TL;DR
This survey synthesizes recent developments in self-improving agent architectures from the 2020 to 2025 period, focusing on how generative AI and cognitive architectures can be integrated for practical applications.
A unified, taxonomy-driven, and deployment-oriented survey of agentic AI systems, synthesizing recent advances through a modular reference architecture and a four-dimensional taxonomy that characterizes agents along the axes of autonomy, tool use, collaboration, and safety–governance is presented.
A consolidated analytical framework is proposed that maps common structural elements and trade-offs across reviewed systems, and outlines a research agenda directed toward formalised agent architectures, memory consistency guarantees, verified planning algorithms, standardised reliability metrics, and benchmark framewo...
Language agents are expected to solve increasingly complex tasks, creating a growing need for continual improvement. One promising approach is to evolve the agent harness, the software that governs tool use, memory management, and task execution, while keeping the underlying language model fixed. Recent methods automat...
A comprehensive framework for the design and evaluation of autonomous systems, based on a generic agent architecture that characterizes their behavior as the composition of cognitive functions organized around a long-term memory containing the agent's evolving knowledge.
The MIT professors collaborated to identify the first AI-discovered antibiotic, a scientific breakthrough that is widely recognized as a milestone in AI-driven drug discovery.
Five MIT faculty members and two additional alumni are honored with fellowships to advance research on beneficial AI.
MIT professors Adam Berinsky and Chara Podimata discuss a new project to track how AI chatbots are responding to questions about the 2026 U.S. midterm elections.
Google DeepMind and UK government work together to reduce planning assessment times
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