Researchers have identified a significant limitation in today’s artificial intelligence, including models from Nvidia, Ring, and OpenAI, in that they can only “vibe code” but fail to deliver real engineering solutions. This is attributed to their inability to efficiently process long-context information and lack of agentic reinforcement learning (RL) capabilities.
The concept of “vibe coding” refers to the ability of AI models to generate code that resembles the style and structure of a given codebase, but lacks the underlying logic and functionality. This limitation is a major obstacle to the widespread adoption of AI in engineering and other fields that require complex problem-solving and critical thinking. To address this gap, researchers are exploring the potential of long-context efficiency and agentic RL, which enables AI models to learn from their environment and make decisions based on their goals and objectives.
The development of GLM-5, a new AI model that incorporates long-context efficiency and agentic RL, is expected to close the gap between “vibe coding” and real engineering. With its advanced capabilities, GLM-5 has the potential to revolutionize the field of AI and enable the creation of more sophisticated and functional AI models. As researchers continue to refine and improve GLM-5, we can expect to see significant advancements in the field of AI and its applications in various industries.
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