest record is not always the current state. In evolving organizational records, the value that should govern a decision may need to be reconstructed from a sequence of updates involving source authority, hierarchical inheritance, entity migration, persistent overrides, and derived rules. Meanwhile, outdated but plausible values remain visible in the history and can still influence an LLM's answer. We study this failure mode, which we call the derived-state persistence gap. Using 1,200 mechanism-isolating benchmark instances, we apply matched interventions that remove stale semantics, explicitly provide the resolved state, or hide history at decision time. Across seven model configurations, these interventions improve macro exact set accuracy from 62.7% to 89.4-91.1%, showing that failures arise not only from stale information, but also from reconstructing and reasoning over the current state. We introduce STATEFLOW, which verifies whether each update is authoritative and applicable, executes valid state transitions externally, and presents the decision model with a canonical current state instead of the full history. STATEFLOW reaches 94.8% accuracy, improving over Full History by 32.1 points and over a deployable LLM State Rewrite baseline by 12.1 points. A format-matched reference-state ablation reaches the same 94.8% accuracy with identical final decision inputs, localizing the remaining system level gap to state construction. A natural history evaluation on public Jira issues shows the same benefit from explicit current-state representation and history isolation. Our results suggest that reliable reasoning over evolving records requires reconstructing the state that is valid now, rather than simply attending to what was written most recently.