Features have life history. And we should care
The paper discusses the life history of features in language models, highlighting their emergence, persistence, and eventual decline during training. It identifies a stable representational backbone that organizes the model's structure and outlines its four key properties. The findings suggest that the initial phase of training is crucial for establishing this scaffold, which influences the model's development throughout the training process.
- ▪Features in language models have a life history that includes emergence, persistence, and decline during training.
- ▪The study identifies approximately 50 sparse features that serve as a stable scaffold for the model's representational structure.
- ▪The first 1% of training is critical for assembling features, which reorganize significantly faster during this period.
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Record
| Original publisher | arXiv cs.AI |
| Canonical URL | https://arxiv.org/abs/2605.18789 |
| Publication time | Wed, 20 May 2026 00:00:00 -0400 |
| Retrieval time | 2026-05-20T04:04:59.484Z |
| Last seen | 2026-05-20T04:04:59.484Z |
| Headline source | Publisher (no WeSearch rewrite) |
| Excerpt source | publisher body |
| Excerpt method | First ~120 words (~800 chars) of extracted publisher body, fair-use limited. |
| Summary | WeSearch · cerebras-chat (WeSearch summarizer) |
| Summary source text | contentText |
| Citation coverage | Summary is a WeSearch-generated derivative; primary citation is the original publisher URL. |
| Cluster | nxDJLzd_CjxU |
| Cluster logic | Grouped by semantic title/content similarity across sources within a rolling window. Same-publisher template collisions are excluded from coverage comparison. |
| Ranking reason | Story pages are not engagement-ranked. Hub feeds use recency, with optional source-diversified chronological ordering (cap consecutive stories per source). No personalized ranking. |
| Publisher visit | Yes — open original |
| Substitutes article? | No — link-out required for full text |
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| Indexing | May the item be indexed (stored, ranked, made findable)? | Allowed |
| Snippet | May a short excerpt of the publisher's text be shown? | Allowed |
| AI summary | May WeSearch generate its own short summary of the article? | Limited |
| Retrieval / RAG | May the content be exposed for third-party retrieval-augmented generation? | Not asserted |
| Model training | May the content be used to train AI models? | Not asserted |
| Commercial reuse | May the content be reused commercially? | Not permitted |
Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.
Opening excerpt (first ~120 words) tap to expand
Quantitative Biology > Neurons and Cognition arXiv:2605.18789 (q-bio) [Submitted on 7 May 2026] Title:Features have life history. And we should care Authors:Philipp Stecher, Sandro Radovanović, Vlasta Sikimić, Reinhard Kahle View a PDF of the paper titled Features have life history. And we should care, by Philipp Stecher and Sandro Radovanovi\'c and Vlasta Sikimi\'c and Reinhard Kahle View PDF HTML (experimental) Abstract:Features in language models have life history: they emerge, persist, and die during training, yet the importance of that history remains largely unexplored.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at arXiv cs.AI.
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