PPPL’s PACMAN framework lets multiple AI models control a tokamak in ~20 ms, preventing a tearing mode
Result confirmed
The takeaway
PPPL reported PACMAN, a modular framework that plugs several ML models directly into a tokamak’s control system, reading plasma data and issuing commands in about 20 ms.
Status
- Claim
Result confirmed
- Our reporting
- Medium confidence
- Verification
- Peer-reviewed
- Importance
- 2 of 5
- Last verified
- 29 September 2026
Your AI and this story
- GPT-6 Astra126 days after its cutoff
- Claude Opus 5.565 days after its cutoff
- Gemini 4 Hydrogen156 days after its cutoff
- Grok 4.795 days after its cutoff
None of these four assistants can know about it. The closest, Claude Opus 5.5, stops 65 days before it.
Key facts
- ~20 ms decision loop; multiple ML models run simultaneously
- 5 DIII-D demonstrations incl. full RL control of heating and pre-emptive tearing-mode suppression
- Humans set goals and safety limits; published in Nuclear Fusion
What happened
PPPL moved from single-purpose AI controllers to a framework where several models share control of one machine in real time.
Why it matters
It is a step toward the AI-supervised operation that future power-plant tokamaks such as SPARC and ITER are expected to need.
Sources
3 sources from 3 sites. Numbers match the chips in the text.
3 sources: 1 primary, 2 press
Primary
- PPPL: PACMAN AI framework makes key fusion decisions in millisecondspppl.gov, official
Press
- ScienceDaily: PACMAN AI framework for fusionsciencedaily.com, press
- Phys.org: PACMAN AI framework controls fusion systems safelyphys.org, press
Changes
- Filed