# LADDER: Graph-Guided Diffusion Language Models for Efficient Multi-Hop Reasoning

Authors: Senlei Zhang, Linhao Luo, Qian-Wen Zhang, Siyu An, Junnan Dong, Shuhao Zhang, Xing Sun.

Triggers graph retrieval when new entities become identifiable in a partially denoised draft, then uses graph propagation to provide multi-hop evidence for subsequent updates. This combines retrieval with parallel textual reasoning rather than removing the text trace.

First public: 2026-09-21.
ArXiv initial submission: 2026-09-21.

[Original paper](https://arxiv.org/abs/2609.24346) · [Reading-list entry](https://latent-implicit-thinking.github.io/papers/2609.24346/)
[Preprint BibTeX](https://latent-implicit-thinking.github.io/papers/2609.24346/citation.bib) · [Preprint CSL-JSON](https://latent-implicit-thinking.github.io/papers/2609.24346/citation.csl.json) · [JSON record](https://latent-implicit-thinking.github.io/papers/2609.24346/record.json)

## Approaches

- [Alternative generation methods](https://latent-implicit-thinking.github.io/topics/alternative-reasoning-schedules/): Change how intermediate steps are generated, revised, or searched—for example, by refining several steps in parallel. These are related approaches: some still produce written chains of thought.

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.