5arXiv cs.CL (Computation and Language)·
GS-Agent: Multi-agent framework for generating physically realistic 4D worlds from natural language
GS-Agent is an end-to-end multi-agent system that generates dynamic, physically plausible 4D worlds from natural language descriptions by integrating physics engines in the loop. The framework decomposes world creation into entity management (3D asset curation, material tuning, placement, motion control) and rendering configuration, with specialized agents collaborating via code and multimodal feedback. Results demonstrate generation of scenes with rich interactions among liquids, deformable objects, and rigid bodies, with cinematic camera and lighting control. The work positions itself as a foundation for physical AI and automated creative content generation.
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Agent-S is an open-source Python framework by Simular AI designed to enable AI agents to interact with computers in a human-like manner. The project has accumulated 11,388 GitHub stars with modest daily growth of 29 stars. It represents an entry in the growing space of computer-use agent frameworks targeting GUI and desktop automation tasks.
Qwen-AgentWorld: Language world models for general agent simulation and planning
Alibaba's Qwen team introduces Qwen-AgentWorld, a pair of language world models (35B-A3B and 397B-A17B) trained to simulate agentic environments across 7 domains using over 10M interaction trajectories. The models are trained via a three-stage pipeline (CPT, SFT, RL) and evaluated on AgentWorldBench, a new benchmark constructed from 5 frontier models across 9 established benchmarks. Beyond simulation, the work demonstrates two downstream use cases: using the world model as a decoupled RL training environment and as a warm-up for agent foundation models, both yielding gains over baselines.
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Genie 3: A new frontier for world models
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