View PDF ↗
PDF Viewer
Open Full PDF

BUILDER'S SANDBOX

Build This Paper

Use an AI coding agent to implement this research.

OpenAI Codex
OpenAI CodexAI Agent

Lightweight coding agent in your terminal.

Claude Code
Claude CodeAI Agent

Agentic coding tool for terminal workflows.

AntiGravity IDE
AntiGravity IDEScaffolding

AI agent mindset installer and workflow scaffolder.

Cursor
CursorIDE

AI-first code editor built on VS Code.

VS Code
VS CodeIDE

Free, open-source editor by Microsoft.

Estimated $9K - $13K over 6-10 weeks.

See exactly what it costs to build this -- with 3 comparable funded startups.

7-day free trial. Cancel anytime.

Discover the researchers behind this paper and find similar experts.

7-day free trial. Cancel anytime.

References (54)

[1]
MCA-LLaVA: Manhattan Causal Attention for Reducing Hallucination in Large Vision-Language Models
2025Qiyan Zhao, Xiaofeng Zhang et al.
[2]
CoMemo: LVLMs Need Image Context with Image Memory
2025Shi Liu, Weijie Su et al.
[3]
AdaToken-3D: Dynamic Spatial Gating for Efficient 3D Large Multimodal-Models Reasoning
2025Kai Zhang, Xingyu Chen et al.
[4]
Qwen3 Technical Report
2025An Yang, Anfeng Li et al.
[5]
STDArm: Transferring Visuomotor Policies From Static Data Training to Dynamic Robot Manipulation
2025Yifan Duan, Heng Li et al.
[6]
Ross3D: Reconstructive Visual Instruction Tuning with 3D-Awareness
2025Haochen Wang, Yucheng Zhao et al.
[7]
VideoRoPE: What Makes for Good Video Rotary Position Embedding?
2025Xilin Wei, Xiaoran Liu et al.
[8]
Mitigating Object Hallucinations in Large Vision-Language Models via Attention Calibration
2025Younan Zhu, Linwei Tao et al.
[9]
Massive Values in Self-Attention Modules are the Key to Contextual Knowledge Understanding
2025Mingyu Jin, Kai Mei et al.
[10]
3D-LLaVA: Towards Generalist 3D LMMs with Omni Superpoint Transformer
2025Jiajun Deng, Tianyu He et al.
[11]
LSceneLLM: Enhancing Large 3D Scene Understanding Using Adaptive Visual Preferences
2024Hongyan Zhi, Peihao Chen et al.
[12]
Video-3D LLM: Learning Position-Aware Video Representation for 3D Scene Understanding
2024Duo Zheng, Shijia Huang et al.
[13]
Seeing Clearly by Layer Two: Enhancing Attention Heads to Alleviate Hallucination in LVLMs
2024Xiaofeng Zhang, Yihao Quan et al.
[14]
Mitigating Object Hallucination via Concentric Causal Attention
2024Yun Xing, Yiheng Li et al.
[15]
Pixtral 12B
2024Pravesh Agrawal, Szymon Antoniak et al.
[16]
Round and Round We Go! What makes Rotary Positional Encodings useful?
2024Federico Barbero, Alex Vitvitskyi et al.
[17]
LLaVA-3D: A Simple yet Effective Pathway to Empowering LMMs with 3D-awareness
2024Chenming Zhu, Tai Wang et al.
[18]
Qwen2-VL: Enhancing Vision-Language Model's Perception of the World at Any Resolution
2024Peng Wang, Shuai Bai et al.
[19]
TC-LLaVA: Rethinking the Transfer from Image to Video Understanding with Temporal Considerations
2024Mingze Gao, Jingyu Liu et al.
[20]
MiniCPM-V: A GPT-4V Level MLLM on Your Phone
2024Yuan Yao, Tianyu Yu et al.

Showing 20 of 54 references

Founder's Pitch

"C^2RoPE enhances 3D multimodal models with a novel positional encoding method for improved spatial continuity and causal reasoning in visual tasks."

Multimodal AIScore: 6View PDF ↗

Commercial Viability Breakdown

0-10 scale

High Potential

1/4 signals

2.5

Quick Build

1/4 signals

2.5

Series A Potential

1/4 signals

2.5

Sources used for this analysis

arXiv Paper

Full-text PDF analysis of the research paper

GitHub Repository

Code availability, stars, and contributor activity

Citation Network

Semantic Scholar citations and co-citation patterns

Community Predictions

Crowd-sourced unicorn probability assessments

Analysis model: GPT-4o · Last scored: 2/11/2026

Explore the full citation network and related research.

7-day free trial. Cancel anytime.

Understand the commercial significance and market impact.

7-day free trial. Cancel anytime.

Get detailed profiles of the research team.

7-day free trial. Cancel anytime.