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This Solicitation opportunity from Department of Defense was posted on August 5, 2026. The submission period has ended. Browse the details below for market research, or find similar active opportunities.

Scalable Platform for Enterprise Engineering and Deployment towards Mathematics for the Discovery of Algorithms and Architectures (SPEED DIAL)

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DPA26TZ05-DV003SBIR / STTR

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The Scalable Platform for Enterprise Engineering and Deployment towards Mathematics for the Discovery of Algorithms and Architectures (SPEED DIAL) is a Small Business Technology Transfer (STTR) solicitation issued by the Defense Advanced Research Projects Agency (DARPA) under the Department of Defense. The program aims to transition AI-driven algorithm discovery from research environments into practical commodities by creating a scalable platform that allows domain experts, such as physicists and engineers, to collaboratively discover, refine, and deploy high-performance algorithms for real-world applications. The project focuses on developing a closed-loop pipeline utilizing Monte Carlo Tree Search and Genetic Programming to autonomously generate and evaluate mathematical variations in the background, ensuring that optimal algorithms can be generated on-the-fly to meet specific engineering constraints. The 24-month period of performance is structured around several key milestones, beginning with the delivery of a system architecture and initial algorithm library by month 4, followed by the prototype discovery engine at month 8, and the release of API wrappers and auto-compilation tools by month 12. The project culminates in two Department of War use case demonstrations, including a target of over 10 percent improvement in computational efficiency for problems such as hypersonic design, and a final software release with a Phase III transition plan. Award selections are based on overall technical value, technical merit, investigator qualifications, and commercial potential. Compliance requirements include CMMC Level 2 certification for firms handling controlled unclassified information and registration in the System for Award Management.

General Info

DARPA STTR program creating a scalable AI platform for autonomous high-performance algorithm discovery.

Documents

(2)

DARPA STTR 2026 Broad Agency Announcement Proposal Submission Instructions Release 5

PDF•baa-proposal-instructions

DoW 2026 STTR Broad Agency Announcement Amendment 2

PDF•baa-amendment

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Timeline

PhaseClosed
Posted

Solicitation

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Submission Closed

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Organization & Contact Information

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AgencyDepartment of Defense → Defense Advanced Research Projects Agency
ContactsNo contacts available
OfficeUSA
Office AddressUSA
ContactsNo contact information available

Full Description

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The DARPA DIAL [1] program has successfully demonstrated that AI can autonomously discover novel, high-performance algorithms for a range of scientific and engineering problems. Recent efforts have shown that models such as Transformers can rediscover fundamental algorithms like the Kalman Filter, Genetic Programming can rediscover Wavelets, Accelerated Monte Carlo Tree Search can rediscover optimization algorithms, and AI-driven methods can produce optimal meta-solvers for complex physics simulations. However, such advanced discovery engines are currently siloed in research environments; they are not yet deployed for scientists to discover new algorithms before they execute their standard scientific process.This project will address this gap by creating a framework to transition both previously discovered algorithms and the algorithm discovery engines themselves from curiosities to commodities [2-5]. The goal is to create a symbiotic relationship between algorithm developers and domain experts (e.g., engineers, physicists), enabling them to collaboratively discover, build, refine, and deploy algorithms for real-world applications. We will foster a partnership between universities (with deep expertise in algorithmic discovery) and companies (representing the US industrial base) to embed these tools directly into standard workflows, allowing engineers to generate optimal algorithms on-the-fly for their specific constraints.

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