mlsanityData to 14 Oct 2026

Hallucination Stations: On Some Basic Limitations of Transformer-Based Language Models

arXiv 2507.07505 ↗First posted 10 Jul 2025cs.CL cs.AIcited by 2

On the map: LLM agents and tool use → Medical and scientific domain agents

Abstract (arXiv)

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.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Placed in Medical and scientific domain agents, one of 16 directions in LLM agents and tool use. Builds on 5 of its 21 references that are in the map. First posted on arXiv 10 Jul 2025.

Builds on

5 of 21 references.

References · 5

  1. 2017Attention Is All You Needcited by 60,361
  2. 2024Hallucination is Inevitable: An Innate Limitation of Large Language Modelscited by 332
  3. 2022On The Computational Complexity of Self-Attentioncited by 139
  4. 2025Gold-medalist Performance in Solving Olympiad Geometry with AlphaGeometry2cited by 97
  5. 2025A Comprehensive Survey of Agents for Computer Use: Foundations, Challenges, and Future Directionscited by 34

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 1; 2026: 1.

2 papers.

  1. 2025Secure and Efficient Access Control for Computer-Use Agents via Context Spacecited by 11
  2. 2026Working Around the Compute Ceiling: Byte-Exact Memory in Galahad Makes LLM Reading a One-Time Cost LLM Reading a One-Time Costcited by 1

Similar, not cited

Close in wording, no citation either way

  1. 2025LLM-based Agents Suffer from Hallucinations: A Survey of Taxonomy, Methods, and Directions
  2. 2025Mitigating Hallucination in Large Language Models (LLMs): An Application-Oriented Survey on RAG, Reasoning, and Agentic Systems
  3. 2025From "Hallucination" to "Suture": Insights from Language Philosophy to Enhance Large Language Models
  4. 2025HEAL: An Empirical Study on Hallucinations in Embodied Agents Driven by Large Language Models
  5. 2026Hallucination Cascade: Analyzing Error Propagation in Multi-Agent LLM Systems
  6. 2025MIRAGE-Bench: LLM Agent is Hallucinating and Where to Find Them
  7. 2024Enhancing Multi-Agent Consensus through Third-Party LLM Integration: Analyzing Uncertainty and Mitigating Hallucinations in Large Language Models
  8. 2026Collective Hallucination in Multi-Agent LLMs:Modeling and Defense