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MLLMs struggle with real-world industrial measurements, achieving only 25.7% accuracy in a new benchmark that simulates authentic operational challenges.
Rather than treating agent trajectories as dead post-training demonstrations, researchers can now resurrect thousands of fully executable, verifiable terminal environments directly from tool-execution logs.
Qwen-AgentWorld achieves unprecedented simulation fidelity, outperforming existing models and enabling scalable agentic reinforcement learning across diverse real-world environments.
Achieve real-time cattle mounting pose estimation in complex environments with FSMC-Pose, a framework that outperforms existing methods while drastically reducing computational costs.
LLM benchmark accuracy jumps 10% when evaluated on a cleaned-up version of Humanity's Last Exam, highlighting the significant impact of dataset noise on performance metrics.
