# V2PE: Improving Multimodal Long-Context Capability of Vision-Language Models with Variable Visual Position Encoding

arXiv: https://arxiv.org/abs/2412.09616 · HTML: https://mlsanity.com/paper/2412.09616/

## Key facts

- Cited by 28 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 129 of its 161 references that are in the map.
- First posted on arXiv 12 Dec 2024.

## 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, 30 of 129)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [LLaMA: Open and Efficient Foundation Language Models](https://mlsanity.com/paper/2302.13971/) (2023)
- [Llama 2: Open Foundation and Fine-Tuned Chat Models](https://mlsanity.com/paper/2307.09288/) (2023)
- [Chain-of-Thought Prompting Elicits Reasoning in Large Language Models](https://mlsanity.com/paper/2201.11903/) (2022)
- [Judging LLM-as-a-Judge with MT-Bench and Chatbot Arena](https://mlsanity.com/paper/2306.05685/) (2023)
- [BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Models](https://mlsanity.com/paper/2301.12597/) (2023)
- [Mamba: Linear-Time Sequence Modeling with Selective State Spaces](https://mlsanity.com/paper/2312.00752/) (2023)
- [RoFormer: Enhanced Transformer with Rotary Position Embedding](https://mlsanity.com/paper/2104.09864/) (2021)
- [Improved Baselines with Visual Instruction Tuning](https://mlsanity.com/paper/2310.03744/) (2023)
- [Flamingo: a Visual Language Model for Few-Shot Learning](https://mlsanity.com/paper/2204.14198/) (2022)
- [BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generation](https://mlsanity.com/paper/2201.12086/) (2022)
- [Qwen2-VL: Enhancing Vision-Language Model's Perception of the World at Any Resolution](https://mlsanity.com/paper/2409.12191/) (2024)
- [OPT: Open Pre-trained Transformer Language Models](https://mlsanity.com/paper/2205.01068/) (2022)
- [Longformer: The Long-Document Transformer](https://mlsanity.com/paper/2004.05150/) (2020)
- [Qwen Technical Report](https://mlsanity.com/paper/2309.16609/) (2023)
- [Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context](https://mlsanity.com/paper/2403.05530/) (2024)
- [Sigmoid Loss for Language Image Pre-Training](https://mlsanity.com/paper/2303.15343/) (2023)
- [InstructBLIP: Towards General-purpose Vision-Language Models with Instruction Tuning](https://mlsanity.com/paper/2305.06500/) (2023)
- [InternVL: Scaling up Vision Foundation Models and Aligning for Generic Visual-Linguistic Tasks](https://mlsanity.com/paper/2312.14238/) (2023)
- [Efficiently Modeling Long Sequences with Structured State Spaces](https://mlsanity.com/paper/2111.00396/) (2021)
- [Qwen-VL: A Versatile Vision-Language Model for Understanding, Localization, Text Reading, and Beyond](https://mlsanity.com/paper/2308.12966/) (2023)
- [Efficient Streaming Language Models with Attention Sinks](https://mlsanity.com/paper/2309.17453/) (2023)
- [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)
- [Learn to Explain: Multimodal Reasoning via Thought Chains for Science Question Answering](https://mlsanity.com/paper/2209.09513/) (2022)
- [Phi-3 Technical Report: A Highly Capable Language Model Locally on Your Phone](https://mlsanity.com/paper/2404.14219/) (2024)

## Built on by (most cited first, 28 of 28)

- [InternVL3: Exploring Advanced Training and Test-Time Recipes for Open-Source Multimodal Models](https://mlsanity.com/paper/2504.10479/) (2025)
- [MMLongBench: Benchmarking Long-Context Vision-Language Models Effectively and Thoroughly](https://mlsanity.com/paper/2505.10610/) (2025)
- [ReasonMap: Towards Fine-Grained Visual Reasoning from Transit Maps](https://mlsanity.com/paper/2505.18675/) (2025)
- [Revisiting Multimodal Positional Encoding in Vision-Language Models](https://mlsanity.com/paper/2510.23095/) (2025)
- [LoVR: A Benchmark for Long Video Retrieval in Multimodal Contexts](https://mlsanity.com/paper/2505.13928/) (2025)
- [LingoLoop Attack: Trapping MLLMs via Linguistic Context and State Entrapment into Endless Loops](https://mlsanity.com/paper/2506.14493/) (2025)
- [Self-alignment of Large Video Language Models with Refined Regularized Preference Optimization](https://mlsanity.com/paper/2504.12083/) (2025)
- [An Empirical Study on How Video-LLMs Answer Video Questions](https://mlsanity.com/paper/2508.15360/) (2025)
- [Improving GUI Grounding with Explicit Position-to-Coordinate Mapping](https://mlsanity.com/paper/2510.03230/) (2025)
- [VideoNorms: Benchmarking Cultural Awareness of Video Language Models](https://mlsanity.com/paper/2510.08543/) (2025)
- [VisChainBench: A Benchmark for Multi-Turn, Multi-Image Visual Reasoning Beyond Language Priors](https://mlsanity.com/paper/2512.06759/) (2025)
- [ID-Align: RoPE-Conscious Position Remapping for Dynamic High-Resolution Adaptation in Vision-Language Models](https://mlsanity.com/paper/2505.21465/) (2025)
- [Mitigating Coordinate Prediction Bias from Positional Encoding Failures](https://mlsanity.com/paper/2510.22102/) (2025)
- [How to Train Your Long-Context Visual Document Model](https://mlsanity.com/paper/2602.15257/) (2026)
- [MODIX: A Training-Free Multimodal Information-Driven Positional Index Scaling for Vision-Language Models](https://mlsanity.com/paper/2604.12537/) (2026)
- [ZAYA1-VL-8B Technical Report](https://mlsanity.com/paper/2605.08560/) (2026)
- [OMEGA: Optimized Multimodal Position Encoding Index Derivation with Global Adaptive Scaling for Vision-Language Models](https://mlsanity.com/paper/2511.00821/) (2025)
- [CASA: Cross-Attention over Self-Attention for Efficient Vision-Language Fusion](https://mlsanity.com/paper/2512.19535/) (2025)
- [T2SGrid: Temporal-to-Spatial Gridification for Video Temporal Grounding](https://mlsanity.com/paper/2603.06973/) (2026)
- [Beyond Sequential Distance: Inter-Modal Distance Invariant Position Encoding](https://mlsanity.com/paper/2603.10863/) (2026)
- [Why Do Vision Language Models Struggle To Recognize Human Emotions?](https://mlsanity.com/paper/2604.15280/) (2026)
- [MMLongCite: A Benchmark for Evaluating Fidelity of Long-Context Vision-Language Models](https://mlsanity.com/paper/2510.13276/) (2025)
- [Decoding the Pulse of Reasoning VLMs in Multi-Image Understanding Tasks](https://mlsanity.com/paper/2603.04676/) (2026)
- [Internalized Reasoning for Long-Context Visual Document Understanding](https://mlsanity.com/paper/2604.02371/) (2026)
- [Mitigating Mask Prior Drift and Positional Attention Collapse in Large Diffusion Vision-Language Models](https://mlsanity.com/paper/2605.14530/) (2026)
- [MotionEnhancer: Leveraging Video Diffusion for Motion-Enhanced Vision-Language Models](https://mlsanity.com/paper/2606.06853/) (2026)
- [RIG-RoPE: Relation-Stratified Multimodal Attention with Instance-Local Rotary Geometry and Representation-Aware Traversal Coordinates](https://mlsanity.com/paper/2608.05154/) (2026)
- [Mr3D-VL: A generalist vision language foundation model for Multiparametric 3D Magnetic Resonance Imaging](https://mlsanity.com/paper/2608.12689/) (2026)

## Abstract (arXiv)

Vision-Language Models (VLMs) have shown promising capabilities in handling various multimodal tasks, yet they struggle in long-context scenarios, particularly in tasks involving videos, high-resolution images, or lengthy image-text documents. In our work, we first conduct an empirical analysis of the long-context capabilities of VLMs using our augmented long-context multimodal datasets. Our findings reveal that directly applying the positional encoding mechanism used for textual tokens to visual tokens is suboptimal, and VLM performance degrades sharply when the position encoding exceeds the model's context window. To address this, we propose Variable Visual Position Encoding (V2PE), a novel positional encoding approach that employs variable and smaller increments for visual tokens, enabling more efficient management of long multimodal sequences. Our experiments demonstrate the effectiveness of V2PE to enhances VLMs' ability to effectively understand and reason over long multimodal contexts. We further integrate V2PE with our augmented long-context multimodal datasets to fine-tune the open-source VLM, InternVL2. The fine-tuned model achieves strong performance on both standard and long-context multimodal tasks. Notably, when the sequence length of the training dataset is increased to 256K tokens, the model is capable of processing multimodal sequences up to 1M tokens, highlighting its potential for real-world long-context applications.