Researchers have introduced a novel method for evaluating enterprise AI agents by replaying temporally accurate snapshots of evolving enterprise data, enabling more realistic and reproducible assessments of agent performance.
Researchers have introduced a novel evaluation system that simulates real-world, time-sensitive enterprise environments to more accurately assess AI agents’ performance across evolving data states.
At Google I/O 2026, Google unveiled Gemini for Science, powered by two Nature‑published systems—Empirical Research Assistance (ERA) and Co‑Scientist—that together enable agentic AI to generate, test, and refine scientific hypotheses at scale.
Researchers at Tufts University have developed a neuro‑symbolic AI system that achieves up to 100‑fold reductions in energy consumption during training and inference, while significantly outperforming conventional neural models on reasoning tasks.