# Schema-Anchored Latent Reasoning for Semantic Parsing-Based Knowledge Base Question Answering

Authors: Guangze Gao, Zixuan Li, Sikui Zhang, Chunfeng Yuan, Wenjuan Li, Bing Li, Xiaolong Jin, Weiming Hu.

Aligns continuous thoughts with a codebook of knowledge-base relations and classes, using schema traces derived from correct logical forms. Feeds the aligned codes into later reasoning steps before generating an executable query.

First public: 2026-09-17.
ArXiv initial submission: 2026-09-17.

[Original paper](https://arxiv.org/abs/2609.20398) · [Reading-list entry](https://latent-implicit-thinking.github.io/papers/2609.20398/)
[Preprint BibTeX](https://latent-implicit-thinking.github.io/papers/2609.20398/citation.bib) · [Preprint CSL-JSON](https://latent-implicit-thinking.github.io/papers/2609.20398/citation.csl.json) · [JSON record](https://latent-implicit-thinking.github.io/papers/2609.20398/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.