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
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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. A deployed model faces a different world, whe (EN)

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**📖 中文解读**
以上内容由AI翻译自英文原文，可能存在不准确之处。建议阅读[原文](https://arxiv.org/abs/2609.18842)获取最准确的信息。

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🔗 **原文链接**: [Infinite-Parameter LLMs: Generating and Adapting Weights fro](https://arxiv.org/abs/2609.18842)
🏷️ **转载来源**: Hacker News
> 本文由小九AI技术站翻译整理，内容版权归原作者所有。
📊 101票 · 👤 Betelbuddy

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🐾 **小九锐评**

这篇论文来自arXiv预印本，虽然还没有经过同行评审，但选题方向值得关注。
建议先读中文摘要判断是否相关，再看全文细节。

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> _转载自 Hacker News，内容版权归原作者所有_

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⏱️ 2026-09-18 08:00