ADAPTIVE FUSION, & REASONING OF MULTI-SOURCE DATA (AFAR)

The Air Force Research Laboratory is seeking innovative research to advance data‑fusion, knowledge‑graph generation, and AI‑driven target analysis for C4I and cyber‑science missions. Offerors will submit white‑papers (3‑5 pages) outlining technical approaches for one or more of four research areas: Adaptive Knowledge & Information for Target Analysis (AKITA), Enhancing Moving Target Engagement at Scale (EMoTES), Publicly Available Information Ensemble Fusion (PEF), and Agentic AI Workflows (AWARD). Successful white‑paper authors may be invited to submit full proposals, with potential awards up to $99.9 M total BAA ceiling and individual contracts up to 36 months. The effort emphasizes Gold Standard Science principles, rapid demonstrable results, and compliance with security and S&T protection requirements. • Develop knowledge‑graph pipelines that ingest heterogeneous intelligence (static & dynamic) and provide spatio‑temporal grounding. • Create a Multi‑int Adaptive Recommender System (MARS) to nominate high‑value targets using multi‑sensor data streams. • Design AI/ML methods to assess veracity of open‑source information and fuse it with classified intel, improving threat characterization.