---
格式版本: 2
标题: "UniProbe: A Learnable Token-Level Hallucination Detector for Large VLMs using Multi-Structural Internal Representations"
原文链接: "https://arxiv.org/abs/2608.10835"
发布日期: "2026-08-11"
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发现时间: "2026-08-12T11:20:34+08:00"
入库时间: "2026-08-12T03:23:15.112Z"
来源平台: "arXiv 学术论文搜索"
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AI优质: "否"
AI打分: 15
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AI质检状态: "不通过"
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采集批次: "2026年8月12日2点04分20秒"
采集批次ID: "20260812-020420-289"
去重键: "https://arxiv.org/abs/2608.10835"
---

## Computer Science > Computer Vision and Pattern Recognition

## Title:UniProbe: A Learnable Token-Level Hallucination Detector for Large VLMs using Multi-Structural Internal Representations

[View PDF](https://arxiv.org/pdf/2608.10835) [HTML (experimental)](https://arxiv.org/html/2608.10835v1)

> Abstract:Large Vision-Language Models (LVLMs) achieve impressive visual reasoning and dialogue capabilities, yet frequently hallucinate content unsupported by the visual input. Effective mitigation requires token-level localization, enabling targeted intervention without discarding the entire response. Existing detectors require expensive full-model fine-tuning, rely on external verifiers that ignore the model's generation process, or reduce internal signals to isolated features and hand-crafted statistics, discarding spatial, sequential, and relational structure. We introduce \\textbf{UniProbe}, a lightweight, unified, learnable detector that models a frozen LVLM's heterogeneous computational trace from a single forward pass. UniProbe constructs a directed graph over image patches, query tokens, and generated tokens, with attention weights encoding their relations. It processes this trace with alternating structure-aware modules: a GNN for relational evidence, a ViT for 2-D visual geometry, and a GRU for response order. Interleaving them allows spatial, relational, and sequential evidence to interact throughout the detector. We further develop a streaming variant for hallucination-aware decoding, which detects and resamples hallucinated tokens during generation, and a self-adaptation strategy aligning the detector with the LVLM's own generations. Across diverse LVLM backbones, UniProbe achieves state-of-the-art token-level and object-hallucination detection. During decoding, it reduces object hallucinations by up to 55\\% at $1.06\times$ the latency of standard generation.

| Comments: |  |
| --- | --- |
| Subjects: | Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG) |
| Cite as: | [arXiv:2608.10835](https://arxiv.org/abs/2608.10835) \[cs.CV\] |
|  | (or [arXiv:2608.10835v1](https://arxiv.org/abs/2608.10835v1) \[cs.CV\] for this version) |
|  | [https://doi.org/10.48550/arXiv.2608.10835](https://doi.org/10.48550/arXiv.2608.10835) |

## Submission history

From: Dvir Samuel \[[view email](https://arxiv.org/show-email/d13d02ec/2608.10835)\]  
**\[v1\]** Tue, 11 Aug 2026 12:01:59 UTC (1,483 KB)

[Which authors of this paper are endorsers?](https://arxiv.org/auth/show-endorsers/2608.10835) | Disable MathJax ([What is MathJax?](https://info.arxiv.org/help/mathjax.html))
