Data Scaling as Progressive Coverage of a Predictive Contribution Spectrum
The paper explores the hypothesis that real-data scaling laws are influenced by a latent predictive contribution spectrum. It presents a method using a suffix-automaton representation to analyze text corpora and defines a global-KL predictive contribution spectrum. The findings indicate a strong correlation between the tail slope of this spectrum and the empirical data-scaling exponent of a small GPT learner.
- ▪The research investigates how real-data scaling laws are governed by a predictive contribution spectrum.
- ▪A suffix-automaton representation of text corpora is utilized to define a global-KL predictive contribution spectrum.
- ▪The study finds a strong correlation between the tail slope of the spectrum and the data-scaling exponent of a GPT learner.
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| Original publisher | arXiv cs.AI |
| Canonical URL | https://arxiv.org/abs/2605.20196 |
| Publication time | Fri, 22 May 2026 00:00:00 -0400 |
| Retrieval time | 2026-05-22T04:02:00.009Z |
| Last seen | 2026-05-22T04:02:00.009Z |
| Headline source | Publisher (no WeSearch rewrite) |
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| Summary source text | contentText |
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| Publisher visit | Yes — open original |
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| 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
Computer Science > Computation and Language arXiv:2605.20196 (cs) [Submitted on 5 Apr 2026] Title:Data Scaling as Progressive Coverage of a Predictive Contribution Spectrum Authors:Zihui Song, Shihao Ji, Hongxi Li, Shuaizhi Cheng, Chunlin Huang View a PDF of the paper titled Data Scaling as Progressive Coverage of a Predictive Contribution Spectrum, by Zihui Song and 4 other authors View PDF HTML (experimental) Abstract:We investigate the hypothesis that real-data scaling laws are governed by progressive coverage of a latent predictive contribution spectrum rather than by token-frequency tails alone.
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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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