UF researcher describes ATLAS system aimed at reducing AI-agent task drift
The University of Florida reports that Sumit Jha and collaborators developed a research system designed to detect and redirect task and role drift inside open-weight AI models.
What the record says
A University of Florida report published October 29 describes ATLAS, a system that researchers designed to help AI agents remain focused on their assigned tasks during extended collaboration.
UF computer science professor Sumit Jha and collaborators describe the approach in a paper titled “Drift No More: Spectral Locking for Long-Horizon Open-Weight Multi-agent AI.” The university says the system works inside a language model as it generates responses, aiming to reinforce its task, assigned role and current shared information.
The report addresses a research problem in increasingly autonomous AI systems. It does not establish that the approach guarantees reliable behavior in every model or production setting.
Source details and supporting locations.
Practical next step
Read UF’s report for the researchers’ description of the system and the paper.
Limits and what is not established
This record summarizes an institutional research announcement. Performance and safety claims are attributed to the researchers and have not been independently validated by Iliyn.
Sources checked October 31, 2026. Later notices may change instructions or arrangements.
Original sources
University of Florida: original information