Infinite-Parameter LLMs: Generating and Adapting Weights from Live Data
Computer Science > Artificial Intelligence arXiv:2609.18842 (cs) [Submitted on 16 Sep 2026] Title:Infinite-Parameter LLMs: Generating and Adapting Weights from Live Data Authors:Jinli Hu, Ross M. That success is built on static pretraining data. A deployed model faces a different world, where much of the data that would make it more useful is not in its training set but in the live interaction it is currently handling, such as the facts a user supplies or the corrections they give.
- ▪Computer Science > Artificial Intelligence arXiv:2609.18842 (cs) [Submitted on 16 Sep 2026] Title:Infinite-Parameter LLMs: Generating and Adapting Weights from Live Data Authors:Jinli Hu, Ross M.
- ▪That success is built on static pretraining data.
- ▪A deployed model faces a different world, where much of the data that would make it more useful is not in its training set but in the live interaction it is currently handling, such as the facts a user supplies or the corrections they give.
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| Original publisher | arXiv.org |
| Canonical URL | https://arxiv.org/abs/2609.18842 |
| Publication time | Thu, 17 Sep 2026 16:55:14 +0000 |
| Retrieval time | 2026-09-17T18:08:44.346Z |
| Last seen | 2026-09-17T18:08:44.346Z |
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| Summary source text | contentText |
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| 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.
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Computer Science > Artificial Intelligence arXiv:2609.18842 (cs) [Submitted on 16 Sep 2026] Title:Infinite-Parameter LLMs: Generating and Adapting Weights from Live Data Authors:Jinli Hu, Ross M. Clarke, Yichuan Zhang, José Miguel Hernández-Lobato View a PDF of the paper titled Infinite-Parameter LLMs: Generating and Adapting Weights from Live Data, by Jinli Hu and 2 other authors View PDF HTML (experimental) Abstract:The scaling laws hold that a language model grows more capable with more parameters and more training data, and Mixture-of-Experts (MoE) architectures have ridden these laws to remarkable results, activating only a fraction of an enormous stored parameter bank for each token. That success is built on static pretraining data.
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