# Latent Agents: A Post-Training Procedure for Internalized Multi-Agent Debate

Authors: John Seon Keun Yi, Aaron Mueller, Dokyun Lee.

Post-trains a single model to internalize the computation of a multi-agent debate rather than emitting the debate as text.

First public: 2026-04-27.
ArXiv initial submission: 2026-04-27.

[Original paper](https://arxiv.org/abs/2604.24881) · [Reading-list entry](https://latent-implicit-thinking.github.io/papers/2604.24881/)
[Preprint BibTeX](https://latent-implicit-thinking.github.io/papers/2604.24881/citation.bib) · [Preprint CSL-JSON](https://latent-implicit-thinking.github.io/papers/2604.24881/citation.csl.json) · [JSON record](https://latent-implicit-thinking.github.io/papers/2604.24881/record.json)
[Code](https://github.com/johnsk95/latent_agents)

## Approaches

- [Internalized reasoning](https://latent-implicit-thinking.github.io/topics/internalized-reasoning/): Learn from worked solutions so that some or all of the reasoning can later happen without writing those steps. Training may use a separate teacher model or progressively remove written steps from the model being trained.

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.