arXiv:2603. 28026v2 Announce Type: replace Abstract: Multimodal multiple-choice question answering (MCQA) provides a standardized and objectively measurable setting for evaluating vision-language models (VLMs).
By Taeyun Roh, Suhyeong Park, Dongyoung Lee, Eunyeong Jo, Wonjune Jang, Junha Jung, Jaewoo Kang
arXiv:2610.14190v1 Announce Type: new
Abstract: Explaining a vision--language model (VLM) prediction requires tracing training-example influence to the multimodal evidence that mediates it, yet exist...
By Laura Yao, Shixing Yu, Jie Peng, Promit Ghosal, Kyra Gan, Tianlong Chen
Melange is a synthetic visual bias benchmark designed to evaluate social biases in multi‑modal large language models (MLLMs). It extends the language‑only BBQ dataset by generating controlled visual scenarios that clearly depict target attributes, and validates images through automated VQA screening and human inspection. Using Melange, 23 open‑source MLLMs were tested in text‑only, image‑only, and image‑with‑context settings, revealing that switching from text to vision reduces fairness by an average of 0.291 and that models often choose stereotypical responses when biased answers are made.
By Razieh Rezaei, Ehsaneddin Asgary, Masoud Jalili Sabet
The paper argues that stochastic decoding, common in large language models, is not ideal for Visual Question Answering (VQA) because VQA is a closed‑ended task with head‑heavy answer distributions and epistemic uncertainty. The authors formalize how model calibration relates to predictive accuracy and identify conditions under which greedy decoding is optimal. Experiments across multiple benchmarks show greedy decoding outperforms stochastic sampling, and a new Greedy Decoding for Reasoning Models further improves multimodal reasoning performance.
By Boqi Chen, Xudong Liu, Yunke Ao, Jianing Qiu
arXiv:2609.37656v1 Announce Type: new
Abstract: Large vision-language models (LVLMs) exhibit strong reasoning capabilities, yet the visual and textual evidence supporting the generated responses rema...
By Bowen Yuan, Danny Wang, Ruihong Qiu, Zijian Wang, Zi Huang
InfMasking introduces a unified multimodal learning framework that uses Infinite Masking to encourage fusion modules to capture synergistic information across modalities. By randomly masking modality features before fusion and enforcing consistency with an unmasked reference, the method discourages reliance on dominant or shared signals and promotes meaningful cross‑modal interactions. The approach models masked fused representations as Gaussian, optimizing closed‑form variational bounds, and demonstrates improved predictive performance and higher synergy metrics on benchmark datasets.
By Liangjian Wen, Qun Dai, Jiang Duan, Yong Dai, Jianzhuang Liu, Dongkai Wang, Jun Wang, Fuyuan Cao, Zenglin Xu, Zhao Kang