Autonomous Metacognitive Collection Orchestration for Multi-Domain Intelligence

SBIR · Phase: BOTH · Topic DPA27BZ02-DV002 · Solicitation 27.BZ

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 these approaches is a structured, auditable, constraint-feasible substrate whose rationale for each recommendation is transparent, reproducible, and directly inspectable from internal state. This topic seeks novel approaches to artificial metacognition: an orchestration substrate that reasons about its own reasoning while reasoning about the world. A metacognitive substrate maintains a structured model of evidence-to-outcome relationships across heterogeneous modalities and tempos; tracks its own uncertainty, prediction error, and representational coherence; computes expected information gain for candidate collection actions under hard operational constraints; returns option-sets rather than single collapsed policies; and, when denial, deception, or distribution shift is detected, locally refines its internal model rather than triggering a full retraining cycle. The resulting capability is the computational backbone of a human-machine formation: the autonomous system acts at machine speed inside a bounded operating envelope, while the human commander sets objectives, constraints, and rules of engagement before the mission and reviews the auditable decision record after. Candidate substrate families for the metacognitive layer include hyperdimensional computing and vector-symbolic architectures; neuro-symbolic architectures that couple learned encoders with symbolic or probabilistic inference; and graph-based reasoning over typed knowledge graphs, causal graphs, and structured inference engines. Hybrids across these approaches are encouraged. This topic is deliberately substrate-agnostic within that class. Offerors are expected to justify their choice on quantitative grounds. Any viable substrate shall provide: • Compositional, inspectable operators (algebraic, logical, graph-based, or probabilistic) that support real-time assembly and decomposition of multi-source evidence structures, rather than end-to-end learned embeddings whose compositional semantics cannot be examined. • Bounded degradation under partial, corrupted, delayed, or adversarially manipulated input, quantified against a mainstream deep-learning baseline. • Tra…

SBIR/STTR 은 미국 중소기업만 지원할 수 있습니다(Small Business Act 법정 요건). • 계열사를 포함해 상시 종업원 500명 이하 • 미국 시민 또는 영주권자 1인 이상이 50%를 초과해 직접 소유·지배 • 미국 내 사업장을 두고 주로 미국 내에서 사업을 영위할 것 • 수행책임자(PI)의 주된 근무처가 신청 기업일 것 출처: https://www.sbir.gov/faq/eligibility-requirements

Primary country

🇺🇸 United States

Also eligible

None

Foreign companies

No

Local entity

Required

Location condition

미국 내 사업장을 두고 미국 시민·영주권자가 50%를 초과해 소유한 중소기업만 신청할 수 있습니다(법정 요건).

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Official URL

https://www.dodsbirsttr.mil/topics-app/

Application URL

https://www.dodsbirsttr.mil/topics-app/

Last fetched

2026.11.06

Human review

Not reviewed — check the official announcement

First seen

2026.11.06

Data status

Auto-collected, not reviewed

AI enrichment

Needs verification