Algorithms
Algorithms

Guided learning lets “untrainable” neural networks realize their potential

CSAIL researchers find even “untrainable” neural nets can learn effectively when guided by another network’s built-in biases using their guidance method.

A new way to increase the capabilities of large language models

MIT-IBM Watson AI Lab researchers developed an expressive architecture that provides better state tracking and sequential reasoning in LLMs over long texts.

Working to eliminate barriers to adopting nuclear energy

Nuclear waste continues to be a bottleneck in the widespread use of nuclear energy, so doctoral student Dauren Sarsenbayev is developing models to address the problem.

Enabling small language models to solve complex reasoning tasks

The “self-steering” DisCIPL system directs small models to work together on tasks with constraints, like itinerary planning and budgeting.

MIT affiliates named 2025 Schmidt Sciences AI2050 Fellows

Postdoc Zongyi Li, Associate Professor Tess Smidt, and seven additional alumni will be supported in the development of AI against difficult problems.

A smarter way for large language models to think about hard problems

This new technique enables LLMs to dynamically adjust the amount of computation they use for reasoning, based on the difficulty of the question.

Charting the future of AI, from safer answers to faster thinking

MIT PhD students who interned with the MIT-IBM Watson AI Lab Summer Program are pushing AI tools to be more flexible, efficient, and grounded in truth.

Teaching robots to map large environments

A new approach developed at MIT could help a search-and-rescue robot navigate an unpredictable environment by rapidly generating an accurate map of its surroundings.

3 Questions: How AI is helping us monitor and support vulnerable ecosystems

MIT PhD student and CSAIL researcher Justin Kay describes his work combining AI and computer vision systems to monitor the ecosystems that support our planet.

A faster problem-solving tool that guarantees feasibility

The FSNet system, developed at MIT, could help power grid operators rapidly find feasible solutions for optimizing the flow of electricity.