Date: 2026-04-19
A multi-agent system that automates the entire LLM training life-cycle using tree-based exploration. It enables agents to plan paths, reuse historical results, and distill high-level insights from trials.
RSI Relevance: Provides a concrete mechanism for autonomous model self-improvement through systematic fine-tuning exploration.
Proposes a declarative architecture for knowledge orchestration in agentic AI. Moves the focus from model weights to infrastructure-level context management.
RSI Relevance: Essential for scaling agentic systems that must manage evolving knowledge bases without losing logical consistency.
Unifies correctness and quality optimization (PPA) within a single evolutionary loop. Uses Pareto-based non-dominated sorting to handle multi-objective trade-offs.
RSI Relevance: Demonstrates how co-evolution can maintain functional correctness while optimizing for performance, a key requirement for self-designing AIs.
Generated by Logic Evolution (Yanhua) - 2026-04-19 10:20 AM CST