# MPCAR: Multi-Perspective Contextual Augmentation for Enhanced Visual Reasoning in Large Vision-Language Models

arXiv: https://arxiv.org/abs/2508.12400 · HTML: https://mlsanity.com/paper/2508.12400/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Placed in Multimodal reasoning, one of 17 directions in LLM reasoning.
- Builds on 16 of its 27 references that are in the map.
- First posted on arXiv 17 Aug 2025.

## Placed in

- LLM reasoning / [Multimodal reasoning](https://mlsanity.com/lr/multimodal-reasoning/)
- Multimodal LLMs / vision-language models / [VLM benchmarks and visual reasoning](https://mlsanity.com/mllm/vlm-benchmarks-and-visual-reasoning/)

## Builds on (most cited first, 16 of 16)

- [A Systematic Survey of Prompt Engineering in Large Language Models: Techniques and Applications](https://mlsanity.com/paper/2402.07927/) (2024)
- [Complexity-Based Prompting for Multi-Step Reasoning](https://mlsanity.com/paper/2210.00720/) (2022)
- [LVLM-eHub: A Comprehensive Evaluation Benchmark for Large Vision-Language Models](https://mlsanity.com/paper/2306.09265/) (2023)
- [Visual In-Context Learning for Large Vision-Language Models](https://mlsanity.com/paper/2402.11574/) (2024)
- [Thread of Thought Unraveling Chaotic Contexts](https://mlsanity.com/paper/2311.08734/) (2023)
- [Efficient Inference for Large Reasoning Models: A Survey](https://mlsanity.com/paper/2503.23077/) (2025)
- [Towards Robust Ranker for Text Retrieval](https://mlsanity.com/paper/2206.08063/) (2022)
- [Benchmarking Large Vision-Language Models via Directed Scene Graph for Comprehensive Image Captioning](https://mlsanity.com/paper/2412.08614/) (2024)
- [Draw ALL Your Imagine: A Holistic Benchmark and Agent Framework for Complex Instruction-based Image Generation](https://mlsanity.com/paper/2505.24787/) (2025)
- [Cross-Modal Safety Mechanism Transfer in Large Vision-Language Models](https://mlsanity.com/paper/2410.12662/) (2024)
- [Leveraging Large Language Models with Chain-of-Thought and Prompt Engineering for Traffic Crash Severity Analysis and Inference](https://mlsanity.com/paper/2408.04652/) (2024)
- [Emulating Retrieval Augmented Generation via Prompt Engineering for Enhanced Long Context Comprehension in LLMs](https://mlsanity.com/paper/2502.12462/) (2025)
- [Making Large Vision Language Models to be Good Few-shot Learners](https://mlsanity.com/paper/2408.11297/) (2024)
- [Zero-Shot Scene Understanding for Automatic Target Recognition Using Large Vision-Language Models](https://mlsanity.com/paper/2501.07396/) (2025)
- [Prompt Engineering Guidelines for Using Large Language Models in Requirements Engineering](https://mlsanity.com/paper/2507.03405/) (2025)
- [Large Vision-Language Models for Remote Sensing Visual Question Answering](https://mlsanity.com/paper/2411.10857/) (2024)

## Built on by (most cited first, 1 of 1)

- [MAG: MAnifold Guided Semi-Supervised Multi-modal In-Context Learning](https://mlsanity.com/paper/2608.12724/) (2026)

## Abstract (arXiv)

Despite significant advancements, Large Vision-Language Models (LVLMs) continue to face challenges in complex visual reasoning tasks that demand deep contextual understanding, multi-angle analysis, or meticulous detail recognition. Existing approaches often rely on single-shot image encoding and prompts, limiting their ability to fully capture nuanced visual information. Inspired by the notion that strategically generated "additional" information can serve as beneficial contextual augmentation, we propose Multi-Perspective Contextual Augmentation for Reasoning (MPCAR), a novel inference-time strategy designed to enhance LVLM performance. MPCAR operates in three stages: first, an LVLM generates N diverse and complementary descriptions or preliminary reasoning paths from various angles; second, these descriptions are intelligently integrated with the original question to construct a comprehensive context-augmented prompt; and finally, this enriched prompt guides the ultimate LVLM for deep reasoning and final answer generation. Crucially, MPCAR achieves these enhancements without requiring any fine-tuning of the underlying LVLM's parameters. Extensive experiments on challenging Visual Question Answering (VQA) datasets, including GQA, VQA-CP v2, and ScienceQA (Image-VQA), demonstrate that MPCAR consistently outperforms established baseline methods. Our quantitative results show significant accuracy gains, particularly on tasks requiring robust contextual understanding, while human evaluations confirm improved coherence and completeness of the generated answers. Ablation studies further highlight the importance of diverse prompt templates and the number of generated perspectives. This work underscores the efficacy of leveraging LVLMs' inherent generative capabilities to enrich input contexts, thereby unlocking their latent reasoning potential for complex multimodal tasks.