# Task-Oriented Key-Layer KV Communication for Efficient Latent Multi-Agent Collaboration

Authors: Dongsen Zhang, Peipei Li, Zekun Li, Wenjun Xu.

Lets agents perform hidden-state reasoning and pass working memory through the key-value cache of one selected transformer layer. Reuses that layer as the entry point for subsequent reasoning updates, avoiding earlier layers and decoding only the final agent's response into text.

First public: 2026-09-24.
ArXiv initial submission: 2026-09-24.

[Original paper](https://arxiv.org/abs/2610.08820) · [Reading-list entry](https://latent-implicit-thinking.github.io/papers/2610.08820/)
[Preprint BibTeX](https://latent-implicit-thinking.github.io/papers/2610.08820/citation.bib) · [Preprint CSL-JSON](https://latent-implicit-thinking.github.io/papers/2610.08820/citation.csl.json) · [JSON record](https://latent-implicit-thinking.github.io/papers/2610.08820/record.json)

## Approaches

- [Continuous-state reasoning](https://latent-implicit-thinking.github.io/topics/continuous-states/): Use vectors—lists of numbers—to carry intermediate reasoning from one step to the next, instead of selecting a text token at every step. Some methods use the model's hidden states; others combine several candidate token representations.

Summary and categories are editorial. Read the original paper to verify claims and limitations. A citation to a paper does not, by itself, establish that a later method builds on it.

Reading-list version 0.9.5; literature checked through 2026-10-18.