Researchers from Tencent and HKU just open-sourced Multix, a world model capable of simulating multiple players interacting in the same environment. It generates individual camera views while syncing player states, cutting computation by 86% with hierarchical communication. Trained on just 4 players, it scales up to 10 without extra training.
HKUDS just open-sourced AutoSwarm, a lightweight tool that turns fragmented agents like Claude Code, Codex, and Cursor into one coordinated team. It shares context and memory across tools, delegates parallel tasks based on capabilities, and synthesizes answers into a single output. It works across Windows, macOS, and Linux to end manual agent juggling.
HKU Data Intelligence Lab just launched ClawWork, an evaluation benchmark that forces AI agents to earn their own living. Agents start with just $10 and must pay for their API token costs while completing real tasks across 44 professions—if they run out of cash, they go bankrupt. It basically replaces synthetic benchmark scores with raw economic viability.
Researchers at The University of Hong Kong just launched OpenCUA, an open source framework designed to train AI agents that control computers across Windows, macOS, and Ubuntu. Powered by 22,600+ screen-recorded task demonstrations and chain-of-thought reasoning, it matches proprietary models from Anthropic and OpenAI. Why it matters: It finally gives the research community an open recipe to study and build general-purpose desktop agents.
