AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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Can AI agents carry out research on open scientific problems? We introduce Into the Unknown, a benchmark that provides an agent with a science paper along with a research direction to pursue and tasks it to write a follow-up paper. We work with scientists at US Department of Energy (DOE) national laboratories, who each...
EurekaBench is introduced, a cross-domain benchmark that tests AI agents'ability to conduct long-horizon experiments and discover mechanisms that explain observations, and shows that current AI agents often overly fixate on predictive accuracy optimization, surpassing human scientists, while falling substantially short...
This work proposes test-time adaptation through human-agent interaction (TAHI), which integrates these signals into agent context and weights, and crystallizes each user's training and evaluation criteria via an evolving rubric module.
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