Offline AI matches analysts’ doctrine score in small pilot

An offline AI system scored 8 out of 10 on an electronic-target identification doctrine assessment, matching the human score in a pilot involving four Brazilian Air Force image analysts. The authors report that the system took 7.1 minutes, against a human average of 26.5 minutes.

Their preprint, submitted to arXiv on 18 September, describes an assistant for isolated environments where analysts cannot rely on external computing resources. It is not peer-reviewed evidence.

Consulting manuals without a connection

The proposed architecture retrieves material from technical manuals and supports both text and image inputs. It is designed to let analysts ask questions in natural language while keeping doctrinal answers traceable to their original references.

The pilot also measured the workload of manually producing a reconnaissance target report. Analysts rated mental demand at 6 out of 7 and effort at 5 out of 7.

Those workload measurements came from work without AI assistance. The assessment timings therefore cannot be read as a demonstrated reduction in report-writing time. The authors outline a future comparison of manual and AI-assisted workflows; this pilot supplies the baseline for that test.