# Circle-RoPE: Cone-like Decoupled Rotary Positional Embedding for Large Vision-Language Models

arXiv: https://arxiv.org/abs/2505.16416 · HTML: https://mlsanity.com/paper/2505.16416/

## Key facts

- Cited by 14 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 32 of its 40 references that are in the map.
- First posted on arXiv 22 May 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, 30 of 32)

- [Visual Instruction Tuning](https://mlsanity.com/paper/2304.08485/) (2023)
- [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)
- [Phi-3 Technical Report: A Highly Capable Language Model Locally on Your Phone](https://mlsanity.com/paper/2404.14219/) (2024)
- [Towards VQA Models That Can Read](https://mlsanity.com/paper/1904.08920/) (2019)
- [MathVista: Evaluating Mathematical Reasoning of Foundation Models in Visual Contexts](https://mlsanity.com/paper/2310.02255/) (2023)
- [Expanding Performance Boundaries of Open-Source Multimodal Models with Model, Data, and Test-Time Scaling](https://mlsanity.com/paper/2412.05271/) (2024)
- [ChartQA: A Benchmark for Question Answering about Charts with Visual and Logical Reasoning](https://mlsanity.com/paper/2203.10244/) (2022)
- [MiniCPM-V: A GPT-4V Level MLLM on Your Phone](https://mlsanity.com/paper/2408.01800/) (2024)
- [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)
- [Emu3: Next-Token Prediction is All You Need](https://mlsanity.com/paper/2409.18869/) (2024)
- [DeepSeek-VL2: Mixture-of-Experts Vision-Language Models for Advanced Multimodal Understanding](https://mlsanity.com/paper/2412.10302/) (2024)
- [MMMU-Pro: A More Robust Multi-discipline Multimodal Understanding Benchmark](https://mlsanity.com/paper/2409.02813/) (2024)
- [VLMEvalKit: An Open-Source Toolkit for Evaluating Large Multi-Modality Models](https://mlsanity.com/paper/2407.11691/) (2024)
- [mPLUG-Owl3: Towards Long Image-Sequence Understanding in Multi-Modal Large Language Models](https://mlsanity.com/paper/2408.04840/) (2024)
- [LLaVA-Plus: Learning to Use Tools for Creating Multimodal Agents](https://mlsanity.com/paper/2311.05437/) (2023)
- [Ovis: Structural Embedding Alignment for Multimodal Large Language Model](https://mlsanity.com/paper/2405.20797/) (2024)
- [Sa2VA: Marrying SAM2 with MLLM for Dense Grounded Understanding of Images and Videos](https://mlsanity.com/paper/2501.04001/) (2025)
- [MAmmoTH-VL: Eliciting Multimodal Reasoning with Instruction Tuning at Scale](https://mlsanity.com/paper/2412.05237/) (2024)
- [Transformer-Based Visual Segmentation: A Survey](https://mlsanity.com/paper/2304.09854/) (2023)
- [Baichuan-Omni-1.5 Technical Report](https://mlsanity.com/paper/2501.15368/) (2025)
- [VideoRoPE: What Makes for Good Video Rotary Position Embedding?](https://mlsanity.com/paper/2502.05173/) (2025)
- [Scalable Vision Language Model Training via High Quality Data Curation](https://mlsanity.com/paper/2501.05952/) (2025)
- [The Scalability of Simplicity: Empirical Analysis of Vision-Language Learning with a Single Transformer](https://mlsanity.com/paper/2504.10462/) (2025)
- [Pixel-SAIL: Single Transformer For Pixel-Grounded Understanding](https://mlsanity.com/paper/2504.10465/) (2025)
- [On Path to Multimodal Generalist: General-Level and General-Bench](https://mlsanity.com/paper/2505.04620/) (2025)

## Built on by (most cited first, 14 of 14)

- [Revisiting Multimodal Positional Encoding in Vision-Language Models](https://mlsanity.com/paper/2510.23095/) (2025)
- [VLA-ATTC: Adaptive Test-Time Compute for VLA Models with Relative Action Critic Model](https://mlsanity.com/paper/2605.01194/) (2026)
- [Spiral RoPE: Rotate Your Rotary Positional Embeddings in the 2D Plane](https://mlsanity.com/paper/2602.03227/) (2026)
- [Improving GUI Grounding with Explicit Position-to-Coordinate Mapping](https://mlsanity.com/paper/2510.03230/) (2025)
- [Sentinel-VLA: A Metacognitive VLA Model with Active Status Monitoring for Dynamic Reasoning and Error Recovery](https://mlsanity.com/paper/2605.01191/) (2026)
- [RAP: KV-Cache Compression via RoPE-Aligned Pruning](https://mlsanity.com/paper/2602.02599/) (2026)
- [SoPE: Spherical Coordinate-Based Positional Embedding for Enhancing Spatial Perception of 3D LVLMs](https://mlsanity.com/paper/2602.22716/) (2026)
- [MODIX: A Training-Free Multimodal Information-Driven Positional Index Scaling for Vision-Language Models](https://mlsanity.com/paper/2604.12537/) (2026)
- [AttAnchor: Guiding Cross-Modal Token Alignment in VLMs with Attention Anchors](https://mlsanity.com/paper/2509.23109/) (2025)
- [Beyond Sequential Distance: Inter-Modal Distance Invariant Position Encoding](https://mlsanity.com/paper/2603.10863/) (2026)
- [DPPE: Rethinking Camera-Based Positional Encoding for Scaling Multi-View Transformers](https://mlsanity.com/paper/2606.31585/) (2026)
- [Tinted Frames: Question Framing Blinds Vision-Language Models](https://mlsanity.com/paper/2603.19203/) (2026)
- [Mitigating Mask Prior Drift and Positional Attention Collapse in Large Diffusion Vision-Language Models](https://mlsanity.com/paper/2605.14530/) (2026)
- [RIG-RoPE: Relation-Stratified Multimodal Attention with Instance-Local Rotary Geometry and Representation-Aware Traversal Coordinates](https://mlsanity.com/paper/2608.05154/) (2026)

## Abstract (arXiv)

Rotary Position Embedding (RoPE) is widely adopted in large language models, but when applied to vision-language models (VLMs) it couples text and image position indices and can introduce spurious cross-modal relative-position bias. We propose Per-Token Distance (PTD) to quantify cross-modal positional disentanglement, and prove that PTD = 0 is a sufficient condition to eliminate the geometric attention bias induced by RoPE. Guided by this criterion, we introduce Circle-RoPE, which remaps 2D image-token coordinates onto an annulus orthogonal to the text position axis, yielding a cone-like geometry where each text token is equidistant to all image tokens while preserving intra-image spatial structure. We further propose Alternating Geometry Encoding (AGE) to combine complementary geometric priors by alternating the decoupled geometry of Circle-RoPE and the grid-based prior of standard RoPE across layers. This design enables cross-modal positional disentanglement while preserving fine-grained intra-image spatial structure. Experiments on diverse VLM backbones and multimodal benchmarks show consistent gains in spatial grounding and visual reasoning. The code is available at https://github.com/lose4578/CircleRoPE.