Closed · HR001125S0010 · CFDA 12.910 · Discretionary
Exponentiating Mathematics (expMath)
Federal grant opportunity posted by DARPA - Information Innovation Office, cataloged on Grants.gov.
- Varies
- Award range
- Closed
- Status
- July 15, 2025
- Close date
- -
- Expected awards
The verdict
Exponentiating Mathematics (expMath) is a closed discretionary listing from DARPA - Information Innovation Office that offered Varies by applicant. Future funding cycles may be published under the same CFDA number.
- Varies
- award range
- Closed
- application status
- 12.910
- CFDA program
Opportunity snapshot. This Grants.gov announcement - Exponentiating Mathematics (expMath) - is cataloged under number HR001125S0010 and tied to CFDA assistance listing 12.910, posted by DARPA - Information Innovation Office. Grants.gov currently shows the opportunity as closed, first posted on April 30, 2025 and last updated on June 10, 2025. The funding category is Discretionary, delivered as a procurement contract.
Award economics. The award range on file is Varies by applicant. Cost sharing is not required, so applicants do not need to commit matching funds to be competitive on this opportunity. Federal award ranges are often upper bounds; actual allocations reflect program appropriations, the strength of the applicant pool, and the evaluation committee's scoring.
Deadline and action path. This opportunity closed on July 15, 2025. Future funding cycles may be published under the same CFDA number, so monitoring the parent program page is the most reliable way to catch re-announcements. Every Grants.gov submission requires an active SAM.gov registration and a Unique Entity ID. Review the Eligibility section below carefully, federal eligibility categories (nonprofit, state or local government, tribal, individual, educational institution, small business) have distinct registration and reporting requirements. Pre-application outreach to the listed agency contact is permitted and often welcomed, it helps clarify scope and scoring priorities. Before acting on the deadline or award figures above, verify them directly on the official Grants.gov listing, amendments can change dates and amounts after this page was last refreshed.
Award Range
Varies by applicant
Close Date
July 15, 2025
See Full Announcement for details.
Posted
April 30, 2025
Instrument
Procurement Contract
Description
MATHEMATICS IS THE SOURCE OF SIGNIFICANT TECHNOLOGICAL ADVANCES; HOWEVER, PROGRESS IN MATH IS SLOW. Recent advances in artificial intelligence (AI) suggest the possibility of increasing the rate of progress in mathematics. Still, a wide gap exists between state-of-the-art AI capabilities and pure mathematics research. Advances in mathematics are slow for two reasons. First, decomposing problems into useful lemmas is a laborious and manual process. To advance the field of mathematics, mathematicians use their knowledge and experience to explore candidate lemmas, which, when composed together, prove theorems. Ideally, these lemmas are generalizable beyond the specifics of the current problem so they can be easily understood and ported to new contexts. Second, proving candidate lemmas is slow, effortful, and iterative. Putative proofs may have gaps, such as the one in Wiles’ original proof of Fermat’s last theorem, which necessitated more than a year of additional work to fix. In theory, formalization in programming languages, such as Lean, could help automate proofs, but translation from math to code and back remains exceedingly difficult. The significant recent advances in AI fall short of the automated decomposition or auto(in)formalization challenges. Decomposition in formal settings is currently a manual process, as seen in the Prime number theorem and beyond and the Polynomial Freiman-Ruzsa conjecture, with existing tools, such as Blueprint for Lean, only facilitating the structuring of math and code. Auto(in)formalization is an active area of research in the AI literature, but current approaches show poor performance and have not yet advanced to even graduate-level textbook problems. Formal languages with automated theorem-proving tools, such as Lean and Isabelle, have traction in the community for problems where the investment in manual formalization is worth it. The goal of expMath is to radically accelerate the rate of progress in pure mathematic
Eligibility
Grants.gov lists this opportunity under eligibility category code 25. These codes correspond to applicant types (state/local government, tribal organization, nonprofit, educational institution, individual, small business, etc.) defined in Grants.gov's own eligibility reference. See the current Grants.gov eligibility categories or check the official listing below for this opportunity's exact eligibility statement.
Official Listing on Grants.gov
View full details, application forms, and submission instructions.
Agency Contact
BAA Coordinator expMath@darpa.mil
Key Dates
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| Sources | the SAM.gov Assistance Listings and Grants.gov |