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EMHAT attacks a measurement problem that is quietly blocking a lot of human-AI teaming work: you cannot evaluate a human-AI team at scale if every evaluation requires recruiting real humans for every configuration you want to test. DARPA's answer is to build the humans in simulation.
The goal is to develop technologies for generating and evaluating diverse and realistic digital twins representing human-AI teams, so that emergent capabilities and limitations can be characterised in proxy operational settings.
Concretely, the program uses generative AI to create computational agents that stand in as diverse human teammate simulacra, models how those synthetic humans interact with an AI system on a collaborative task, measures task completion rates against a baseline, and assesses how the AI adapts its behaviour in response to simulated human behaviour.
The deliverable is a modeling and simulation framework producing naturalistic data suitable for quantitative assessment of human-machine team effectiveness in realistic scenarios. The word doing the most work in the program description is diverse - the premise is that human-AI team behaviour varies enormously across operator backgrounds and that single-population evaluation misses emergent failure modes entirely.
EMHAT was informed by the workshops that produced DARPA's AI Forward initiative, and reflects DARPA's stated view that evaluation paradigms for human-AI teams need updating to handle the behaviours emerging across diverse team configurations. It is managed by program manager Matthew Marge under IPTO, alongside FACT. Released under AIE, it carries the mechanism's three-month kickoff and 18-month feasibility structure.
This is a strong reference point for anyone building human-AI teaming evaluation infrastructure, and a watch entry for the next AI Forward AIE in this line.
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Or search similar grants →According to the current listing, eligibility includes: DARPA states explicitly that additional information including the AIE opportunity number, award amounts, duration and application deadlines is available in the official EMHAT Program Announcement on SAM.gov, and that announcement rather than the program page governs. DARPA AIE announcements of this type are typically open to universities, non-profit research organisations, FFRDCs subject to restrictions, and for-profit companies including small businesses. The AIE mechanism provides an 18-month period of performance aimed at establishing feasibility of a new AI concept, with awards up to $1,000,000 as described in each AIE Opportunity, and DARPA compresses proposal-to-kickoff to roughly three months. The submission window for the EMHAT announcement has closed and no application route is currently open on the program page. Groups working on modeling and simulation of human-machine teams, generative agent populations, digital twins of operators, or quantitative human-AI team evaluation should read the EMHAT Program Announcement on SAM.gov to understand DARPA's framing, and should monitor darpa.mil/work-with-us/opportunities and the DARPA opportunities RSS feed for successor AIE opportunities. Confirm award value, eligibility, period of performance and deadlines directly in the governing SAM.gov announcement before preparing a submission. Confirm the full requirements in the official notice before applying.
The current listing shows EMHAT is an Artificial Intelligence Exploration (AIE) opportunity and DARPA directs readers to the EMHAT Program Announcement on SAM.gov for the award amount, duration and deadline; no EMHAT-specific dollar figure appears on the program page. The AIE mechanism states awards may be worth up to $1,000,000 each over an 18-month feasibility period, and amount_min and amount_max are both set to 1,000,000 on that basis. This is the inherited AIE ceiling, not an EMHAT-specific published number, and the governing SAM.gov announcement should be treated as authoritative. Verify award ceilings, matching requirements, and allowable costs in the official notice.
DARPA EMHAT (Exploratory Models of Human-AI Teams) AI Exploration Opportunity for Generative Digital Twins of Human-Machine Teams is funded by U.S. Defense Advanced Research Projects Agency (DARPA), Information Processing Techniques Office (IPTO). Verify program details on the funder's official page before applying.
Start from the official opportunity page linked in this listing — it carries the sponsor's submission instructions.
The NSF Computer and Information Science and Engineering: Future Computing Research (Future CoRe) program (NSF 25-543) supports foundational computing research and education with a strong emphasis on AI and machine learning. The program funds research that advances the foundations of computing including AI/ML theory, algorithms, systems, and applications. Future CoRe supports small to medium-sized research projects that can have significant impact on computing foundations. The program has target dates of February 5, 2026 and September 10, 2026 for proposal submissions (note: these are target dates, not deadlines, meaning proposals may be submitted at any time but will be reviewed in batches around these dates). Investigators may not serve as PI, co-PI, or Senior/Key Personnel on more than two proposals submitted within any consecutive 12-month period across all Future CoRe programs. This is one of NSF's primary mechanisms for funding fundamental AI and machine learning research at U.S. academic institutions, covering areas such as machine learning theory, natural language processing, computer vision, robotics foundations, and human-AI interaction.
The ONR Long Range Broad Agency Announcement (N00014-25-S-B001) is the Office of Naval Research's primary mechanism for soliciting research proposals across all naval science and technology priority areas. The BAA accepts proposals on a rolling basis through September 30, 2026 and covers ONR's full spectrum of research interests with particular emphasis on AI-related topics including autonomous maritime systems, human-machine teaming, machine learning for sensor fusion, cooperative autonomous swarm technology, undersea autonomy, and AI-enabled decision superiority. Proposals can be funded through multiple mechanisms including individual investigator grants, the Young Investigator Program (~$510K over 3 years for early-career faculty), and Multidisciplinary University Research Initiative (MURI) awards ($1.5M/year for 3-5 years for research teams). ONR recommends contacting relevant program officers before submitting to discuss alignment with current research priorities. The BAA supports basic research (6.1), applied research (6.2), and advanced technology development (6.3) across the full range of naval-relevant science and engineering disciplines.
DE-FOA-0003600 is the DOE Office of Science's FY 2026 open, rolling solicitation for financial assistance, providing roughly $500 million across seven program areas: Advanced Scientific Computing Research, Basic Energy Sciences, Biological and Environmental Research, Fusion Energy Sciences, High Energy Physics, Nuclear Physics, and Isotope R&D and Production. AI and machine learning research is supported directly through Advanced Scientific Computing Research, while Biological and Environmental Research funds atmospheric process research, environmental systems process research, and earth-energy systems modeling — making this a major channel for AI-enhanced climate and earth system modeling work. DOE anticipates 200 to 350 new awards ranging from $5,000 to $5 million each, with project periods from six months to five years. The FOA opened September 30, 2025 and accepts applications on a rolling basis through the end of FY 2026.
DPA26BZ06-DV023 is a Direct-to-Phase-II SBIR paying $700,000 over 18 months plus a $500,000 option. The physics demands 256x more transmit power than the systems that qualify you to compete, and DARPA will not accept modeling alone as proof. Here is the eligibility wall, the five engineering problems, and who can realistically win it before the October 21 close.
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