# Features have life history. And we should care

arXiv: https://arxiv.org/abs/2605.18789 · HTML: https://mlsanity.com/paper/2605.18789/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 15 of its 24 references that are in the map.
- First posted on arXiv 7 May 2026.

## Builds on (most cited first, 15 of 15)

- [Locating and Editing Factual Associations in GPT](https://mlsanity.com/paper/2202.05262/) (2022)
- [The Pile: An 800GB Dataset of Diverse Text for Language Modeling](https://mlsanity.com/paper/2101.00027/) (2020)
- [The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks](https://mlsanity.com/paper/1803.03635/) (2018)
- [Pythia: A Suite for Analyzing Large Language Models Across Training and Scaling](https://mlsanity.com/paper/2304.01373/) (2023)
- [Scaling and evaluating sparse autoencoders](https://mlsanity.com/paper/2406.04093/) (2024)
- [Sparse Feature Circuits: Discovering and Editing Interpretable Causal Graphs in Language Models](https://mlsanity.com/paper/2403.19647/) (2024)
- [A is for Absorption: Studying Feature Splitting and Absorption in Sparse Autoencoders](https://mlsanity.com/paper/2409.14507/) (2024)
- [Automatically Interpreting Millions of Features in Large Language Models](https://mlsanity.com/paper/2410.13928/) (2024)
- [Towards Principled Evaluations of Sparse Autoencoders for Interpretability and Control](https://mlsanity.com/paper/2405.08366/) (2024)
- [Critical Learning Periods in Deep Neural Networks](https://mlsanity.com/paper/1711.08856/) (2017)
- [Tracking the Feature Dynamics in LLM Training: A Mechanistic Study](https://mlsanity.com/paper/2412.17626/) (2024)
- [Critical Learning Periods Emerge Even in Deep Linear Networks](https://mlsanity.com/paper/2308.12221/) (2023)
- [Evolution of Concepts in Language Model Pre-Training](https://mlsanity.com/paper/2509.17196/) (2025)
- [Crosscoding Through Time: Tracking Emergence & Consolidation Of Linguistic Representations Throughout LLM Pretraining](https://mlsanity.com/paper/2509.05291/) (2025)
- [How a Bilingual LM Becomes Bilingual: Tracing Internal Representations with Sparse Autoencoders](https://mlsanity.com/paper/2503.06394/) (2025)

## Abstract (arXiv)

Features in language models have life history: they emerge, persist, and die during training, yet the importance of that history remains largely unexplored. We find evidence of a persistent representational backbone, which we identify in Pythia-160M and -410M as the carrier scaffold: ~50 sparse features with stable life histories, around which the model's representational structure organises. It has four properties. (i)~It assembles early: features emerge, die, and reorganise ~40!× faster in the first 1% of training than afterwards, and the scaffold is already largely fixed by then. (ii)~It is load-bearing: joint cross-layer ablation identifies the carriers as far more load-bearing than any count-matched non-scaffold population, a gap invisible to per-firing single-feature methods. (iii)~Function precedes direction: which features will become carriers is already predictable from training-onset firing patterns alone, correctly distinguishing future carriers from non-carriers in 4 of 5 cases, before the geometry has settled. (iv)~It seeds subsequent development: by the end of training, scaffold carriers have recruited 64% of all active features into the scaffold hierarchy. Life history is consistent with a two-phase account of training: selection appears to largely determine the scaffold in the first 1%; the remaining 99% appears to calibrate geometry around a substrate already set.