SPRITE: Spatial Reasoning and Embodied Intelligence for VLMs

SPRITE is a spatial reasoning dataset for Vision-Language Models (VLMs) developed by zhihelu and released in early 2026. It provides image-text pairs designed to improve embodied intelligence by balancing linguistic diversity with computational precision, as detailed in Arxiv paper 2512.16237.

Use Cases

• Training Vision-Language Models (VLMs) using image-text pairs to improve spatial reasoning
• Benchmarking embodied AI agents on their ability to interpret precise spatial relationships
• Fine-tuning models for object-relation mapping based on linguistically diverse spatial descriptions

Strengths

• Apache 2.0 license allows for broad research and commercial application
• Addresses the computational imprecision found in manual spatial annotations
• Provides a scalable alternative to rigid template-based spatial datasets

Limitations

• Unknown record count and file size in current metadata
• Lack of explicit column schema documentation in the primary repository

Provenance

Source

zhihelu (Arxiv 2512.16237)

Collection Method

Hybrid approach combining scalable structures with linguistically diverse annotations

Freshness

Last updated January 2026.