arXiv — NLP / Computation & Language · · 3 min read

Slow to See, Slow to Suppress: Understanding the Effects of Modality in Context-Memory Conflicts

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Computer Science > Computation and Language

arXiv:2609.00293 (cs)
[Submitted on 31 Aug 2026]

Title:Slow to See, Slow to Suppress: Understanding the Effects of Modality in Context-Memory Conflicts

View a PDF of the paper titled Slow to See, Slow to Suppress: Understanding the Effects of Modality in Context-Memory Conflicts, by Athulith Paraselli and 2 other authors
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Abstract:We investigate how vision-language models (VLMs) handle context-memory conflicts; that is, situations in which the model is given information in context that differs from what was stored parametrically during training. We document asymmetric biases: models tend to prefer in-context information about entities which appear in text, but prefer parametric information about entities which appear in images. We relate this asymmetry to the late representational alignment across modalities, showing that the longer processing time associated with resolving visual entities prevents the suppression of the model's usual factual recall mechanism, thus resulting in more parametric answers. Chain-of-thought reasoning does not appear to resolve the gap, but increasing the amount of visual information in the context does show an effect. These results illustrate the complexity of ensuring consistent behavior as models become increasingly multimodal and retrieval-augmented.
Comments: Accepted to EMNLP Findings 2026. Code and dataset are available at this https URL
Subjects: Computation and Language (cs.CL)
Cite as: arXiv:2609.00293 [cs.CL]
  (or arXiv:2609.00293v1 [cs.CL] for this version)
  https://doi.org/10.48550/arXiv.2609.00293
arXiv-issued DOI via DataCite (pending registration)

Submission history

From: Athulith Paraselli [view email]
[v1] Mon, 31 Aug 2026 19:37:07 UTC (2,712 KB)
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