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Synapse
April 30, 20260 citationsOpen Access

LSC and Massively Documented LLM Hallucination: A Dual-Interpretation Framework for AI-Assisted Scientific Discovery

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LLuciferSun

Key Points

  • The study proposes a framework for understanding epistemic risks in AI-assisted scientific discovery.
  • Proposes Massively Documented LLM Hallucination as a framework.
  • Uses LSC neutrino research as a dual-interpretation case study.
  • Conceptual separation of validated and unvalidated physics in scientific research.
  • Identification of AI-generated epistemic artifacts in research.

Abstract

A publication-grade working paper proposing Massively Documented LLM Hallucination (MDLH) as a formal epistemic-risk framework for AI-assisted scientific discovery, using the LSC neutrino research line as a dual-interpretation case study. The work does not claim that LSC is validated physics or that it is false; it separates unvalidated physics from AI-generated epistemic artifacts.

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Cite This Study

LuciferSun (2026) studied this question.

synapsesocial.com/papers/69f2a4b78c0f03fd67763c2fhttps://doi.org/10.5281/zenodo.19851006
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Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1MHLM / MDLH Ultra Master Library Update: Final AI-Collaboration Evidence Archive and Freeze-Preparation Package2026
  2. 2LSC and Massively Documented LLM Hallucination: Prompt Archive, Simulation Lab, and Model-Lineage Extension2026
  3. 3Hallucinations in Scholarly LLMs: A Conceptual Overview and Practical Implications2026
  4. 4Comprehending and Reducing LLM Hallucinations2024 · 1 citations
  5. 5SLM Meets LLM: Balancing Latency, Interpretability and Consistency in Hallucination Detection2024