≈$3.7M DELIVERY ORDER HQ003425FE453

University of Maryland's Defense Department research on modeling "population complexity" with OSINT and LLMs for decision support — a federal contract (USAspending)

Department of Defense 2025-09-26 〜 2027-09-25

A roughly $3.69 million research contract awarded by the Defense Department's Washington Headquarters Services to the University of Maryland, College Park. It explores using AI and large language models (LLMs) to analyze open-source information (OSINT and PAI) for decision support.

Contract key facts

  • RecipientUNIVERSITY OF MARYLAND, COLLEGE PARK
  • Contract value$3,691,895 (≈$3.7M)
  • Awarding agencyDepartment of Defense
  • Awarding sub-agencyWashington Headquarters Services
  • Award typeDELIVERY ORDER
  • Period of performance2025-09-26 〜 2027-09-25
  • Contract ID (PIID)HQ003425FE453

Contract scope (original)

CONDUCT RESEARCH RELEVANT TO THE USE OF OSINT AND PAI WITH ARTIFICIAL INTELLIGENCE (AI) TOOLS AND LARGE LANGUAGE MODELS (LLMS) TO SUPPORT THE CREATION OF DECISION SUPPORT THAT CAN MODEL POPULATION COMPLEXITY BASED UPON OSINT.

Key points

  • A research contract from the DoD's Washington Headquarters Services to the University of Maryland, College Park.
  • Valued at $3,691,895, running from September 26, 2025 to September 25, 2027.
  • Research on analyzing OSINT (open-source intelligence) and PAI (publicly available information) with AI and large language models (LLMs).
  • The stated purpose is to model "population complexity" and create decision support.
  • Structured as a delivery order (an order against a broader contract); a research-stage effort rather than operational deployment.
  • Framing it as research rather than deployment reads as intent to establish both the usefulness and the limits.

1A government body commissioning a university

This contract follows a familiar pattern: a government body commissioning research from a university. The awarding office, the Defense Department's Washington Headquarters Services, supports the operation and administration of the DoD headquarters, and one of its components has turned to a university to bring in outside expertise.

The recipient, the University of Maryland, College Park, is a public research university close to the nation's capital and a natural home for this kind of government-linked research.

2OSINT and PAI as the sources

At the center of the work are OSINT (open-source intelligence—drawing insight from sources anyone can access, such as news, public records, and web content) and PAI (publicly available information).

The stated purpose in the original data is to combine these with AI tools and large language models (LLMs—AI trained on large bodies of text that can generate and analyze natural language) to model "population complexity" in support of decision-making. It is one example of the broader effort to apply LLMs, on a research basis, to government analytical work.

3Examined as research, not deployment

What this contract illustrates is that the government is carefully examining—at the research stage—where and how rapidly spreading generative AI and large language models might be used. Framing it as a research contract rather than an operational deployment suggests an intent to gauge both the usefulness and the limits of these tools.

Because the theme involves analyzing public information and modeling people, questions about privacy and the handling of information can arise as a general matter; however, the specific scope, methods, and deliverables of this contract are not described in the original data, so no conclusions can be drawn here.

4Within the wider AI procurement trend

Viewed more broadly, this sits within the wider trend of AI procurement and AI research across the federal government. A single contract published on USAspending lets anyone trace which technologies the government is testing, with which universities, and at what scale—offering citizens a window into both how tax dollars are spent and the direction of technology policy.

5Deploying it, or examining it as research

That this contract takes the form of research says something about how generative AI is being handled.

Deploying it in operationExamining it as research (this contract)
Effects arrive soonerBoth usefulness and limits can be established
Problems appear after the factProblems can be surfaced in advance
Judgement forms inside the workThe material for judgement is produced as research

At the centre are OSINT — intelligence from openly available sources — and PAI, publicly available information, combined with AI tools and large language models to model population complexity in support of decisions.

Because it deals with analysis of public information and modelling about people, questions of privacy and information handling can arise in general terms, though the specific scope and methods do not appear in the source.

Why it matters

It matters as an example of evaluating—through a research contract rather than operational use—how generative AI and large language models might serve government analytical work. From open contract data, citizens can track federal AI procurement and research trends, how tax dollars are spent, and the direction of technology policy.

FAQ

What are OSINT and PAI?
OSINT is open-source intelligence: deriving insight from sources anyone can access, such as news, public records, and web content. PAI stands for publicly available information.
What are the specific targets and deliverables of this research?
The original data states the purpose as using OSINT and PAI with AI and LLMs to model "population complexity" for decision support. Beyond that, the specific scope and deliverables are not described in the original data.
What is a delivery order?
It is an individual order for work or goods placed against a previously established, broader contract framework—rather than a new standalone contract.

Sources (primary)

This article is an independent organization based on the U.S. official spending data below. Verify the exact, latest details with the official source.

#federal contract#Department of Defense#University of Maryland#OSINT#large language models#AI research#decision support
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