Minecraft

Headlines · 3

1. 3d
   Four Gemini 4 Argon agents beat Minecraft in 27 hours

   Google DeepMind highlighted an arXiv paper (arXiv:2610.22319, submitted Oct 15, by Maxence Faldor, Satinder Singh and Roberta Raileanu) that placed four Gemini 4 Argon agents in a sandboxed Minecraft survival world with one goal: defeat the Ender Dragon. According to the paper, each agent controlled a player through the open-source Mineflayer library and coordinated via in-game chat and shared memory, planning, writing and debugging every program themselves; the team wrote 2,783 programs and killed the dragon in 27 hours and 14 minutes, dying 605 times, while researchers watched on a live dashboard without intervening and no roles or game logic were assigned by humans. The authors say this is the first end-to-end autonomous completion of Minecraft by AI agents using only the standard Mineflayer library, without pre-scouted seeds or human-written game-specific tools, in a randomly generated Easy-difficulty world, and argue the same long-horizon coding and multi-agent coordination is needed for real-world software engineering and enterprise tasks.

   Google DeepMind (X) · 🔥 8
2. 5d
   Vibecoder uses Claude Opus 5.5 to build Minecraft inside GTA V on iPhone

   A modder going by 0xProbability launched Minecraft inside an iPhone-native port of GTA V, showing the Minecraft HUD and loading in Steve; according to their account, Claude Opus 5.5 handled everything from writing the code to testing the game, with the modder saying they did not touch the phone while Claude flew the Ender Dragon around Los Santos. The GTA V iOS port itself was not their work — it comes from C22, a student at EPFL in Switzerland, who adapted a browser-playable version into a native iOS app that supports iOS 15 or later and macOS 14 or later on Apple silicon, runs at 50–60 FPS on an iPhone 17 using about 1.1GB of RAM, and even supports multiplayer. The Minecraft mod was first tried on the browser version and includes block-building mechanics, not just Minecraft mobs and characters.

   Tom’s Hardware · 🔥 0
3. Oct 5
   Attacca: goal-directed control for long-horizon embodied agents

   An arXiv paper introduces Attacca, a method for training visual goal-conditioned policies for long-horizon embodied agents. It uses context-decoupled goal sampling to pair each demonstration with a class-compatible masked goal image from another world, adds a target-mask prediction head for dense current-view grounding, and conditions the policy on Search, Approach and Interact phases. On short- and long-horizon Minecraft tasks, the authors report 39.0–47.5% clean success, a 1.7–2.4x gain over the strongest baseline, and 54%, 30% and 28% completion on long-horizon tasks, up to a 7x improvement.

   Hugging Face · Papers · 🔥 0

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