A researcher trained a world model based on Yann LeCun's JEPA architecture to play Pokémon Red, specifically to select a starter Pokémon in Professor Oak's lab. World models learn to predict future game states from current observations and actions, enabling AI agents to plan sequences of button presses to achieve objectives in the game.
JEV-Star is a system that combines language-model planning with StarCraft II control, enabling both macro-level game strategy and micro-level unit management. The framework integrates the Astra planner with JEV action selection across multiple game configurations, achieving wins on hard micromanagement tasks and full-game macro control with realtime performance.
NetHackers is an open challenge to build the first program to win NetHack 3.6.6, a 37-year-old game requiring descent through procedurally generated levels and escape under permadeath conditions. No autonomous program has ever achieved ascension on the modern version, though recent reinforcement learning agents have doubled previous progression records. The project invites researchers to use hand-coding, AI agents, and iterative improvement to tackle this unsolved frontier in AI generalization.