# Enhancing Large Vision-Language Models via Quantized Grounded Reasoning

**Authors:** Reed, Barnes
**Year:** 2025
**DOI:** 10.20944/preprints202510.2397.v1
**URL:** https://doi.org/10.20944/preprints202510.2397.v1

## Abstract

Large Vision-Language Models (LVLMs) have achieved strong results in general visual understanding but remain limited in fine-grained visual reasoning. This paper introduces LVLM-GR, a framework designed to improve detailed visual grounding and robust multimodal reasoning. The proposed Visual Concept Quantizer (VCQ) encodes images into discrete visual tokens through context-aware pooling and a semantic hierarchical codebook, effectively preserving fine-grained semantics. These visual tokens are then aligned with language via a lightweight Grounded Reasoning Adapter (GRA) based on LoRA-tuned adaptation atop a frozen LLaVA 1.5 13B backbone. Experiments on GQA, RefCOCO+, and A-OKVQA show that LVLM-GR achieves superior performance in fine-grained visual understanding, reasoning, and grounding, highlighting its potential for complex multimodal reasoning tasks in material-level and detailed visual analysis.

## Citation Details

Enhancing Large Vision-Language Models via Quantized Grounded Reasoning
**Authors**: Reed, Barnes
**Year**: 2025
**DOI**: 10.20944/preprints202510.2397.v1
**URL**: https://doi.org/10.20944/preprints202510.2397.v1

**Abstract**: Large Vision-Language Models (LVLMs) have achieved strong results in general visual understanding but remain limited in fine-grained visual reasoning. This paper introduces LVLM-GR, a framework designed to improve detailed visual grounding and robust multimodal reasoning. The proposed Visual Concept Quantizer (VCQ) encodes images into discrete visual tokens through context-aware pooling and a semantic hierarchical codebook, effectively preserving fine-grained semantics. These visual tokens are then aligned with language via a lightweight Grounded Reasoning Adapter (GRA) based on LoRA-tuned adaptation atop a frozen LLaVA 1.5 13B backbone. Experiments on GQA, RefCOCO+, and A-OKVQA show that LVLM-GR achieves superior performance in fine-grained visual understanding, reasoning, and grounding, highlighting its potential for complex multimodal reasoning tasks in material-level and detailed visual analysis.  # Truncate if very long

---
*Extracted from citation research database - all 43 citations available*
