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When Irrelevant Text Matters: Affine Margin Shifts in Multimodal Large Language Models — arXiv:2608.19208

Source: arXiv (en)
Original: https://arxiv.org/abs/2608.19208
License (as archived): CC0-1.0
Archived: 2026-10-18T11:39:03.656Z
SHA-256 of the archived text: 34be1f14cb04cedb3a37c696870c6465d1fc993ff25a6c6d6f3f34d1cde8e5be
Stable URL of this page: https://metraipolis.com/documento/dc_eb5f8bb4100b

How to cite

When Irrelevant Text Matters: Affine Margin Shifts in Multimodal Large Language Models — arXiv:2608.19208 — arXiv (en), https://arxiv.org/abs/2608.19208 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_eb5f8bb4100b (Metraipolis archive dc_eb5f8bb4100b, sha256 34be1f14cb04cedb3a37c696870c6465d1fc993ff25a6c6d6f3f34d1cde8e5be)

BibTeX

@misc{dceb5f8bb4100b,
  title = {When Irrelevant Text Matters: Affine Margin Shifts in Multimodal Large Language Models — arXiv:2608.19208},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2608.19208},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_eb5f8bb4100b; sha256 34be1f14cb04cedb3a37c696870c6465d1fc993ff25a6c6d6f3f34d1cde8e5be},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2608.19208. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Multimodal large language models (MLLMs) are frequently exposed to auxiliary textual context, the impact of which on visually grounded tasks remains underexplored. In this paper, we investigate the influence of task-irrelevant context by formulating it as a controlled intervention within a binary visual judgment framework. By maintaining an invariant prompt structure while varying auxiliary inputs, we observe that irrelevant text consistently biases model predictions across diverse benchmarks. To move beyond performance metrics, we characterize this sensitivity through a decision margin defined by the log-probability difference between binary candidates. Our analysis reveals a robust geometric regularity: contextconditioned margins follow a consistent affine transformation of their context-free counterparts. This finding demonstrates that irrelevant context does not manifest as unstructured stochastic noise but as a estimable distortion of model preference. We further interpret the fitted affine parameters as metrics for visual commitment preservation and directional answer bias. These findings provide a margin-level diagnostic view of irrelevant-context effects in MLLMs and offer a basis for future studies on noisy-context robustness

Tema degli esploratori: agenti e intelligenza artificiale (retro-catalogo arXiv: annunci del 2026-08-21 in cs.CL).
Categorie arXiv: Computation and Language (cs.CL) ; Computer Vision and Pattern Recognition (cs.CV).
Pagina del lavoro: https://arxiv.org/abs/2608.19208 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-18T11:38:27.883Z dalla pagina https://arxiv.org/catchup/cs.CL/2026-08-21?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).