# We Should Chart an Atlas of All the World's Models

arXiv: https://arxiv.org/abs/2503.10633 · HTML: https://mlsanity.com/paper/2503.10633/

## Key facts

- Cited by 25 papers in the map (data through 14 Oct 2026).
- Builds on 52 of its 67 references that are in the map.
- First posted on arXiv 13 Mar 2025.

## Builds on (most cited first, 30 of 52)

- [High-Resolution Image Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2112.10752/) (2021)
- [The Llama 3 Herd of Models](https://mlsanity.com/paper/2407.21783/) (2024)
- [LoRA: Low-Rank Adaptation of Large Language Models](https://mlsanity.com/paper/2106.09685/) (2021)
- [Model soups: averaging weights of multiple fine-tuned models improves accuracy without increasing inference time](https://mlsanity.com/paper/2203.05482/) (2022)
- [Model Cards for Model Reporting](https://mlsanity.com/paper/1810.03993/) (2018)
- [HyperNetworks](https://mlsanity.com/paper/1609.09106/) (2016)
- [TIES-Merging: Resolving Interference When Merging Models](https://mlsanity.com/paper/2306.01708/) (2023)
- [ZipLoRA: Any Subject in Any Style by Effectively Merging LoRAs](https://mlsanity.com/paper/2311.13600/) (2023)
- [LEEP: A New Measure to Evaluate Transferability of Learned Representations](https://mlsanity.com/paper/2002.12462/) (2020)
- [LogME: Practical Assessment of Pre-trained Models for Transfer Learning](https://mlsanity.com/paper/2102.11005/) (2021)
- [Stealing Part of a Production Language Model](https://mlsanity.com/paper/2403.06634/) (2024)
- [HyperDiffusion: Generating Implicit Neural Fields with Weight-Space Diffusion](https://mlsanity.com/paper/2303.17015/) (2023)
- [Transferability and Hardness of Supervised Classification Tasks](https://mlsanity.com/paper/1908.08142/) (2019)
- [Model merging with SVD to tie the Knots](https://mlsanity.com/paper/2410.19735/) (2024)
- [Predicting Neural Network Accuracy from Weights](https://mlsanity.com/paper/2002.11448/) (2020)
- [Equivariant Architectures for Learning in Deep Weight Spaces](https://mlsanity.com/paper/2301.12780/) (2023)
- [Learning to Learn with Generative Models of Neural Network Checkpoints](https://mlsanity.com/paper/2209.12892/) (2022)
- [Permutation Equivariant Neural Functionals](https://mlsanity.com/paper/2302.14040/) (2023)
- [Instructional Fingerprinting of Large Language Models](https://mlsanity.com/paper/2401.12255/) (2024)
- [Classifying the classifier: dissecting the weight space of neural networks](https://mlsanity.com/paper/2002.05688/) (2020)
- [Hyper-Representations as Generative Models: Sampling Unseen Neural Network Weights](https://mlsanity.com/paper/2209.14733/) (2022)
- [Graph Neural Networks for Learning Equivariant Representations of Neural Networks](https://mlsanity.com/paper/2403.12143/) (2024)
- [Knowledge is a Region in Weight Space for Fine-tuned Language Models](https://mlsanity.com/paper/2302.04863/) (2023)
- [Graph Metanetworks for Processing Diverse Neural Architectures](https://mlsanity.com/paper/2312.04501/) (2023)
- [Neural Functional Transformers](https://mlsanity.com/paper/2305.13546/) (2023)
- [A Survey on Model MoErging: Recycling and Routing Among Specialized Experts for Collaborative Learning](https://mlsanity.com/paper/2408.07057/) (2024)
- [Ranking Neural Checkpoints](https://mlsanity.com/paper/2011.11200/) (2020)
- [Copy, Right? A Testing Framework for Copyright Protection of Deep Learning Models](https://mlsanity.com/paper/2112.05588/) (2021)
- [Frustratingly Easy Transferability Estimation](https://mlsanity.com/paper/2106.09362/) (2021)
- [Towards Scalable and Versatile Weight Space Learning](https://mlsanity.com/paper/2406.09997/) (2024)

## Built on by (most cited first, 25 of 25)

- [Symmetry in Neural Network Parameter Spaces](https://mlsanity.com/paper/2506.13018/) (2025)
- [Learning a Generative Meta-Model of LLM Activations](https://mlsanity.com/paper/2602.06964/) (2026)
- [W2T: LoRA Weights Already Know What They Can Do](https://mlsanity.com/paper/2603.15990/) (2026)
- [Economies of Open Intelligence: Tracing Power & Participation in the Model Ecosystem](https://mlsanity.com/paper/2512.03073/) (2025)
- [Likelihood Variance as Text Importance for Resampling Texts to Map Language Models](https://mlsanity.com/paper/2505.15428/) (2025)
- [Future of Edge AI in biodiversity monitoring](https://mlsanity.com/paper/2602.13496/) (2026)
- [LOCUS: Low-Dimensional Model Embeddings for Efficient Model Exploration, Comparison, and Selection](https://mlsanity.com/paper/2601.21082/) (2026)
- [The Appeal and Reality of Recycling LoRAs with Adaptive Merging](https://mlsanity.com/paper/2602.12323/) (2026)
- [GeoSANE: Learning Geospatial Representations from Models, Not Data](https://mlsanity.com/paper/2603.23408/) (2026)
- [Query-efficient model evaluation using cached responses](https://mlsanity.com/paper/2605.07096/) (2026)
- [The Case for Model Science: Verify, Explore, Steer, Refine](https://mlsanity.com/paper/2606.01189/) (2026)
- [Discovering Hidden Gems in Model Repositories](https://mlsanity.com/paper/2601.22157/) (2026)
- [Ensuring Semantics in Weights of Implicit Neural Representations through the Implicit Function Theorem](https://mlsanity.com/paper/2601.23181/) (2026)
- [ModelLens: Finding the Best for Your Task from Myriads of Models](https://mlsanity.com/paper/2605.07075/) (2026)
- [Which Models Are Our Models Built On? Auditing Invisible Dependencies in Modern LLMs](https://mlsanity.com/paper/2606.12385/) (2026)
- [Language Model Maps for Prompt-Response Distributions via Log-Likelihood Vectors](https://mlsanity.com/paper/2603.18593/) (2026)
- [MetaGAI: A Large-Scale and High-Quality Benchmark for Generative AI Model and Data Card Generation](https://mlsanity.com/paper/2604.23539/) (2026)
- [ABLE: Representing and Mapping LLMs via Attribution-Based Large-model Embedding](https://mlsanity.com/paper/2606.07524/) (2026)
- [AI Supply Chain Galaxy: 3D Visual Analytics for License Compliance](https://mlsanity.com/paper/2606.16292/) (2026)
- [A Gravitational Interpretation of Safety Reversion under Fine-Tuning](https://mlsanity.com/paper/2606.28525/) (2026)
- [Spectral Signatures of Large Language Models](https://mlsanity.com/paper/2607.03377/) (2026)
- [WeightCLIP: Aligning Datasets and Models for Weight Space Learning](https://mlsanity.com/paper/2607.03551/) (2026)
- [Scalable Black-Box Model Attribution for Images](https://mlsanity.com/paper/2608.15652/) (2026)
- [An Investigation of the NeurIPS and ICML 2025 Position Tracks](https://mlsanity.com/paper/2608.16894/) (2026)
- [Sparse Priors for Efficient Distribution Learning](https://mlsanity.com/paper/2609.20883/) (2026)

## Abstract (arXiv)

Public model repositories now contain millions of models, yet most models remain undocumented and effectively lost. In this position paper, we advocate for charting the world's model population in a unified structure we call the Model Atlas: a graph that captures models, their attributes, and the weight transformations that connect them. The Model Atlas enables applications in model forensics, meta-ML research, and model discovery, challenging tasks given today's unstructured model repositories. However, because most models lack documentation, large atlas regions remain uncharted. Addressing this gap motivates new machine learning methods that treat models themselves as data, inferring properties such as functionality, performance, and lineage directly from their weights. We argue that a scalable path forward is to bypass the unique parameter symmetries that plague model weights. Charting all the world's models will require a community effort, and we hope its broad utility will rally researchers toward this goal.