# Internalized Reasoning for Long-Context Visual Document Understanding

Authors: Austin Veselka.

Trains on synthetic traces that rank document pages and extract relevant evidence, then uses low-strength model merging to internalize the trained reasoning behavior. Compares answering with and without written reasoning on long visual documents.

First public: 2026-03-31.
ArXiv initial submission: 2026-03-31.

[Original paper](https://arxiv.org/abs/2604.02371) · [Reading-list entry](https://latent-implicit-thinking.github.io/papers/2604.02371/)
[Preprint BibTeX](https://latent-implicit-thinking.github.io/papers/2604.02371/citation.bib) · [Preprint CSL-JSON](https://latent-implicit-thinking.github.io/papers/2604.02371/citation.csl.json) · [JSON record](https://latent-implicit-thinking.github.io/papers/2604.02371/record.json)

## 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.