All the Facts, Still the Wrong Answer: Integration Gaps in Vision-Language Models

Authors: Yuyang Dai , Yuxia Wang , Zhuohan Xie

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Abstract: Multimodal reasoning requires models to combine multiple pieces of evidence into decisions that satisfy all relevant constraints. Evaluations centered on final-answer accuracy leave unclear whether failures arise from reading individual facts or using them together. We study this question through the multi-evidence integration gap, linking local fact recovery to global decision validity on controlled subset-selection and path-finding tasks. Our evaluation combines conditional performance given correct local answers with same-grid local queries, text-oracle inputs, retrieval controls, and constraint ablations. We further examine hidden-state probes and activation replacement across network depth, token positions, and model families. Across the evaluated models, accurate local recovery coexists with substantial global failure. For example, Gemma-3-27B recovers all evaluated isolated item facts correctly but achieves only 19.75% global validity on eight-item visual instances. Probes decode information about solution membership from global-prompt states, while corresponding clean-state replacement recovers failed decisions over model-dependent depth ranges. These findings show that recovering facts and representing solution-related information are insufficient for reliable joint decisions. They motivate evaluations and model improvements that explicitly target how available evidence is combined, alongside advances in perception and model scale.

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53 pages

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Artificial Intelligence (cs.AI)

Cite as:

arXiv:2610.33764 [cs.AI]

(or

arXiv:2610.33764v1 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2610.33764

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arXiv-issued DOI via DataCite (pending registration)

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From: Yuyang Dai [ view email ]

[v1]

Sat, 24 Oct 2026 16:14:52 UTC (2,330 KB)

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