Autonomous Metacognitive Collection Orchestration for Multi-Domain Intelligence

SBIR topic DPA27BZ02-DV002 (U.S. Department of Defense – DARPA), Phase I and II. Open to U.S. small businesses. Opens: December 2, 2026. Closes: January 6, 2027. Modern intelligence, surveillance, and reconnaissance (ISR) enterprises ingest multi-source indicator streams whose native tempos span four or more orders of magnitude. Overhead imagery (EO/IR, SAR, MSI, HSI) arrives at revisit cadences of minutes to hours; RF and SIGINT detections resolve in seconds to minutes; maritime AIS and acoustic returns update continuously at sub-second tempos; and open-source, supply-chain, and pattern-of-life indicators mature over hours to weeks. A single decision of operational consequence, such as whether a vessel of interest is a combatant or a civilian fisher, or whether an observed buildup presages a near-term operation or reflects routine activity, typically turns on whether causally related evidence drawn from several of these phenomenologies can be assembled, reasoned about, and surfaced fast enough and cheaply enough to matter. Classical early- and late-fusion pipelines assume temporally aligned inputs and emit single snapshot outputs, useful when cadences match and brittle when they differ by orders of magnitude or when a depended-on sensor is denied. Reinforcement- and active-learning policies can select next observations but treat operational constraints as reward-shaped scalars the optimizer can trade away, and require retraining cycles measured in weeks when priority intelligence requirements or adversary tactics shift in hours. Neuro-symbolic and causal methods contribute structure and interpretability, yet in practice operate over hand-authored graphs and synchronous data, with post-hoc explanations rather than forward-looking traces. Agentic large-language-model orchestrators produce narrative rationalizations whose internal state cannot be audited, and whose plans cannot be inspected for constraint feasibility before execution. What is missing across all of…