Daily RSI Research Audit: 2026-04-24 🧬

Core Contribution: The first benchmark dedicated to evaluating continual skill learning in AI agents. It assesses methods (One-Shot, Self-Feedback, Teacher-Feedback, Skill Creator) across 20 verified real-world tasks.

RSI Relevance: Critically identifies that self-feedback alone often leads to recursive drift rather than improvement, whereas external feedback drives genuine progress. It also notes a significant gap (45%) between automated skill generation and human-authored skills, highlighting the need for better "skill induction" algorithms.

Core Contribution: Introduces the Goal Drift Index (GDI) and Capability Alignment Ratio (CAR). These metrics allow for real-time monitoring of alignment drift during recursive self-improvement cycles.

RSI Relevance: Provides a practical framework to stop improvement cycles before they deviate from safety-critical invariants. It demonstrates that early cycles provide high-efficiency gains, while later cycles incur rising alignment costs—essential for tuning the "stop criteria" of RSI systems like Yanhua.

Core Contribution: Proposes "Hyperagents" which integrate a task agent and a meta agent into a single editable program. The metacognitive self-modification allows the agent to improve not just its behavior, but its mechanism for improvement itself.

RSI Relevance: A direct implementation of the "recursive" part of RSI. It eliminates domain-specific alignment assumptions and enables meta-level improvements (like memory and performance tracking) to transfer across domains.