# USI-Brain: Enhancing Underwater Spatial Intelligence via Spatial Data-Driven Vision-Language Modeling

**Authors:** Zhiliang Zhu, Zhiqiang Zhao, Xie'an Jiang, Yingxin Lai, Kangye Zheng, Dawei Zhao  
**Lab:** Harbin Engineering University · **Published:** 4 Nov 2026  
**Links:** [arXiv](https://arxiv.org/abs/2611.06040) · [PDF](https://arxiv.org/pdf/2611.06040) · [Code](https://github.com/SKY-Zhu-91/USI-Brain) · [This page](https://omen-bit.github.io/ai-paper-daily/paper/2611.06040.html)

## Key numbers

- **4.27M**: It produces 4.27M QA pairs across five capability groups
- **6,525**: USI-Bench evaluates the resulting capabilities with 6,525 held-out QA pairs
- **18.0**: while UPR adds 18.0 points in scene description under matched training data

## Abstract

Underwater spatial reasoning depends on object boundaries and geometric cues that are obscured by scattering, color distortion, and low visibility. Existing underwater resources capture these cues through images, annotations, and depth maps, but provide fragmented supervision for vision-language models. We present a spatial data engine that connects visual appearance with object-level geometry through enhancement, 2D and geometry-assisted question generation, and expert filtering. It produces 4.27M QA pairs across five capability groups. We use this corpus to train USI-Brain and introduce an Underwater Prior-guided Rectifier (UPR) that applies gated corrections to visual features. USI-Bench evaluates the resulting capabilities with 6,525 held-out QA pairs. On its 15 task metrics, USI-Brain and USI-Brain + UPR improve over Qwen3-VL-8B-Instruct by 5.76 and 7.63 points on average, respectively, using signed differences on 100-point scales. Ablations show that geometry-assisted supervision improves depth and ordering by 4.8 and 6.1 points with UPR disabled, while UPR adds 18.0 points in scene description under matched training data. Both variants use raw images at evaluation. Across NautData, UWBench, and MarineEval, USI-Brain achieves the best or joint-best score on 28 of 32 metrics. Model weights and USI-Bench will be released at https://github.com/SKY-Zhu-91/USI-Brain.

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Notes written by AI from the paper, numbers copied from the abstract. Check the paper before you rely on them. From AI Papers Daily.