# MMCR: Advancing Visual Language Model in Multimodal Multi-Turn Contextual Reasoning

arXiv: https://arxiv.org/abs/2503.18533 · HTML: https://mlsanity.com/paper/2503.18533/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- Placed in Multimodal reasoning, one of 17 directions in LLM reasoning.
- Builds on 7 of its 50 references that are in the map.
- First posted on arXiv 24 Mar 2025.

## Placed in

- LLM reasoning / [Multimodal reasoning](https://mlsanity.com/lr/multimodal-reasoning/)
- Multimodal LLMs / vision-language models / [MLLM training and visual instruction tuning](https://mlsanity.com/mllm/mllm-training-and-visual-instruction-tuning/)

## Builds on (most cited first, 7 of 7)

- [FlashAttention-2: Faster Attention with Better Parallelism and Work Partitioning](https://mlsanity.com/paper/2307.08691/) (2023)
- [Ovis: Structural Embedding Alignment for Multimodal Large Language Model](https://mlsanity.com/paper/2405.20797/) (2024)
- [MuTual: A Dataset for Multi-Turn Dialogue Reasoning](https://mlsanity.com/paper/2004.04494/) (2020)
- [OmniCorpus: A Unified Multimodal Corpus of 10 Billion-Level Images Interleaved with Text](https://mlsanity.com/paper/2406.08418/) (2024)
- [TextBind: Multi-turn Interleaved Multimodal Instruction-following in the Wild](https://mlsanity.com/paper/2309.08637/) (2023)
- [M2Lingual: Enhancing Multilingual, Multi-Turn Instruction Alignment in Large Language Models](https://mlsanity.com/paper/2406.16783/) (2024)
- [Raw Text is All you Need: Knowledge-intensive Multi-turn Instruction Tuning for Large Language Model](https://mlsanity.com/paper/2407.03040/) (2024)

## Built on by (most cited first, 16 of 16)

- [Perception, Reason, Think, and Plan: A Survey on Large Multimodal Reasoning Models](https://mlsanity.com/paper/2505.04921/) (2025)
- [Visionary-R1: Mitigating Shortcuts in Visual Reasoning with Reinforcement Learning](https://mlsanity.com/paper/2505.14677/) (2025)
- [Holistic Evaluation of Multimodal LLMs on Spatial Intelligence](https://mlsanity.com/paper/2508.13142/) (2025)
- [Beyond Seeing: Evaluating Multimodal LLMs on Tool-Enabled Image Perception, Transformation, and Reasoning](https://mlsanity.com/paper/2510.12712/) (2025)
- [Visual Reasoning Tracer: Object-Level Grounded Reasoning Benchmark](https://mlsanity.com/paper/2512.05091/) (2025)
- [A Neuropsychologically Grounded Evaluation of LLM Cognitive Abilities](https://mlsanity.com/paper/2603.02540/) (2026)
- [TPRU: Advancing Temporal and Procedural Understanding in Large Multimodal Models](https://mlsanity.com/paper/2602.18884/) (2026)
- [EpiBench: Benchmarking Multi-turn Research Workflows for Multimodal Agents](https://mlsanity.com/paper/2604.05557/) (2026)
- [Context-Aware Multi-Turn Visual-Textual Reasoning in LVLMs via Dynamic Memory and Adaptive Visual Guidance](https://mlsanity.com/paper/2509.05669/) (2025)
- [LLaVAShield: Safeguarding Multimodal Multi-Turn Dialogues in Vision-Language Models](https://mlsanity.com/paper/2509.25896/) (2025)
- [Multi-turn Conversational AI from Text to Multimodal Interaction: Data, Models, Evaluation, and Open Challenges](https://mlsanity.com/paper/2608.17605/) (2026)
- [Not Search, But Scan: Benchmarking MLLMs on Scan-Oriented Academic Paper Reasoning](https://mlsanity.com/paper/2603.28651/) (2026)
- [AMIGO: Agentic Multi-Image Grounding Oracle Benchmark](https://mlsanity.com/paper/2603.28662/) (2026)
- [StochasT: Learning with Stochastic Turn Depth for Visual Instruction Tuning](https://mlsanity.com/paper/2607.00465/) (2026)
- [Not Just Reason, Not Just Scan: Reinforcement Learning for Proactive Scientific Error Verification over Academic Paper](https://mlsanity.com/paper/2608.26596/) (2026)
- [SciReC: Diagnostic Evaluation of Multimodal, Multi-Turn Relational Reasoning with Adaptive Interaction](https://mlsanity.com/paper/2608.27461/) (2026)

## Abstract (arXiv)

Compared to single-turn dialogue, multi-turn dialogue involving multiple images better aligns with the needs of real-world human-AI interactions. Additionally, as training data, it provides richer contextual reasoning information, thereby guiding the model to achieve better performance. However, existing vision-language models (VLMs) primarily rely on single-turn dialogue training and evaluation benchmarks. In this paper, following the characteristics of human dialogue, such as focused topics and concise, clear content, we present MMCR (Multimodal Multi-turn Contextual Reasoning), a novel dataset comprising: (1) MMCR-310k -- the largest multi-image multi-turn instruction tuning dataset with 310K contextual dialogues, each covering 1-4 images and 4 or 8 dialogue turns; and (2) MMCR-Bench -- a diagnostic benchmark featuring dialogues, spanning 8 domains (Humanities, Natural, Science, Education, etc.) and 40 sub-topics. Extensive evaluations demonstrate that models fine-tuned with MMCR-310k achieve 5.2% higher contextual accuracy on MMCR-Bench, while showing consistent improvements on existing benchmarks (+1.1% on AI2D, +1.2% on MMMU and MMVet). MMCR and prompt engineering will be released publicly.