# Nexus: Depth-Adaptive KV-Cache Splicing and Retrieval-Decoupled Tool Routing for Agentic LLMs on Unified Memory

Published: 2026-08-24 · Updated: 2026-08-24 · Category: Research

Source page: https://www.sipply.dev/story/nexus-depth-adaptive-kv-cache-splicing-and-retrieval-9a1bd74f · 中文: https://www.sipply.dev/zh/story/nexus-depth-adaptive-kv-cache-splicing-and-retrieval-9a1bd74f

## Summary

A recent study introduced Nexus, a novel approach aimed at enhancing the efficiency of agentic large language models (LLMs). This method focuses on two key innovations: depth-adaptive key-value (KV) cache splicing and retrieval-decoupled tool routing. By separating the tool routing process from the schema-prefill costs, Nexus significantly improves the time-to-first-token performance, allowing for more accurate routing as the tool registry expands. This advancement could lead to more responsive and capable LLMs in various applications.

## Key takeaways

- Nexus enhances LLM efficiency by decoupling tool routing from schema-prefill costs.
- The method improves time-to-first-token performance, making LLMs more responsive.
- Nexus supports an expanding tool registry for more accurate routing.

## Why it matters

The development of Nexus represents a significant step forward in optimizing large language models, which are increasingly critical in AI applications. Improved efficiency can lead to broader adoption and more sophisticated uses of LLMs in real-world scenarios.

- **Product managers:** For product managers, Nexus's advancements could mean faster deployment of LLM-based features, enhancing user experience and satisfaction while reducing operational costs.
- **Developers:** Developers and engineers can leverage Nexus to build more responsive applications, allowing for greater flexibility in integrating various tools and enhancing overall system performance.
- **Students and job seekers:** For students and job-seekers, understanding Nexus and its implications for LLMs can provide valuable insights into cutting-edge AI research, making them more competitive in the job market.

## Sources

- [Nexus: Depth-Adaptive KV-Cache Splicing and Retrieval-Decoupled Tool Routing for Agentic LLMs on Unified Memory](https://arxiv.org/abs/2608.20397) (arXiv cs.AI, 2026-08-24)

## 中文

### Nexus：深度自适应KV缓存拼接与检索解耦工具路由在统一内存中的应用

最近的一项研究介绍了Nexus，这是一种旨在提高智能大型语言模型（LLMs）效率的新方法。该方法专注于两个关键创新：深度自适应键值（KV）缓存拼接和检索解耦工具路由。通过将工具路由过程与模式预填充成本分离，Nexus显著改善了首次令牌的响应时间，使得在工具注册表扩展时能够实现更准确的路由。这一进展可能会导致在各种应用中更具响应性和能力的LLMs。

**要点：**

- Nexus通过将工具路由与模式预填充成本解耦来提高LLM效率。
- 该方法改善了首次令牌的响应时间，使LLMs更具响应性。
- Nexus支持扩展的工具注册表，以实现更准确的路由。

**为什么重要：** Nexus的发展代表了优化大型语言模型的重要一步，这在AI应用中变得越来越关键。提高效率可以促进LLMs在现实场景中的更广泛应用和更复杂的使用。

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