Model Releases
Banger paper from Google DeepMind on long-horizon multi-agent coding. (bookmark it) They report that, to the best of their knowledge, this i…
Banger paper from Google DeepMind on long-horizon multi-agent coding. (bookmark it) They report that, to the best of their knowledge, this is the first end-to-end autonomous completion of Minecraft by
Banger paper from Google DeepMind on long-horizon multi-agent coding. (bookmark it) They report that, to the best of their knowledge, this is the first end-to-end autonomous completion of Minecraft by AI agents. They report that four Gemini 4 Argon agents beat Minecraft end-to-end. They defeated the Ender Dragon in 27 hours and 14 minutes with no human intervention. Each agent controlled one player through the standard Mineflayer library. No one assigned roles, wrote game logic, or scouted a seed in advance. The agents planned, wrote, and debugged every program themselves, and coordinated through in-game chat and a shared memory. Over the run, they wrote 2,783 programs and died 605 times. They split the work, shared what they learned, and recovered each other's items after deaths. If you build agents for long software or enterprise tasks, this is a useful case study. The team had to keep one plan consistent across thousands of dependent steps for more than a day. https://arxiv.org/abs/2610.22319 Chat with Paper: https://academy.dair.ai/papers/a-team-of-gemini-4-argon-agents-beats-minecraft-2610.22319
Related
- Banger paper from Google on agent harnesses for long-horizon tasks. (bookmark it) Google Research built a many-agent harness for long mathem…
- Banger paper from Google DeepMind on co-evolving an agent's harness and its weights. (bookmark it) If you already log your harness changes a…
- Neat work on long-horizon agents. Splitting a hard task across agents is typically how standard multi-agent work. The usual design lets them…
Source: DAIR.AI (X) | 2026-10-16