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 (34)

[1]
Vision-Language Introspection: Mitigating Overconfident Hallucinations in MLLMs via Interpretable Bi-Causal Steering
2026Shuliang Liu, Songbo Yang et al.
[2]
CRoPS: A Training-Free Hallucination Mitigation Framework for Vision-Language Models
2026Neeraj Anand, Samyak Jha et al.
[3]
Understanding and Mitigating Hallucination in Large Vision-Language Models via Modular Attribution and Intervention
2025Tianyun Yang, Ziniu Li et al.
[4]
Erasing Conceptual Knowledge from Language Models
2024Rohit Gandikota, Sheridan Feucht et al.
[5]
Qwen2-VL: Enhancing Vision-Language Model's Perception of the World at Any Resolution
2024Peng Wang, Shuai Bai et al.
[6]
Self-Introspective Decoding: Alleviating Hallucinations for Large Vision-Language Models
2024Fushuo Huo, Wenchao Xu et al.
[7]
What Do VLMs NOTICE? A Mechanistic Interpretability Pipeline for Noise-free Text-Image Corruption and Evaluation
2024Michal Golovanevsky, William Rudman et al.
[8]
Alleviating Hallucinations in Large Vision-Language Models through Hallucination-Induced Optimization
2024Beitao Chen, Xinyu Lyu et al.
[9]
Chameleon: Mixed-Modal Early-Fusion Foundation Models
2024Chameleon Team, Mingda Chen et al.
[10]
A Survey on Hallucination in Large Vision-Language Models
2024Hanchao Liu, Wenyuan Xue et al.
[11]
OPERA: Alleviating Hallucination in Multi-Modal Large Language Models via Over-Trust Penalty and Retrospection-Allocation
2023Qidong Huang, Xiao-wen Dong et al.
[12]
Mitigating Object Hallucinations in Large Vision-Language Models through Visual Contrastive Decoding
2023Sicong Leng, Hang Zhang et al.
[13]
Beyond Hallucinations: Enhancing LVLMs through Hallucination-Aware Direct Preference Optimization
2023Zhiyuan Zhao, Bin Wang et al.
[14]
A Survey on Hallucination in Large Language Models: Principles, Taxonomy, Challenges, and Open Questions
2023Lei Huang, Weijiang Yu et al.
[15]
Hallusionbench: An Advanced Diagnostic Suite for Entangled Language Hallucination and Visual Illusion in Large Vision-Language Models
2023Tianrui Guan, Fuxiao Liu et al.
[16]
Improved Baselines with Visual Instruction Tuning
2023Haotian Liu, Chunyuan Li et al.
[17]
Efficient Streaming Language Models with Attention Sinks
2023Guangxuan Xiao, Yuandong Tian et al.
[18]
Aligning Large Multimodal Models with Factually Augmented RLHF
2023Zhiqing Sun, Sheng Shen et al.
[19]
Qwen-VL: A Versatile Vision-Language Model for Understanding, Localization, Text Reading, and Beyond
2023Jinze Bai, Shuai Bai et al.
[20]
Mitigating Hallucination in Large Multi-Modal Models via Robust Instruction Tuning
2023Fuxiao Liu, Kevin Lin et al.

Showing 20 of 34 references

Founder's Pitch

"A practical solution to reduce hallucination in vision-language models through inference-time spatial credit redistribution."

Vision-Language ModelsScore: 8View PDF ↗

Commercial Viability Breakdown

0-10 scale

High Potential

2/4 signals

5

Quick Build

3/4 signals

7.5

Series A Potential

3/4 signals

7.5

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: 2/25/2026

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.