# Revisiting Multimodal Positional Encoding in Vision-Language Models

arXiv: https://arxiv.org/abs/2510.23095 · HTML: https://mlsanity.com/paper/2510.23095/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- Placed in MLLM training and visual instruction tuning, one of 16 directions in Multimodal LLMs / vision-language models.
- Builds on 27 of its 34 references that are in the map.
- First posted on arXiv 27 Oct 2025.

## Placed in

- 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, 27 of 27)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Qwen3 Technical Report](https://mlsanity.com/paper/2505.09388/) (2025)
- [Qwen2.5-VL Technical Report](https://mlsanity.com/paper/2502.13923/) (2025)
- [RoFormer: Enhanced Transformer with Rotary Position Embedding](https://mlsanity.com/paper/2104.09864/) (2021)
- [Qwen2-VL: Enhancing Vision-Language Model's Perception of the World at Any Resolution](https://mlsanity.com/paper/2409.12191/) (2024)
- [MMBench: Is Your Multi-modal Model an All-around Player?](https://mlsanity.com/paper/2307.06281/) (2023)
- [MMMU: A Massive Multi-discipline Multimodal Understanding and Reasoning Benchmark for Expert AGI](https://mlsanity.com/paper/2311.16502/) (2023)
- [Towards VQA Models That Can Read](https://mlsanity.com/paper/1904.08920/) (2019)
- [ChartQA: A Benchmark for Question Answering about Charts with Visual and Logical Reasoning](https://mlsanity.com/paper/2203.10244/) (2022)
- [Video-MME: The First-Ever Comprehensive Evaluation Benchmark of Multi-modal LLMs in Video Analysis](https://mlsanity.com/paper/2405.21075/) (2024)
- [DocVQA: A Dataset for VQA on Document Images](https://mlsanity.com/paper/2007.00398/) (2020)
- [MVBench: A Comprehensive Multi-modal Video Understanding Benchmark](https://mlsanity.com/paper/2311.17005/) (2023)
- [Are We on the Right Way for Evaluating Large Vision-Language Models?](https://mlsanity.com/paper/2403.20330/) (2024)
- [A Diagram Is Worth A Dozen Images](https://mlsanity.com/paper/1603.07396/) (2016)
- [YaRN: Efficient Context Window Extension of Large Language Models](https://mlsanity.com/paper/2309.00071/) (2023)
- [BLINK: Multimodal Large Language Models Can See but Not Perceive](https://mlsanity.com/paper/2404.12390/) (2024)
- [OCRBench: On the Hidden Mystery of OCR in Large Multimodal Models](https://mlsanity.com/paper/2305.07895/) (2023)
- [InfographicVQA](https://mlsanity.com/paper/2104.12756/) (2021)
- [LVBench: An Extreme Long Video Understanding Benchmark](https://mlsanity.com/paper/2406.08035/) (2024)
- [OmniGen2: Towards Instruction-Aligned Multimodal Generation](https://mlsanity.com/paper/2506.18871/) (2025)
- [STAR: A Benchmark for Situated Reasoning in Real-World Videos](https://mlsanity.com/paper/2405.09711/) (2024)
- [Mogao: An Omni Foundation Model for Interleaved Multi-Modal Generation](https://mlsanity.com/paper/2505.05472/) (2025)
- [Apollo: An Exploration of Video Understanding in Large Multimodal Models](https://mlsanity.com/paper/2412.10360/) (2024)
- [VideoRoPE: What Makes for Good Video Rotary Position Embedding?](https://mlsanity.com/paper/2502.05173/) (2025)
- [V2PE: Improving Multimodal Long-Context Capability of Vision-Language Models with Variable Visual Position Encoding](https://mlsanity.com/paper/2412.09616/) (2024)
- [VRoPE: Rotary Position Embedding for Video Large Language Models](https://mlsanity.com/paper/2502.11664/) (2025)
- [Circle-RoPE: Cone-like Decoupled Rotary Positional Embedding for Large Vision-Language Models](https://mlsanity.com/paper/2505.16416/) (2025)

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

- [Wan-Image: Pushing the Boundaries of Generative Visual Intelligence](https://mlsanity.com/paper/2604.19858/) (2026)
- [DVGT-2: Vision-Geometry-Action Model for Autonomous Driving at Scale](https://mlsanity.com/paper/2604.00813/) (2026)
- [FocusUI: Efficient UI Grounding via Position-Preserving Visual Token Selection](https://mlsanity.com/paper/2601.03928/) (2026)
- [EchoingPixels: Aliasing-Resistant Joint Token Reduction for Audio-Visual LLMs](https://mlsanity.com/paper/2512.10324/) (2025)
- [Think-as-You-See: Streaming Chain-of-Thought Reasoning for Large Vision-Language Models](https://mlsanity.com/paper/2603.02872/) (2026)
- [Beyond Surface Artifacts: Capturing Shared Latent Forgery Knowledge Across Modalities](https://mlsanity.com/paper/2604.07763/) (2026)
- [Qwen-3D: A Generalist 3D Vision-Language Model for Spatial Understanding](https://mlsanity.com/paper/2608.02980/) (2026)
- [Linking Perception, Confidence and Accuracy in MLLMs](https://mlsanity.com/paper/2603.12149/) (2026)
- [Uncovering Understanding-Generation Synergy in Native Unified Multimodal Models: From Representation, Task to System](https://mlsanity.com/paper/2609.01607/) (2026)
- [Beyond Sequential Distance: Inter-Modal Distance Invariant Position Encoding](https://mlsanity.com/paper/2603.10863/) (2026)
- [Unveiling the Visual Counting Bottleneck in Vision-Language Models](https://mlsanity.com/paper/2605.30170/) (2026)
- [InCaRPose: In-Cabin Relative Camera Pose Estimation Model and Dataset](https://mlsanity.com/paper/2604.03814/) (2026)
- [RIG-RoPE: Relation-Stratified Multimodal Attention with Instance-Local Rotary Geometry and Representation-Aware Traversal Coordinates](https://mlsanity.com/paper/2608.05154/) (2026)
- [Beyond Compression: Training Latent Representations for Stable Long-Horizon Rollout in Neural Surrogate Solvers](https://mlsanity.com/paper/2609.30198/) (2026)
- [EchoDino: A pediatric foundation model for transferable echocardiographic analysis across the lifespan](https://mlsanity.com/paper/2610.05603/) (2026)
- [GNFormer: Gaussian Neural Field Transformer for Efficient Camera-based Occupancy Prediction](https://mlsanity.com/paper/2610.15522/) (2026)

## Abstract (arXiv)

Multimodal position encoding is essential for vision-language models, yet there has been little systematic investigation into multimodal position encoding. We conduct a comprehensive analysis of multimodal Rotary Positional Embedding (RoPE) by examining its two core components: position design and frequency allocation. Through extensive experiments, we identify three key guidelines: positional coherence, full frequency utilization, and preservation of textual priors-ensuring unambiguous layout, rich representation, and faithful transfer from the pre-trained LLM. Based on these insights, we propose Multi-Head RoPE (MHRoPE) and MRoPE-Interleave (MRoPE-I), two simple and plug-and-play variants that require no architectural changes. Our methods consistently outperform existing approaches across diverse benchmarks, with significant improvements in both general and fine-grained multimodal understanding. Code will be avaliable at https://github.com/JJJYmmm/Multimodal-RoPEs.