VLM幻覺可提前攔截

Computer Science > Computer Vision and Pattern Recognition arXiv:2606.00435 (cs) [Submitted on 29 May 2026 (v1), last revised 16 Sep 2026 (this version, v4)] Title:Detect Before You Leap: Mirage Detection in Vision-Language Models Authors:Md.Shaown Miah, S.M.

Taiabul Haque, Syed Ishtiaque Ahmed, Sayeed Shafayet Chowdhury View a PDF of the paper titled Detect Before You Leap: Mirage Detection in Vision-Language Models, by Md.

Shaown Miah and 3 other authors View PDF HTML (experimental) Abstract:Vision-language models (VLMs) can produce confident answers without relevant visual evidence, a failure mode known as mirage reasoning (Asadi et al., 2026).

To that end, we study pre-release mirage detection: deciding whether a VLM answer should be released or withheld.

Our model-agnostic method, Text-Conditioned Layer-wise Internal Alignment (TC-LIA), tracks question-image alignment across the layers of a frozen CLIP ViT-H/14 encoder, summarizing patch-text alignment by final similarity, late-layer top-k alignment, early-to-late gain, and slope.

TC-LIA is purely unsupervised (fixed projections, fixed scoring weights, no labels, no training) and already delivers strong detection independently.

Additionally, when combined with blank/noise detection, domain routing, and VLM self-assessment, it forms an ensemble whose supervised training improves performance but is an optional add-on.On 19,004 samples spanning ten VQA domains, fourteen state-of-the-art VLMs exhibit 57.3-75.

0% base mirage rates.Our proposed TC-LIA alone cuts this to 7.5% with 83.5% Related/Unrelated/Blank-Noise classification accuracy, and the ensemble reaches 84.3-88.4% accuracy with 5.9-7.2% mirage rates (best joint result: 88.4% accuracy, 6.4% mirage rate).

Notably, an ensemble trained on a single backbone transfers well to unseen backbones, with the best-transferring source staying within 1.2% accuracy points of per-backbone training across thirteen held-out VLMs.Subjects: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.

AI) Cite as: arXiv:2606.00435 [cs.CV] (or arXiv:2606.00435v4 [cs.CV] for this version) https://doi.org/10.48550/arXiv.2606.00435 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Md.

Shaown Miah [view email] [v1] Fri, 29 May 2026 23:51:35 UTC (11,848 KB) [v2] Mon, 15 Jun 2026 15:09:01 UTC (13,483 KB) [v3] Mon, 27 Jul 2026 23:31:43 UTC (16,234 KB) [v4] Wed, 16 Sep 2026 06:47:00 UTC (16,286 KB) Full-text links: Access Paper: View a PDF of the paper titled Detect Before You Leap: Mirage Detection in Vision-Language Models, by Md.

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