@inproceedings{wani-etal-2026-answer,
title = "Same Answer, Different Representations: Hidden instability in {VLM}s",
author = "Wani, Farooq Ahmad and
Suglia, Alessandro and
Saxena, Rohit and
Gema, Aryo Pradipta and
Kwan, Wai-Chung and
Barez, Fazl and
Bucarelli, Maria Sofia and
Silvestri, Fabrizio and
Minervini, Pasquale",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.emnlp-main.1803/",
pages = "41059--41087",
ISBN = "979-8-89176-459-0",
abstract = "The robustness of Vision Language Models (VLMs) is commonly assessed through output-level invariance, implicitly assuming that stable predictions reflect stable multimodal processing. In this work, we argue that this assumption is insufficient. We introduce a representation-aware and frequency-aware evaluation framework that measures internal embedding drift, spectral sensitivity, and structural smoothness (spatial consistency of vision tokens), alongside standard label-based metrics. Applying this framework to modern VLMs across the SEEDBench, MMMU, and POPE datasets reveals three distinct failure modes. First, models frequently preserve predicted answers while undergoing substantial internal representation drift; for perturbations such as text overlays, this drift approaches the magnitude of inter-image variability, indicating that representations move to regions typically occupied by unrelated inputs despite unchanged outputs. Second, robustness does not improve with scale; larger models achieve higher accuracy but exhibit equal or greater sensitivity, consistent with sharper yet more fragile decision boundaries. Third, we find that perturbations affect tasks differently: they harm reasoning when they disrupt how models combine coarse and fine visual cues, but on the hallucination benchmarks, they can reduce false positives by making models generate more conservative answers."
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%0 Conference Proceedings
%T Same Answer, Different Representations: Hidden instability in VLMs
%A Wani, Farooq Ahmad
%A Suglia, Alessandro
%A Saxena, Rohit
%A Gema, Aryo Pradipta
%A Kwan, Wai-Chung
%A Barez, Fazl
%A Bucarelli, Maria Sofia
%A Silvestri, Fabrizio
%A Minervini, Pasquale
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-459-0
%F wani-etal-2026-answer
%X The robustness of Vision Language Models (VLMs) is commonly assessed through output-level invariance, implicitly assuming that stable predictions reflect stable multimodal processing. In this work, we argue that this assumption is insufficient. We introduce a representation-aware and frequency-aware evaluation framework that measures internal embedding drift, spectral sensitivity, and structural smoothness (spatial consistency of vision tokens), alongside standard label-based metrics. Applying this framework to modern VLMs across the SEEDBench, MMMU, and POPE datasets reveals three distinct failure modes. First, models frequently preserve predicted answers while undergoing substantial internal representation drift; for perturbations such as text overlays, this drift approaches the magnitude of inter-image variability, indicating that representations move to regions typically occupied by unrelated inputs despite unchanged outputs. Second, robustness does not improve with scale; larger models achieve higher accuracy but exhibit equal or greater sensitivity, consistent with sharper yet more fragile decision boundaries. Third, we find that perturbations affect tasks differently: they harm reasoning when they disrupt how models combine coarse and fine visual cues, but on the hallucination benchmarks, they can reduce false positives by making models generate more conservative answers.
%U https://aclanthology.org/2026.emnlp-main.1803/
%P 41059-41087
Markdown (Informal)
[Same Answer, Different Representations: Hidden instability in VLMs](https://aclanthology.org/2026.emnlp-main.1803/) (Wani et al., EMNLP 2026)
ACL
- Farooq Ahmad Wani, Alessandro Suglia, Rohit Saxena, Aryo Pradipta Gema, Wai-Chung Kwan, Fazl Barez, Maria Sofia Bucarelli, Fabrizio Silvestri, and Pasquale Minervini. 2026. Same Answer, Different Representations: Hidden instability in VLMs. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing, pages 41059–41087, Budapest, Hungary. Association for Computational Linguistics.