# LLMs, Reasoning and Plagiarism

arXiv: https://arxiv.org/abs/2601.02380 · HTML: https://mlsanity.com/paper/2601.02380/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Placed in Reasoning benchmarks and evaluation, one of 17 directions in LLM reasoning.
- Builds on 7 of its 41 references that are in the map.
- First posted on arXiv 18 Dec 2025.

## Placed in

- LLM reasoning / [Reasoning benchmarks and evaluation](https://mlsanity.com/lr/reasoning-benchmarks-and-evaluation/)

## Builds on (most cited first, 7 of 7)

- [Sparks of Artificial General Intelligence: Early experiments with GPT-4](https://mlsanity.com/paper/2303.12712/) (2023)
- [Quantifying Memorization Across Neural Language Models](https://mlsanity.com/paper/2202.07646/) (2022)
- [Monitoring AI-Modified Content at Scale: A Case Study on the Impact of ChatGPT on AI Conference Peer Reviews](https://mlsanity.com/paper/2403.07183/) (2024)
- [Granite Code Models: A Family of Open Foundation Models for Code Intelligence](https://mlsanity.com/paper/2405.04324/) (2024)
- [Foundation Models and Fair Use](https://mlsanity.com/paper/2303.15715/) (2023)
- [Mathematical exploration and discovery at scale](https://mlsanity.com/paper/2511.02864/) (2025)
- [Early science acceleration experiments with GPT-5](https://mlsanity.com/paper/2511.16072/) (2025)

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

- [CliffSearch: Structured Agentic Co-Evolution over Theory and Code for Scientific Algorithm Discovery](https://mlsanity.com/paper/2604.01210/) (2026)
- [Can AI Scientists Change Their Minds? Prior-Evidence Conflict in Synthetic Universes](https://mlsanity.com/paper/2609.36726/) (2026)

## Abstract (arXiv)

Recent reports claim that Large Language Models (LLMs) derive new science and exhibit human-level general intelligence. Such claims are entangled with two different narratives about what LLMs do: one in which they are an engine of synthesis that genuinely reasons to new knowledge, and one in which they retrieve and re-emit the work of others without attribution. In the scientific setting these are best understood as a contrast between reasoning and plagiarism. Finding where the truth lies between these two narratives is very challenging, as central components of the model -- the training data and the interaction transcript -- remain opaque. Thus claims of LLM reasoning do not satisfy Popper's refutability principle. We propose guidelines for transparency and reproducibility that will allow reasoning claims to be studied using the scientific method. The dominance of the reasoning narrative, we suggest, is in practice encouraging plagiarism in the scientific literature; we discuss what might be done about it.