# GradCuit: Credit-Assigned Gradient Flow Enables Robust and Interpretable Test-Time Latent Reasoning

Authors: Zhaoxin Yu, Qi Shen, Hengli Li, Zhaowei Zhang, Song-Chun Zhu, Chi Zhang, Zilong Zheng.

At test time, adjusts extra hidden states inside the transformer using feedback on generated answers, while keeping the model’s weights fixed.

First public: 2026-08-03.
ArXiv initial submission: 2026-08-03.

[Original paper](https://arxiv.org/abs/2608.02585) · [Reading-list entry](https://latent-implicit-thinking.github.io/papers/2608.02585/)
[Preprint BibTeX](https://latent-implicit-thinking.github.io/papers/2608.02585/citation.bib) · [Preprint CSL-JSON](https://latent-implicit-thinking.github.io/papers/2608.02585/citation.csl.json) · [JSON record](https://latent-implicit-thinking.github.io/papers/2608.02585/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.