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Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

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Mini-Gemini: Mining the Potential of Multi-Modality Vision Language Models.

Yanwei Li, Yuechen Zhang, Chengyao Wang

IEEE Transactions on Pattern Analysis and Machine Intelligence

|November 26, 2025

Summary

This summary is machine-generated.

Mini-Gemini enhances multi-modality Vision Language Models (VLMs) by refining high-resolution visual tokens and using high-quality data. This framework boosts image understanding, reasoning, and generation, outperforming existing models.

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Area of Science:

Artificial Intelligence
Computer Vision
Natural Language Processing

Background:

Vision Language Models (VLMs) show promise but lag behind advanced proprietary models in complex tasks.
Existing VLMs struggle with high-resolution image understanding and reasoning-driven generation.
A performance gap exists between open-source and proprietary models in cross-modal tasks.

Purpose of the Study:

To introduce Mini-Gemini, a framework for enhancing VLM performance.
To address limitations in visual token representation and data quality for VLMs.
To bridge the performance gap between open-source and proprietary VLMs.

Main Methods:

Utilized an additional visual encoder for high-resolution refinement without increasing token count.
Constructed a high-quality dataset to improve image comprehension and reasoning.
Integrated Mini-Gemini with various dense and Mixture-of-Experts (MoE) Large Language Models (LLMs).

Main Results:

Achieved leading zero-shot performance on several benchmarks, surpassing private models.
Attained 80.6% accuracy on the MMB benchmark and 74.1% on TextVQA.
Demonstrated consistent improvement with stronger LLMs, visual encoders, and data.

Conclusions:

Mini-Gemini effectively enhances VLMs for image understanding, reasoning, and generation.
The framework successfully narrows the performance gap with advanced proprietary models.
Mini-Gemini offers a scalable and effective approach for advancing open-source VLM capabilities.