# Enhancing Long-Video VLM Embeddings with Query-Aware Streaming Latent Reasoning

Authors: Haozhe Chi, Song Jin, Yang Jin, Yadong Mu.

Processes selected video clips sequentially while persistent think tokens accumulate evidence for a fixed-size retrieval embedding. Intermediate retrieval supervision and self-distillation train partial-video readouts; the output is an embedding rather than a generated reasoning trace.

First public: 2026-10-04.
ArXiv initial submission: 2026-10-04.

[Original paper](https://arxiv.org/abs/2610.04864) · [Reading-list entry](https://latent-implicit-thinking.github.io/papers/2610.04864/)
[Preprint BibTeX](https://latent-implicit-thinking.github.io/papers/2610.04864/citation.bib) · [Preprint CSL-JSON](https://latent-implicit-thinking.github.io/papers/2610.04864/citation.csl.json) · [JSON record](https://latent-implicit-thinking.github.io/papers/2610.04864/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.
- [Recurrent reasoning](https://latent-implicit-thinking.github.io/topics/recurrent-reasoning/): Repeat parts of the model or keep intermediate information in memory while working through a problem. Some methods also learn how many passes to run.

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.