# Large Language Models and Emergence: A Complex Systems Perspective

arXiv: https://arxiv.org/abs/2506.11135 · HTML: https://mlsanity.com/paper/2506.11135/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 73 references that are in the map.
- First posted on arXiv 10 Jun 2025.

## Builds on (most cited first, 22 of 22)

- [Scaling Laws for Neural Language Models](https://mlsanity.com/paper/2001.08361/) (2020)
- [Emergent Abilities of Large Language Models](https://mlsanity.com/paper/2206.07682/) (2022)
- [Deep Double Descent: Where Bigger Models and More Data Hurt](https://mlsanity.com/paper/1912.02292/) (2019)
- [GSM-Symbolic: Understanding the Limitations of Mathematical Reasoning in Large Language Models](https://mlsanity.com/paper/2410.05229/) (2024)
- [Are Emergent Abilities of Large Language Models a Mirage?](https://mlsanity.com/paper/2304.15004/) (2023)
- [Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task](https://mlsanity.com/paper/2210.13382/) (2022)
- [Emergent Linear Representations in World Models of Self-Supervised Sequence Models](https://mlsanity.com/paper/2309.00941/) (2023)
- [Emergent Analogical Reasoning in Large Language Models](https://mlsanity.com/paper/2212.09196/) (2022)
- [Sudden Drops in the Loss: Syntax Acquisition, Phase Transitions, and Simplicity Bias in MLMs](https://mlsanity.com/paper/2309.07311/) (2023)
- [Evaluating the World Model Implicit in a Generative Model](https://mlsanity.com/paper/2406.03689/) (2024)
- [Are Emergent Abilities in Large Language Models just In-Context Learning?](https://mlsanity.com/paper/2309.01809/) (2023)
- [Large Language Models as Tax Attorneys: A Case Study in Legal Capabilities Emergence](https://mlsanity.com/paper/2306.07075/) (2023)
- [Double Descent Demystified: Identifying, Interpreting & Ablating the Sources of a Deep Learning Puzzle](https://mlsanity.com/paper/2303.14151/) (2023)
- [Emergent Abilities in Large Language Models: A Survey](https://mlsanity.com/paper/2503.05788/) (2025)
- [A U-turn on Double Descent: Rethinking Parameter Counting in Statistical Learning](https://mlsanity.com/paper/2310.18988/) (2023)
- [LLMs learn governing principles of dynamical systems, revealing an in-context neural scaling law](https://mlsanity.com/paper/2402.00795/) (2024)
- [Position: Key Claims in LLM Research Have a Long Tail of Footnotes](https://mlsanity.com/paper/2308.07120/) (2023)
- [A Rainbow in Deep Network Black Boxes](https://mlsanity.com/paper/2305.18512/) (2023)
- [Phase transition in large language models and the criticality of natural languages](https://mlsanity.com/paper/2406.05335/) (2024)
- [Numeracy from Literacy: Data Science as an Emergent Skill from Large Language Models](https://mlsanity.com/paper/2301.13382/) (2023)
- [On Principles of Emergent Organization](https://mlsanity.com/paper/2311.13749/) (2023)
- [Emergence and Function of Abstract Representations in Self-Supervised Transformers](https://mlsanity.com/paper/2312.05361/) (2023)

## Built on by (most cited first, 7 of 7)

- [If LLMs Have Human-Like Attributes, Then So Does Age of Empires II](https://mlsanity.com/paper/2605.31514/) (2026)
- [Relative-Based Scaling Law for Neural Language Models](https://mlsanity.com/paper/2510.20387/) (2025)
- [Implicature in Interaction: Understanding Implicature Improves Alignment in Human-LLM Interaction](https://mlsanity.com/paper/2510.25426/) (2025)
- [Emergence is Overrated: AGI as an Archipelago of Experts](https://mlsanity.com/paper/2603.07979/) (2026)
- [Arithmetic Pedagogy for Language Models](https://mlsanity.com/paper/2606.05106/) (2026)
- [The Role of Rigor in Artificial Intelligence](https://mlsanity.com/paper/2607.03634/) (2026)
- [Population Physics, Population Problems: Safety and Emergence in LLM Societies](https://mlsanity.com/paper/2609.33871/) (2026)

## Abstract (arXiv)

Emergence is a concept in complexity science that describes how many-body systems manifest novel higher-level properties, properties that can be described by replacing high-dimensional mechanisms with lower-dimensional effective variables and theories. This is captured by the idea "more is different". Intelligence is a consummate emergent property manifesting increasingly efficient -- cheaper and faster -- uses of emergent capabilities to solve problems. This is captured by the idea "less is more". In this paper, we first examine claims that Large Language Models exhibit emergent capabilities, reviewing several approaches to quantifying emergence, and secondly ask whether LLMs possess emergent intelligence.