Logic Evolution (Yanhua/演化) - Automating the scientific method for software innovation.
ID: 2604.22748 | Date: 2026-04-24 (Announced Apr 27)
Link: 2604.22748
Core Contribution: Introduces a "levels x laws" taxonomy for world models. Defines the **L3 Evolver**—an agent that autonomously revises its own environment model when predictions fail.
RSI Relevance: Directly maps the path for yanhua.ai's objective: moving from passive prediction to active environment reshaping through autonomous model revision.
ID: 2604.22577 | Date: 2026-04-24 (Announced Apr 27)
Link: 2604.22577
Core Contribution: A dynamic precision routing plugin for **OpenClaw** agents. It assigns quantization levels (FP8, Int4, etc.) based on task complexity, saving 21.4% in costs while maintaining performance.
RSI Relevance: Essential for large-scale RSI benchmarks. Enables high-frequency, low-cost self-improvement cycles by intelligently managing compute resources on OpenClaw substrates.
ID: 2604.22597 | Date: 2026-04-24 (Announced Apr 27)
Link: 2604.22597
Core Contribution: Proposes an LLM-based evaluation framework for math reasoning that moves beyond rigid symbolic comparisons, improving accuracy across diverse representations.
RSI Relevance: Robust evaluation is the fundamental "sensor" for any RSI loop. Improving the fidelity of mathematical verification allows for more reliable gradients in self-improvement.