---
id: 20260603-T0-16
title: "TIGER：多模态生成幻觉修复方案"
title_en: "TIGER: Hallucination Repair for Multimodal AI"
url: https://ai.daily.yangsir.net/daily/20260603-T0-16
issue_date: 2026-06-03
publish_date: 2026-06-02T04:00:00.000Z
category: research
source_name: "arXiv cs.AI"
source_url: https://arxiv.org/abs/2606.00232
---

# TIGER：多模态生成幻觉修复方案

斯坦福研究推出TIGER方案，通过图证据路由修复多模态生成中的事实错误。该方法在推理时动态调整输出事实，基于输入环境验证信息准确度。在图文生成任务中，事实一致性提升50%，已发布开源实现。

## English Version

**TIGER: Hallucination Repair for Multimodal AI**

Stanford's TIGER uses graph-based evidence routing to repair factual errors in multimodal generation. It dynamically validates outputs against input environments, showing 50% improvement in fact consistency for image-text tasks. Code now open-sourced.

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**来源**：[arXiv cs.AI](https://arxiv.org/abs/2606.00232)

**详情页**：https://ai.daily.yangsir.net/daily/20260603-T0-16

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