Enhancing LLM collaboration for smarter, more efficient solutions
“Co-LLM” algorithm helps a general-purpose AI model collaborate with an expert large language model by combining the best parts of both answers, leading to more factual responses.
“Co-LLM” algorithm helps a general-purpose AI model collaborate with an expert large language model by combining the best parts of both answers, leading to more factual responses.
New STUDIO.nano supports artistic research and encounters within MIT.nano’s facilities.
“ScribblePrompt” is an interactive AI framework that can efficiently highlight anatomical structures across different medical scans, assisting medical workers to delineate regions of interest and abnormalities.
Computer scientist who specializes in database management systems joins the leadership of the Department of Electrical Engineering and Computer Science.
Mechatronics combines electrical and mechanical engineering, but above all else it’s about design.
A new algorithm solves complicated partial differential equations by breaking them down into simpler problems, potentially guiding computer graphics and geometry processing.
Building on a landmark algorithm, researchers propose a way to make a smaller and more noise-tolerant quantum factoring circuit for cryptography.
Amulya Aluru ’23, MEng ’24 and the MIT Spokes have spent the summer spreading science, over 3,000 miles on two wheels.
With extensive international outreach experience as a faculty member and program leader, Boning brings a spirit of curiosity and collaboration to his new role.
An AI team coordinator aligns agents’ beliefs about how to achieve a task, intervening when necessary to potentially help with tasks in search and rescue, hospitals, and video games.
AI agents could soon become indistinguishable from humans online. Could “personhood credentials” protect people against digital imposters?
In controlled experiments, MIT CSAIL researchers discover simulations of reality developing deep within LLMs, indicating an understanding of language beyond simple mimicry.
The approach can detect anomalies in data recorded over time, without the need for any training.
An MIT-led group shows how to achieve precise control over the properties of Weyl semimetals and other exotic substances.
SimPLE learns to pick, regrasp, and place objects using the objects’ computer-aided design model.