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AI Large-Model Wave and Transformation Guide
AI Large-Model Wave and Transformation Guide
Mar 28, 2026 · Artificial Intelligence

What Large‑Model Training Actually Optimizes: Parameters, Attention, and Knowledge Explained

This article breaks down the core of large‑model training by showing that training optimizes neural‑network parameters, that attention is a mechanism realized by those parameters, and that knowledge is encoded implicitly within the weight matrices, providing a clear hierarchy for interview or presentation use.

AI InterviewAttention Mechanismdeep learning
0 likes · 6 min read
What Large‑Model Training Actually Optimizes: Parameters, Attention, and Knowledge Explained
AntTech
AntTech
Jan 16, 2026 · Databases

Can Multi‑Agent Collaboration Automatically Tune Database Parameters with High Efficiency?

The paper presents CMA+DB, a hierarchical multi‑agent framework that automatically tunes database parameters across diverse workloads by combining classification‑based collaboration, layered training, and joint action selection, achieving superior performance, faster convergence, and strong generalization compared with existing tuning methods.

CMA+DBDatabase Tuningmulti-agent reinforcement learning
0 likes · 9 min read
Can Multi‑Agent Collaboration Automatically Tune Database Parameters with High Efficiency?