A curated list of autonomous improvement loops, research agents, and autoresearch-style systems inspired by Karpathy's autoresearch.

Autonomous self-improving multi-swarm AI platform with CRDT state, memory, simulation, execution governance, and verified repair loops.

Self-improving multi-level optimization & scientific discovery with meta-meta-learning

A framework for building AI-driven systems that execute work, observe outcomes, learn from failures, and continuously improve their code, processes, and operating procedures.

Compare AdaL and Claude Code on Autoresearch benchmarks to find better hyperparameters, run more experiments, and converge faster

Executable governance for consequences across plural horizons in self-improving systems

Automate crypto trading strategies with AI agents using evolution, high-fidelity backtesting, and live execution on DEX and CEX platforms.

Autonomous, evidence-gated improvement loop for any codebase — Praxis truth kernel + Janus adversarial council (Explorer/Red Team). Explore freely. Prove ruthlessly.

Explore curated AutoResearch use cases with optimization traces and open source implementations for each entry

A recursive research system that improves how AI agents are organized across fresh reasoning problems.

Pure RL Agents: I implemented Q-Learning agents that learn through Self-Play. They play against each other to get smarter without human help! Symmetry Optimization: To make them "genius" faster, I added logic so they understand that a board mirrored left-to-right is the same situation. This cuts the learning time in half!

A lightweight artificial life that learns to replace parent-model reasoning with local memory, skills, and routines.