Companion materials for our survey of how execution experience becomes persistent harness state under a fixed base model. The survey covers update mechanisms, evolutionary search, continual PAC learning, evaluation, applications, and societal impact.
Token-scale scientific discovery engine — autonomous hypothesis generation, experiment execution, and knowledge graph synthesis
skill —— 一个让你的Skill无限进化的系统：评估→改进→测试→保留或回滚 | Autoresearch-inspired autonomous skill optimization for Claude Code. Evaluate, improve, test, keep or revert.
SkillOpt is a text-space optimizer that trains reusable natural-language skills for frozen LLM agents through trajectory-driven edits, validation-gated updates, and deployable bestskill.md artifacts.
Recursive Self-Improvement (RSI) refers to processes in which AI systems improve their own capabilities and can also improve the mechanisms that generate subsequent improvements.
Hermes Agent Self-Evolution uses DSPy + GEPA (Genetic-Pareto Prompt Evolution) to automatically evolve and optimize Hermes Agent's skills, tool descriptions, system prompts, and code — producing measurably better versions through reflective evolutionary search.
27454) for Hermes Agent — self-evolving agent skills via a persistent knowledge wiki. Faithful Algorithm 1 implementation with real agent runs, isolated skill gating, and a documented live run log.
VibeGame: Vibe Your Dream Game -- An open-source self-evolving multi-agent framework with an AI-Native game engine that turns your natural language into a fully playable 2D web game and edit it any way you want, built on Claude Code & Codex.
EvoScientist aims to harness vibe research by enabling self-evolving AI scientists that autonomously explore, generate insights, and iteratively improve.
Zetta is an efficient closed-loop embodied harness for self-evolving physical intelligence. It evolves code-based runtime critics and recovery skills online while keeping the base policy frozen. Project Page:
审查报告会显示在对应的助手回复下方。质量审查每个完成的父回复增加一次模型调用和相应延迟，但不会自动执行项目的 test、lint 或 build 命令。
Plug in any agent harness × any model, let it rewrite its own harness over many context-fresh episodes, and measure how the harness changes — under a goal, many goals, or no goal at all.
Motus2: A Self-Evolving General World Model for Dexterous Manipulation