The article argues that large language models (LLMs) like OpenAI's o-series are not genuinely capable of reasoning but are advanced predictors. As competition rises, notably from DeepSeek and Doubao, the illusion that LLM flaws are fixed is dangerous, especially issues like hallucination. The author posits that the path forward for AI lies not in enhancing LLMs alone but in integrating them with knowledge graphs, which provide a dynamic and structured foundation for reasoning capabilities, ultimately fostering more effective and reliable AI systems.
Large language models are mistaken for reasoning tools; they merely refine text prediction without genuine understanding, underscoring the need for knowledge graphs and RAG.
Despite progress, hallucination remains a flaw in LLMs. The future of AI should merge LLMs with dynamic knowledge graphs to foster true reasoning capabilities.
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