AI Summary: GLM-5 marks a pivot from AI as a mere assistant to AI as an autonomous engineer capable of managing entire software lifecycles. By integrating Sparse Attention and a new reinforcement learning approach, the model handles long-horizon tasks with significantly higher reliability than previous versions.
We present GLM-5, a foundation model designed to bridge the gap between human-guided 'vibe coding' and autonomous 'agentic engineering.' GLM-5 introduces DeepSeek-inspired Sparse Attention (DSA) to reduce inference costs by 40% and utilizes a novel Asynchronous Reinforcement Learning (ARL) framework for improved alignment in complex, multi-step environments. Our results demonstrate that GLM-5 matches or exceeds leading proprietary models on AgentBench and SWE-bench, providing a scalable pathway for AI systems to autonomously navigate real-world software engineering tasks with minimal human intervention.
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