The rise of artificial intelligence resurfaces a question older than the abacus: If we have a tool to do it for us, why learn to do it ourselves? The answer, argues MIT electrical engineering and computer science (EECS) Professor Devavrat…
In this tutorial, we implement the Agent2Agent collaborative framework built atop Google’s Gemini models. The guide walks through the creation of specialized AI personas, ranging from data scientists and product strategists to risk analysts and creative innovators. It demonstrates how…
Mistral has introduced its Agents API, a framework designed to facilitate the development of AI agents capable of executing a variety of tasks including running Python code, generating images, and performing retrieval-augmented generation (RAG). This API aims to provide a…
Multi-modal large language models (MLLMs) have shown great progress as versatile AI assistants capable of handling diverse visual tasks. However, their deployment as isolated digital entities limits their potential impact. The growing demand to integrate MLLMs into real-world applications like…
On Dec. 21, 2022, just as peak holiday season travel was getting underway, Southwest Airlines went through a cascading series of failures in their scheduling, initially triggered by severe winter weather in the Denver area. But the problems spread through…
MIT today launched its Initiative for New Manufacturing (INM), an Institute-wide effort to reinfuse U.S. industrial production with leading-edge technologies, bolster crucial U.S. economic sectors, and ignite job creation. The initiative will encompass advanced research, innovative education programs, and partnership…
While large reasoning models (LRMs) have shown impressive capabilities in short-context reasoning through reinforcement learning (RL), these gains do not generalize well to long-context scenarios. Applications such as multi-document QA, research synthesis, and legal or financial analysis require models to…
Neural networks have long been powerful tools for handling complex data-driven tasks. Still, they often struggle to make discrete decisions under strict constraints, like routing vehicles or scheduling jobs. These discrete decision problems, commonly found in operations research, are computationally…
Chaotic systems, such as fluid dynamics or brain activity, are highly sensitive to initial conditions, making long-term predictions difficult. Even minor errors in modeling these systems can rapidly grow, which limits the effectiveness of many scientific machine learning (SciML) approaches.…
Reinforcement learning (RL) has emerged as a fundamental approach in LLM post-training, utilizing supervision signals from human feedback (RLHF) or verifiable rewards (RLVR). While RLVR shows promise in mathematical reasoning, it faces significant constraints due to dependence on training queries…
