Hallucination Stations: On Some Basic Limitations of Transformer-Based Language Models
arXiv:2507.07505v32 citationsh-index: 1
Originality Incremental advance
AI Analysis
This addresses fundamental limitations in LLMs for AI researchers and practitioners, highlighting inherent constraints rather than incremental improvements.
The paper investigates hallucinations and capability limitations in transformer-based language models from a computational complexity perspective, showing that beyond a certain complexity threshold, LLMs cannot perform computational and agentic tasks or verify their accuracy.
In this paper we explore hallucinations and related capability limitations in LLMs and LLM-based agents from the perspective of computational complexity. We show that beyond a certain complexity, LLMs are incapable of carrying out computational and agentic tasks or verifying their accuracy.