# Reasoning as Pattern Matching: Shared Mechanisms in Human and LLM Everyday Reasoning

arXiv: https://arxiv.org/abs/2606.13607 · HTML: https://mlsanity.com/paper/2606.13607/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in World models for language agents, one of 11 directions in World models.
- Builds on 16 of its 85 references that are in the map.
- First posted on arXiv 11 Jun 2026.

## Placed in

- World models / [World models for language agents](https://mlsanity.com/wm/world-models-for-language-agents/)
- LLM reasoning / [Reasoning benchmarks and evaluation](https://mlsanity.com/lr/reasoning-benchmarks-and-evaluation/)

## Builds on (most cited first, 16 of 16)

- [Interpretability in the Wild: a Circuit for Indirect Object Identification in GPT-2 small](https://mlsanity.com/paper/2211.00593/) (2022)
- [In-Context Learning Creates Task Vectors](https://mlsanity.com/paper/2310.15916/) (2023)
- [Large Language Models Fail on Trivial Alterations to Theory-of-Mind Tasks](https://mlsanity.com/paper/2302.08399/) (2023)
- [Embers of Autoregression: Understanding Large Language Models Through the Problem They are Trained to Solve](https://mlsanity.com/paper/2309.13638/) (2023)
- [From Word Models to World Models: Translating from Natural Language to the Probabilistic Language of Thought](https://mlsanity.com/paper/2306.12672/) (2023)
- [A Theory of Emergent In-Context Learning as Implicit Structure Induction](https://mlsanity.com/paper/2303.07971/) (2023)
- [Auxiliary task demands mask the capabilities of smaller language models](https://mlsanity.com/paper/2404.02418/) (2024)
- [Prompting is not a substitute for probability measurements in large language models](https://mlsanity.com/paper/2305.13264/) (2023)
- [GPT-4 Can't Reason](https://mlsanity.com/paper/2308.03762/) (2023)
- [Using Counterfactual Tasks to Evaluate the Generality of Analogical Reasoning in Large Language Models](https://mlsanity.com/paper/2402.08955/) (2024)
- [Linear Latent World Models in Simple Transformers: A Case Study on Othello-GPT](https://mlsanity.com/paper/2310.07582/) (2023)
- [How Do Language Models Compose Functions?](https://mlsanity.com/paper/2510.01685/) (2025)
- [LLMs are not (consistently) Bayesian: Quantifying internal (in)consistencies of LLMs' probabilistic beliefs](https://mlsanity.com/paper/2605.06915/) (2026)
- [Characterizing Pattern Matching and Its Limits on Compositional Task Structures](https://mlsanity.com/paper/2505.20278/) (2025)
- [The unreasonable effectiveness of pattern matching](https://mlsanity.com/paper/2601.11432/) (2026)
- [Grounding Social Perception in Intuitive Physics](https://mlsanity.com/paper/2603.27410/) (2026)

## Abstract (arXiv)

When large language models (LLMs) fail to generalize or make content-sensitive errors in reasoning, it is often taken as evidence that LLMs are not truly reasoning, but rather performing a kind of pattern matching. The implication is that human behavior does not exhibit the same types of failures because human reasoning relies on principled and content-invariant world models. We test this assumption by first evaluating humans and LLMs on their ability to engage in common-sense reasoning about a variety of everyday situations. Our results reveal convergent patterns of reasoning across 46 LLMs and two cohorts of human participants. We then ask whether this behavioral convergence is due to LLMs having acquired content-invariant world models or a set of pattern-matching heuristics by characterizing the roles of content-invariant and content-sensitive model neurons in producing human-like responses. We find that while LLMs encode both content-invariant and content-sensitive representations, it is content-sensitive mechanisms which are causally responsible for aligning models with humans. Taken together, our results suggest that everyday causal reasoning in people and LLMs makes heavy use of pattern-matching.