AutoResearch 2.0 and Self-Evolution Systems | Dual-Agent and Reinforcement Learning Architecture

00:00 Overview and introduction of AutoResearch 2.0 and self-evolving AI systems. 00:33 Explaining the 'prompt complexity wall', a limitation of optimizing prompts beyond human intuition. 01:21 Dual-agent asymmetries addressing the cooperation between the giant cloud model and the small local model (...)... 04:26 The cognitive separation principle that applies principles of physics to separate task execution from meta-inference (Cogn... 07:08 Two steps divided into training time dynamics (Phase 1) and test time calculation (Phase 2)... 07:50 A training architecture where local models analyze failures and rewrite prompts with reinforcement learning... 08:33 Algorithmic evolution trees and navigation methods for finding the optimal prompt in the inference process. 09:39 Real Failure Diagnostics and Linguistic State Extraction (LSE) examined through SQL query creation cases... 11:09 Fine model behavior using textual perturbation theory... 12:44 Data metrics and experimental performance analysis with improved performance over traditional benchmarks. 14:01 Establishment of the Artificial Scientific Methodology...