# Latent-VC-Data

Latent Video Cache for Video Reasoning

## Introduction **Latent-VC (Latent Visual Cache)** introduces a recurrent latent visual cache inside the decoder of a large multimodal model to mitigate **Visual Anchoring Decay** in long-form video reasoning. Instead of relying on a pure *read-once, generate-many* pipeline, Latent-VC constructs a compact latent visual memory before answer generation, enabling the model to preserve grounding to visual evidence throughout reasoning. This dataset contains the **video training data** used to train the [Latent-VC-9B](https://huggingface.co/BRZ911/Latent-VC-9B) model, built on **Qwen3.5-9B-Base** and trained with two stages: Supervised Fine-Tuning (SFT) with contrastive cache alignment, followed by GRPO with vision-grounded rewards and latent grounding supervision. ## Dataset Details - **Task:** Grounded long-form video reasoning - **Usage:** Training the Latent-VC model (SFT and GRPO stages) - **Companion model:** [BRZ911/Latent-VC-9B](https://huggingface.co/BRZ911/Latent-VC-9B) - **Paper:** [Latent Visual Cache for Video Reasoning](https://arxiv.org/abs/2607.02607) ## Usage Please refer to the [official GitHub repository](https://github.com/BRZ911/Latent-VC) for instructions on how to download and use this dataset for training and evaluation. ## License Please refer to the GitHub repository for license details. ## Citation If you find this work useful, please cite: ```bibtex @misc{zhang2026latentvisualcachevideo, title={Latent Visual Cache for Video Reasoning}, author={Yongheng Zhang and Zhipeng Xu and Hao Wu and Yinghui Li and Di Yin and Xing Sun and Philip S. Yu}, year={2026}, eprint={2607.02607}, archivePrefix={arXiv}, primaryClass={cs.CV}, url={https://arxiv.org/abs/2607.02607}, } ```