We build systems where the signal travels and the raw experience stays put. Every participant keeps what they learn. Nobody owns the middle.
Aligned with cutting edge
global research
- Zhang et al. · 2026Darwin Gödel Machine: Open-Ended Evolution of Self-Improving AgentsUses empirical evaluation and an archive of discoveries for open-ended agent evolution.
- Hu, Lu & Clune · 2025Automated Design of Agentic SystemsTreats agents as code and searches over prompts, tools, workflows, and their combinations.
- Akiba et al. · 2025Evolutionary Optimization of Model Merging RecipesDemonstrates evolutionary discovery across both parameter and data-flow composition spaces.
- Romera-Paredes et al. · 2024Mathematical Discoveries from Program Search with Large Language ModelsPairs language-model variation with evaluators to evolve interpretable programs.
- Wang et al. · 2024Mixture-of-Agents Enhances Large Language Model CapabilitiesShows that heterogeneous model outputs can be coordinated into stronger runtime performance.
- Wang et al. · 2024Voyager: An Open-Ended Embodied Agent with Large Language ModelsShows cumulative agent learning through a reusable library of executable skill artifacts.
- Packer et al. · 2023MemGPT: Towards LLMs as Operating SystemsTreats agent memory as managed tiers past the context window.
- Hegedűs, Danner & Jelasity · 2021Decentralized Learning WorksDemonstrates viable peer-to-peer learning without a central aggregation server.