NSF award 2541848 · University of North Carolina at Chapel Hill

CAREER: Learning to See, Reason, and Act: Language-Grounded Video Intelligence for Perceptual AI Agents

Final report due

28 November 2031

in 1,867 days · The award ends on 31 July 2031.

GrantClerk is a private service. We are not NSF or any government agency.

Dates this award owes

NSF emails you about this too.

Decide about an extension2 Apr 2031in 1627 days
award ending 2031-07-31 · Research.gov
This one is ours, not NSF’s.
Extension request16 Jun 2031in 1702 days
award ending 2031-07-31 · Research.gov
PAPPG 24-1: “at least 45 days prior to the end date”
No-cost extension notice21 Jul 2031in 1737 days
award ending 2031-07-31 · Research.gov
PAPPG 24-1: “at least ten calendar days prior to the end date”
Final report due28 Nov 2031in 1867 days
award ending 2031-07-31 · Research.gov
PAPPG 24-1: “no later than 120-days following the end date of the award”
Project outcomes report due28 Nov 2031in 1867 days
award ending 2031-07-31 · Research.gov
PAPPG 24-1: “no later than 120 days following the end date”
Award is closed out28 Nov 2031in 1867 days
award ending 2031-07-31 · Research.gov
PAPPG 24-1: “financially closed out 120 days after their expiration date”

Annual report dates come from the budget period on your award notice. That date is not public, so it is not shown here.

How we got this date

NSF lists this award's end date as 31 July 2031.
NSF rules say the final report is due no later than 120 days after that.
So the final report is due by 28 November 2031.

Read the rule on Research.gov

Get a note before each report date

Add this award to your desk. We email you before each date and keep the dates in one place.

Renews each year until you cancel. Cancel any time from your account.

Already a member? Sign in

Award card

Award number: 2541848
Period of support: 1 Aug 2026 – 31 Jul 2031
Amount: $584,937
Awardee: University of North Carolina at Chapel Hill, Chapel Hill, NC
Program: Robust Intelligence
Directorate: CSE

Abstract

Modern artificial intelligence (AI) systems can recognize simple actions in short video clips, yet they cannot understand the full complexity of events unfolding over time. They struggle to follow multi-step activities, explain why something happened, imagine what would happen under different circumstances, or use what they observe to guide physical actions. These limitations prevent AI from having a transformative…

Is this right?

We copy this from the public NSF award record. Your award notice and NSF always win. If a date looks wrong, check Research.gov.