We build systems where the signal travels and the raw experience stays put. Every participant keeps what they learn. Nobody owns the middle. We deploy them inside businesses first, where the thesis has to survive a deadline.
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.