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 $10K - $14K 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 (26)

[1]
Emergent Introspective Awareness in Large Language Models
2026Jack Lindsey
[2]
Why Language Models Hallucinate
2025A. Kalai, Ofir Nachum et al.
[3]
InternVL3.5: Advancing Open-Source Multimodal Models in Versatility, Reasoning, and Efficiency
2025Weiyun Wang, Zhangwei Gao et al.
[4]
Qwen3 Technical Report
2025An Yang, Anfeng Li et al.
[5]
InternVL3: Exploring Advanced Training and Test-Time Recipes for Open-Source Multimodal Models
2025Jinguo Zhu, Weiyun Wang et al.
[6]
Qwen2.5-VL Technical Report
2025Shuai Bai, Keqin Chen et al.
[7]
Mitigating Hallucinations in Large Vision-Language Models via DPO: On-Policy Data Hold the Key
2025Zhihe Yang, Xufang Luo et al.
[8]
Nullu: Mitigating Object Hallucinations in Large Vision-Language Models via HalluSpace Projection
2024Le Yang, Ziwei Zheng et al.
[9]
Reducing Hallucinations in Vision-Language Models via Latent Space Steering
2024Sheng Liu, Haotian Ye et al.
[10]
Paying More Attention to Image: A Training-Free Method for Alleviating Hallucination in LVLMs
2024Shiping Liu, Kecheng Zheng et al.
[11]
Mitigating Object Hallucinations in Large Vision-Language Models with Assembly of Global and Local Attention
2024Wenbin An, Feng Tian et al.
[12]
Multi-Modal Hallucination Control by Visual Information Grounding
2024Alessandro Favero, L. Zancato et al.
[13]
A Survey on Hallucination in Large Vision-Language Models
2024Hanchao Liu, Wenyuan Xue et al.
[14]
Mitigating Object Hallucinations in Large Vision-Language Models through Visual Contrastive Decoding
2023Sicong Leng, Hang Zhang et al.
[15]
Beyond Hallucinations: Enhancing LVLMs through Hallucination-Aware Direct Preference Optimization
2023Zhiyuan Zhao, Bin Wang et al.
[16]
Improved Baselines with Visual Instruction Tuning
2023Haotian Liu, Chunyuan Li et al.
[17]
Representation Engineering: A Top-Down Approach to AI Transparency
2023Andy Zou, Long Phan et al.
[18]
Aligning Large Multimodal Models with Factually Augmented RLHF
2023Zhiqing Sun, Sheng Shen et al.
[19]
MM-Vet: Evaluating Large Multimodal Models for Integrated Capabilities
2023Weihao Yu, Zhengyuan Yang et al.
[20]
A survey on multimodal large language models
2023Shukang Yin, Chaoyou Fu et al.

Showing 20 of 26 references

Founder's Pitch

"A framework to reduce object hallucination in vision-language models by reactive visual feature extraction."

Vision-Language ModelsScore: 6View PDF ↗

Commercial Viability Breakdown

0-10 scale

High Potential

2/4 signals

5

Quick Build

4/4 signals

10

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: 2/12/2026

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.