A new mathematical proof by Stanford’s Varin Sikka and Vian AI’s Vishal Sikka raises concerns about the reliability of large language models (LLMs) in executing complex computational tasks. Their paper, “Hallucination Stations,” published on arXiv, argues that LLMs, limited by self-attention mechanisms, cannot effectively handle complexities exceeding O(N² · d). Theorem 1 indicates that for tasks of complexity O(n^k) or higher, LLMs will inevitably hallucinate. Examples include combinatorial problems and verification tasks that exceed quadratic limits. The research, highlighted by Gizmodo and WIRED, questions the potential of agentic AI for artificial general intelligence. While some companies explore verification layers to enhance reliability, challenges persist, as pure LLMs exhibit inherent limitations. The researchers advocate for hybrid systems to bypass these constraints but warn that verification often introduces additional complexities. This proof urges industry players to recalibrate expectations and seek engineered solutions rather than infinite scaling for AGI.
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