Qwen-AgentWorld are language world models that simulate multi-domain agent environments and boost general agent capabilities via decoupled RL simulation and unified foundation model training.
Webatlas: An llm agent with experience- driven memory and action simulation.arXiv preprint arXiv:2510.22732,
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GiG uses a Graph-in-Graph architecture with GNN-encoded states, experience memory retrieval, and bounded symbolic lookahead to improve LLM planning on embodied benchmarks with gains up to 37%.
The paper develops a unified framework that organizes computer-use agent reliability around perception-decision-execution layers and creation-deployment-operation-maintenance stages to map security and alignment interventions.
citing papers explorer
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Qwen-AgentWorld: Language World Models for General Agents
Qwen-AgentWorld are language world models that simulate multi-domain agent environments and boost general agent capabilities via decoupled RL simulation and unified foundation model training.
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Embodied Task Planning via Graph-Informed Action Generation with Large Language Models
GiG uses a Graph-in-Graph architecture with GNN-encoded states, experience memory retrieval, and bounded symbolic lookahead to improve LLM planning on embodied benchmarks with gains up to 37%.
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Securing Computer-Use Agents: A Unified Architecture-Lifecycle Framework for Deployment-Grounded Reliability
The paper develops a unified framework that organizes computer-use agent reliability around perception-decision-execution layers and creation-deployment-operation-maintenance stages to map security and alignment interventions.