# Layers at Similar Depths Generate Similar Activations Across LLM Architectures

arXiv: https://arxiv.org/abs/2504.08775 · HTML: https://mlsanity.com/paper/2504.08775/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- Builds on 23 of its 36 references that are in the map.
- First posted on arXiv 3 Apr 2025.

## Builds on (most cited first, 23 of 23)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Measuring Massive Multitask Language Understanding](https://mlsanity.com/paper/2009.03300/) (2020)
- [Phi-3 Technical Report: A Highly Capable Language Model Locally on Your Phone](https://mlsanity.com/paper/2404.14219/) (2024)
- [Similarity of Neural Network Representations Revisited](https://mlsanity.com/paper/1905.00414/) (2019)
- [Instruction-Following Evaluation for Large Language Models](https://mlsanity.com/paper/2311.07911/) (2023)
- [BERT Rediscovers the Classical NLP Pipeline](https://mlsanity.com/paper/1905.05950/) (2019)
- [Instruction Tuning for Large Language Models: A Survey](https://mlsanity.com/paper/2308.10792/) (2023)
- [Do Wide and Deep Networks Learn the Same Things? Uncovering How Neural Network Representations Vary with Width and Depth](https://mlsanity.com/paper/2010.15327/) (2020)
- [Probing Pretrained Language Models for Lexical Semantics](https://mlsanity.com/paper/2010.05731/) (2020)
- [The Remarkable Robustness of LLMs: Stages of Inference?](https://mlsanity.com/paper/2406.19384/) (2024)
- [Similarity of Neural Network Models: A Survey of Functional and Representational Measures](https://mlsanity.com/paper/2305.06329/) (2023)
- [A Practical Review of Mechanistic Interpretability for Transformer-Based Language Models](https://mlsanity.com/paper/2407.02646/) (2024)
- [Universal Neurons in GPT2 Language Models](https://mlsanity.com/paper/2401.12181/) (2024)
- [Analyzing Individual Neurons in Pre-trained Language Models](https://mlsanity.com/paper/2010.02695/) (2020)
- [Emergence of a High-Dimensional Abstraction Phase in Language Transformers](https://mlsanity.com/paper/2405.15471/) (2024)
- [Similarity Analysis of Contextual Word Representation Models](https://mlsanity.com/paper/2005.01172/) (2020)
- [From Tokens to Words: On the Inner Lexicon of LLMs](https://mlsanity.com/paper/2410.05864/) (2024)
- [Blackbox meets blackbox: Representational Similarity and Stability Analysis of Neural Language Models and Brains](https://mlsanity.com/paper/1906.01539/) (2019)
- [Towards Universality: Studying Mechanistic Similarity Across Language Model Architectures](https://mlsanity.com/paper/2410.06672/) (2024)
- [Understanding the Inner Workings of Language Models Through Representation Dissimilarity](https://mlsanity.com/paper/2310.14993/) (2023)
- [On the Origins of the Block Structure Phenomenon in Neural Network Representations](https://mlsanity.com/paper/2202.07184/) (2022)
- [Towards Measuring Representational Similarity of Large Language Models](https://mlsanity.com/paper/2312.02730/) (2023)
- [Investigating Representation Universality: Case Study on Genealogical Representations](https://mlsanity.com/paper/2410.08255/) (2024)

## Built on by (most cited first, 12 of 12)

- [Harnessing the Universal Geometry of Embeddings](https://mlsanity.com/paper/2505.12540/) (2025)
- [Correlated Errors in Large Language Models](https://mlsanity.com/paper/2506.07962/) (2025)
- [Differential syntactic and semantic encoding in LLMs](https://mlsanity.com/paper/2601.04765/) (2026)
- [Signal in the Noise: Polysemantic Interference Transfers and Predicts Cross-Model Influence](https://mlsanity.com/paper/2505.11611/) (2025)
- [Dynamic Weight Grafting: Localizing Finetuned Factual Knowledge in Transformers](https://mlsanity.com/paper/2506.20746/) (2025)
- [Large Language Models Show Signs of Alignment with Human Neurocognition During Abstract Reasoning](https://mlsanity.com/paper/2508.10057/) (2025)
- [Mapping Overlaps in Benchmarks through Perplexity in the Wild](https://mlsanity.com/paper/2509.23488/) (2025)
- [Characterizing Human Semantic Navigation in Concept Production as Trajectories in Embedding Space](https://mlsanity.com/paper/2602.05971/) (2026)
- [Contravariance Theory: Strong Alignment for Minimal Solutions to Hard Tasks](https://mlsanity.com/paper/2607.08561/) (2026)
- [Gradient Smoothing: Coupling Layer-wise Updates for Improved Optimization](https://mlsanity.com/paper/2606.30813/) (2026)
- [SAEVerbalizer: Generating Explanations for Sparse Autoencoder Features via Representation Verbalization](https://mlsanity.com/paper/2608.13538/) (2026)
- [Global Divergence, Local Convergence: Representation Geometry in SSMs and Transformers](https://mlsanity.com/paper/2609.08692/) (2026)

## Abstract (arXiv)

How do the latent spaces used by independently-trained LLMs relate to one another? We study the nearest neighbor relationships induced by activations at different layers of 24 open-weight LLMs, and find that they 1) tend to vary from layer to layer within a model, and 2) are approximately shared between corresponding layers of different models. Claim 2 shows that these nearest neighbor relationships are not arbitrary, as they are shared across models, but Claim 1 shows that they are not "obvious" either, as there is no single set of nearest neighbor relationships that is universally shared. Together, these suggest that LLMs generate a progression of activation geometries from layer to layer, but that this entire progression is largely shared between models, stretched and squeezed to fit into different architectures.