# From Hallucination to Reliability: Generative Modeling and the Structure of Scientific Inference

arXiv: https://arxiv.org/abs/2504.08526 · HTML: https://mlsanity.com/paper/2504.08526/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 62 references that are in the map.
- First posted on arXiv 11 Apr 2025.

## Builds on (most cited first, 13 of 13)

- [Score-Based Generative Modeling through Stochastic Differential Equations](https://mlsanity.com/paper/2011.13456/) (2020)
- [Sparks of Artificial General Intelligence: Early experiments with GPT-4](https://mlsanity.com/paper/2303.12712/) (2023)
- [Survey of Hallucination in Natural Language Generation](https://mlsanity.com/paper/2202.03629/) (2022)
- [Diffusion Models: A Comprehensive Survey of Methods and Applications](https://mlsanity.com/paper/2209.00796/) (2022)
- [Assessing Generative Models via Precision and Recall](https://mlsanity.com/paper/1806.00035/) (2018)
- [Hallucination is Inevitable: An Innate Limitation of Large Language Models](https://mlsanity.com/paper/2401.11817/) (2024)
- [Understanding Hallucinations in Diffusion Models through Mode Interpolation](https://mlsanity.com/paper/2406.09358/) (2024)
- [LLMs Will Always Hallucinate, and We Need to Live With This](https://mlsanity.com/paper/2409.05746/) (2024)
- [AtmoRep: A stochastic model of atmosphere dynamics using large scale representation learning](https://mlsanity.com/paper/2308.13280/) (2023)
- [A Mathematical Abstraction for Balancing the Trade-off Between Creativity and Reality in Large Language Models](https://mlsanity.com/paper/2306.02295/) (2023)
- [Deep Learning Opacity in Scientific Discovery](https://mlsanity.com/paper/2206.00520/) (2022)
- [Beyond transparency: computational reliabilism as an externalist epistemology of algorithms](https://mlsanity.com/paper/2502.20402/) (2025)
- [Artificial Neural Nets and the Representation of Human Concepts](https://mlsanity.com/paper/2312.05337/) (2023)

## Built on by (most cited first, 2 of 2)

- [Generative Inverse Design with Abstention via Diagonal Flow Matching](https://mlsanity.com/paper/2603.15925/) (2026)
- [CONFIDE: Hallucination Assessment for Reliable Biomolecular Structure Prediction and Design](https://mlsanity.com/paper/2512.02033/) (2025)

## Abstract (arXiv)

Generative AI is increasingly used in science, but is unavoidably prone to hallucination. I develop a reliabilist account of how generative AI nevertheless gives rise to new scientific knowledge. I analyze hallucinations as non-strategic misrepresentations of the target phenomenon, introduced by a model's generative activity, rather than inherited from training data. Through case studies of AlphaFold and SEEDS, I show how scientific workflows draw on pre-existing knowledge of target phenomena to filter or qualify hallucinatory outputs, thereby preventing their erroneous content from propagating into downstream inference. Finally, I show that workflows are units of epistemic evaluation in their own right.