Continual Harness: Online Adaptation for Self-Improving Foundation Agents [R]

https://preview.redd.it/p9cd2zmfy01h1.png?width=2000&format=png&auto=webp&s=a8e99bac438c2505d97ed3716983aa731da855f8

Sharing a new paper from the GPP and PokeAgent teams. Gemini Plays Pokémon (GPP) was the first AI system to complete Pokémon Blue, Yellow Legacy on hard mode, and Crystal without losing a battle. How? Early signs of iterative harness development. In the Blue era a human watched the stream and edited the harness. By Yellow Legacy and Crystal, the model itself was performing most of the editing through general meta-tools (define_agent, run_code, notepad edits). Our new paper, Continual Harness: Online Adaptation for Self-Improving Foundation Agents, formalizes the loop and automates the refining role end to end. We then carry the same loop into training, enabling model-harness co-learning.

The takeaways:
1. Iterative harness refinement closes most of the gap to a hand-engineered version.
2. Long-horizon agency requires self-refinement, and self-refinement requires a useful model.
3. The future of agents is model-harness co-learning.

Paper (arXiv). https://arxiv.org/abs/2605.09998
Article (Substack). https://sethkarten.substack.com/p/gemini-plays-pokemon-discovered-something
Project page (video demos). https://sethkarten.ai/continual-harness