OpenLoop

Industrial shift logs record what operators wrote, not what remains unfinished. Technicians leave free-text notes covering machine status, maintenance work, delayed reports, and corrections. When a note logs a pending safety test and a later note says the pump is operating normally, or a backdated entry arrives after a task was reopened, incoming supervisors must manually reconcile what is still open before the next operating window. OpenLoop is an evidence-based semantic reconciliation layer for industrial shift handovers. Instead of asking an LLM to summarize notes or maintain state in prompt memory, OpenLoop separates semantic extraction from state resolution: 1. Semantic Extraction (Gemma-4-31B-IT): Reads each unstructured shift note against the equipment and task registry and emits strict JSON containing typed assertions (pending, completed, status, retract), a mandatory verbatim substring quote, and a resolved occurrence timestamp via an OpenAI- compatible HTTPS adapter. Ambiguous equipment references are routed to supervisor review rather than guessed. 2. Deterministic Python Evidence Ledger: Verifies that every quote appears literally in the source note, isolates equipment operating status on a separate rail from task obligations, filters available knowledge by recording time (recorded_at), and resolves task state from the latest active assertion by occurrence time (occurred_at). Explicit corrections withdraw prior claims by ID without deleting historical records. Across two runs of a four-way benchmark on four synthetic held-out sequences (12 checkpoints per run), OpenLoop produced correct documented task states at 19 of 24 planned checkpoints (12/12 in Run 1; 7/12 in Run 2 with 5 client 45s timeouts) and zero false closures among its 19 valid checkpoints, while the full-history LLM baseline produced false closures at the same two checkpoints in both runs.