Title: When Symbols Speak: Understanding Logo Triggered Texts in Vision-Language Models
Primary area: applications to computer vision, audio, language, and other modalities
Keywords: Vision-Language Models, logo hallucination, projector embeddings, multimodal robustness, text recognition

Abstract: Vision Language Models (VLMs) have achieved impressive progress in multimodal reasoning, yet they remain vulnerable to hallucinations where outputs are not grounded in visual evidence. In this paper, we investigate a previously overlooked setting: logo hallucination, where models generate brand names or textual content despite logos containing no visible words. Using curated splits of pure symbols, hybrids, and text-bearing logos, as well as the challenging Hard-60 subset, we systematically measure hallucination across leading VLMs. We further probe robustness through nine structured perturbations and show that hallucinations persist even under strong distortions, with occlusion exposing the sharpest weakness. Embedding-level analysis with open-weight LLaVA demonstrates that hallucination is tied to a small subset of projector dimensions, and targeted ablation substantially reduces errors while preserving OCR accuracy. Together, these findings reveal that VLMs often rely on symbolic priors rather than genuine glyph perception, particularly for iconic circular logos, and that projector subspaces play a decisive role in this failure mode. Our work contributes both a novel diagnostic lens and actionable mitigation insights, highlighting projector disentanglement and OCR-guided decoding as promising directions for building more trustworthy multimodal systems.

TL;DR: VLMs hallucinate brand names for text-free logos; we show this bias stems from projector embeddings and propose diagnostics pointing to disentangled projector design as a solution.