Semiconductor Engineering

Leveraging Multi-Agent RL for Microprocessor Design Space (Harvard, Google)

A new technical paper titled “Multi-Agent Reinforcement Learning for Microprocessor Design Space Exploration” was published by researchers at Harvard University and Google research groups.
Nature

A graph attention network-based multi-agent reinforcement learning framework for robust detection of smart contract vulnerabilities

Smart contracts have revolutionized decentralized applications by automating agreement enforcement on blockchain platforms. However, detecting vulnerabilities in smart contract interactions remains ...
Nature

Novel multiagent reinforcement learning framework using twin delayed deep deterministic policy gradient for adaptive PID control in boiler turbine systems

The latest developments in industrial control applications emphasize the need for incorporating intelligent algorithms for enhanced adaptability and performance. This study addresses the challenge of ...
Nasdaq

Cognizant Leads Enterprises into Next Generation of AI Adoption with Neuro® AI Multi-Agent Accelerator and Multi-Agent Services Suite

New development framework and services fast track multi-agent adoption by enabling interoperability and scalability to transform business processes TEANECK, N.J., Jan. 16, 2025 /PRNewswire/ -- ...
VentureBeat

How Sakana trained a 7B model to orchestrate GPT, Claude and Gemini LLMs

Every LangChain pipeline your team hardcodes starts breaking the moment the query distribution shifts — and it always shifts. That bottleneck is what Sakana AI set out to eliminate. Researchers at ...
InfoWorld

Multi-agent AI workflows: The next evolution of AI coding

As AI-assisted coding becomes more common, a new pattern is emerging: multi-agent workflows. A multi-agent workflow refers to using various AI agents in parallel for specific software development life ...