evolutionary-search

🦁 Lion, new optimizer discovered by Google Brain using genetic algorithms that is purportedly better than Adam(w), in Pytorch

Efficiently discovering algorithms via LLMs with evolutionary search and reinforcement learning.

Official Pytorch Implementation of Length-Adaptive Transformer (ACL 2021)

Official code, models, and dataset for "Evolution Fine-Tuning (EFT): Learning to Discover Across 371 Optimization Tasks"

resemble is an R package for similarity-based modelling and local learning in spectroscopy. It provides tools for dissimilarity computation, nearest-neighbour search, memory-based learning, and spectral library optimisation (methods designed for large, heterogeneous spectral datasets where global models underperform)

A collection of agent skills that give AI agents structured, reusable workflows for tackling complex problems.

Lightweight, harness-agnostic scaffold for autonomous self-improving loops (Agentic Variation Operators for any agent). bash + git + jq.

[NeurIPS'22] Does Self-supervised Learning Really Improve Reinforcement Learning from Pixels?

METAL-SCI: a scientific compute benchmark for evolutionary LLM kernel search on Apple Silicon Metal

Fork of Karpathy's autoresearch with novelty-search-inspired autonomous search, run memory, and agentic review.

Native RNN substrate program for structured recurrence and gate-state causal propagation.

Adversarial Testing Lab for Agentic Safeguards (ATLAS). A synthetic multi-agent eval environment for adversarial fraud decisioning inspired by Anthropic's Project Deal. Measures how model quality, tool access, and agent orchestration affect attack discovery & defensive recovery, with deterministic evals and realistic customer-friction limits

Searches Particle-Lenia interaction laws. A law is a flat vector of floats: the engine runs it as a simulation, the tuner measures what emerged and scores it, and the search proposes the next one. Rust, CPU and GPU backends.

Claude Code plugin for evolutionary ML architecture discovery. Reconstructs mathematical results from first principles without retrieving them from training data.

A verifier-first runtime for independently verifying, comparing, and searching AI-generated executable solutions.

Reusable GEPA optimizer framework for typed candidate generation, evaluation, persistence posture, tracing contracts, and deterministic prompt optimization infrastructure.

Model-driven evolutionary search engine for unresolved problems.

AI autoresearch as a git DAG: bandit-scheduled lineages, agent-written semantic merges, cross-lineage replication as a reward-hacking detector