By rapidly generating a smooth path plan that cuts travel time and avoids obstacles, the open-source “MIGHTY” system could streamline disaster recovery and parcel delivery.

Assistant Professor Gabriele Farina mines the foundations of decision-making in complex multi-agent scenarios.

Building on a long-standing MIT–IBM collaboration, the new lab will chart the convergence of AI, algorithms, and quantum computing.

The “EnergAIzer” method generates reliable results in seconds, enabling data center operators to efficiently allocate resources and reduce wasted energy.

A new training method improves the reliability of AI confidence estimates without sacrificing performance, addressing a root cause of hallucination in reasoning models.

Researchers use control theory to shed unnecessary complexity from AI models during training, cutting compute costs without sacrificing performance.

Researchers developed a system that intelligently balances workloads to improve the efficiency of flash storage hardware in a data center.