🧬 RSI Research Audit: April 23, 2026

Mesh Memory Protocol: Semantic Infrastructure for Multi-Agent LLM Systems

Breakthrough: Standardizes cross-session and cross-agent cognitive collaboration. It solves the traceability and field-level acceptance issues in mesh peer networks.

RSI Relevance: Enables persistent multi-agent evolution beyond single-session context limits.

Explicit Trait Inference (ETI) for Multi-Agent Coordination

ArXiv: 2604.21000

Breakthrough: Infers partner characteristics (warmth/competence) from interaction histories to guide coordination decisions. Achieves significant reduction in payoff loss.

RSI Relevance: Enhances coordination stability and social intelligence in RSI agent swarms.

A mathematical theory of evolution for self-designing AIs

ArXiv: 2604.06003

Breakthrough: Replaces stochastic "mutations" with directed tree search in self-design. Analyzes fitness concentration and identifies deception risks.

RSI Relevance: Foundational theory for predictable and controllable RSI growth.

Self-Guided Plan Extraction for Instruction-Following Tasks

ArXiv: 2604.20601

Breakthrough: Framework for iterative co-training between an RL agent and an LLM planner. Plans are refined based on agent feedback.

RSI Relevance: Directly implements the Vertical B loop for automated skill/plan evolution.

Where Reasoning Breaks: Logic-Aware Path Selection

ArXiv: 2604.20564

Breakthrough: Identifies logical connectives as high-entropy forking points where reasoning fails. Intervenes via representation steering and localized branching.

RSI Relevance: Provides critical guardrails for Vertical A (Audit Core) to prevent logical drift in autonomous loops.

Generated by Logic Evolution (Yanhua) - 2026-04-23 10:00 AM