TRIAD is a new benchmark designed to test omni‑modal large language models’ ability to resolve ambiguity across text, vision, and audio. Each item consists of a text–image–audio triplet, a question, and answer options, constructed so that the full triplet yields a unique answer while any single modality removed makes the answer ambiguous. The benchmark includes 327 queries in 86 scene groups, covering 18 ambiguity categories, and shows that current models lag humans in scene‑level disambiguation, especially when answer options are removed.
By Zhaolu Kang, Yidi Wang, Yile Li, Siqi Zeng, Yingjie He, Richeng Xuan, Zhichao hu
arXiv:2608. 05381v1 Announce Type: new Abstract: Current Multimodal Large Language Models (MLLMs) can process diverse sensory inputs, yet their reasoning remains heavily biased toward a dominant modality, resulting in brittle cross-modal reasoning.
By Swapnanil Mukherjee, Agyeya Negi, Tanuja Ganu, Ponnurangam Kumaraguru
arXiv:2609.18323v1 Announce Type: new
Abstract: Recent Omni-Modal Generative Models (Omni-Models) have advanced content generation toward unified modeling of text, images, video, and audio. MiniMax-H...
By Haoyu Zhao, Zihao Zhao, Tianyu Deng, Ziqin Xu, Zihao Zhang, Xudong Wang, Jinxiang Guo, Chen Gao, Ziyi Ye, Yeying Jin, Jiaxi Gu, Zuxuan Wu, Shuicheng Yan
arXiv:2607. 10299v1 Announce Type: new Abstract: Recent advances in large-scale multimodal models have drivenremarkable progress in vision-language tasks; however, comprehensiveomni-modal understanding remains under-explored, largely due to thescarcity of datasets with rich, explicitly aligned auditory cues.
By Kaiying Yan, Luoyi Sun, Xiao Zhou, Weidi Xie
The paper introduces D3-Omni, a balanced and decoupled benchmark designed to diagnose fine‑grained multimodal understanding in OmniJudges that evaluate text‑to‑image, text‑to‑video, and text‑to‑speech generation. D3-Omni covers 53 orthogonal binary dimensions across 10,671 samples, using fixed positive seeds and controlled prompt rewriting to generate negatives, thereby ensuring each error can be attributed to a single capability. The benchmark’s dual‑balanced, decoupled, and dynamic design achieves near 1:1 per‑dimension parity and a uniform total‑score distribution, revealing that strong OmniJudges often miss modality‑related failures and treat distinct attributes as a single decision, masking systematic blind spots.
By Guangzheng Hu, Ziyue Jiang, Weixu Qiao, Lixin Zhang, Jianye Kang, Yuru Wu, Rong Bao, Niantong Li, Wei Wang, Ziyi Cheng, Xinfa Zhu, HangRui Hu, Ting He, Bing Zhao, Lin Qu, Hu Wei, Jin Xu
arXiv:2609.36798v1 Announce Type: cross
Abstract: Omni-modal large language models (LLMs) are expected to answer a question using the modality it explicitly refers to. However, existing training para...
By Yueran Ma, Ronghao Lin
The paper introduces a new benchmark called Speech-Augmented Visually Grounded Contrastive Triplet Benchmark, comprising 10,150 images from 18 MENA countries, each paired with a supported statement and two plausible but unsupported alternatives. It defines contrastive instability as the rate at which multimodal models fail to resolve all statements within a triplet, distinguishing fragmented reasoning from complete failure. Experiments on recent multimodal models show that shifts in modality (text vs. speech) and language (English vs. Arabic) lead to significant triplet-level inconsistencies, especially when speech is used, which are not fully reflected by overall accuracy metrics.
By Basel Mousi, Fahim Dalvi, Shammur Chowdhury, Firoj Alam, Nadir Durrani
arXiv:2606. 00959v1 Announce Type: new Abstract: Understanding modality interaction in multimodal large language models (MLLMs) is central to reliable deployment.
By Wanlong Fang, Tianle Zhang, Wen Tao, Alvin Chan
OmniHallu is a unified framework for detecting hallucinations in multimodal large language models across both comprehension and generation tasks involving image, video, and audio modalities. It introduces OmniHallu-Bench, a 10,000-sample benchmark with claim-level human annotations for six cross-modal tasks (I2T, V2T, A2T, T2I, T2V, T2A). The system uses a multi‑agent architecture that decomposes outputs into atomic claims, verifies them with modality‑specific experts, and aggregates evidence through structured reasoning, while a preference‑optimized verifier reduces expert calls by 66% with minimal performance loss.
By Jianjiang Yang, Peihang Li, Shanqing Xu, Mengchen Qian, Lu Zhang, Meng Luo
The paper introduces IMAVB, a 500‑clip benchmark that tests whether omnimodal large language models can detect when a textual premise contradicts their visual or audio input. Experiments on eight open‑source models and Gemini 3.1 Pro reveal a Representation‑Action Gap: internal states encode mismatches, yet the models rarely reject false premises, exhibiting under‑rejection or over‑rejection. A probe‑guided logit adjustment improves rejection behavior, suggesting the main bottleneck is in translating perception to action rather than in perception itself.
By Trung Nguyen Quang, Yiming Gao, Fanyi Pu, Kaichen Zhang, Shuo Sun, Ziwei Liu
VIDA (Visually-Dependent Ambiguity) is a new dataset comprising 2,500 curated instances designed to test whether multimodal machine translation models can use visual input to resolve ambiguous source spans. The authors also introduce Disambiguation-Centric Metrics, which employ an LLM-as-a-judge classifier to verify correct span-level resolution. Experiments with recent large vision‑language models show that visual disambiguation remains difficult, and that chain‑of‑thought supervised fine‑tuning yields stronger out‑of‑distribution performance, especially for collective‑noun ambiguities.
By Jingheng Pan, Xintong Wang, Longyue Wang, Liang Ding, Weihua Luo, Chris Biemann
The Modality Maturity Index (MMI) is a new benchmark that evaluates large language models on their ability to handle five different modalities—text, image, audio, video, and document—across up to three-input and three-output combinations. It contains 893 self‑contained questions, each with human‑authored rubric criteria for the expected output modalities, and measures performance via an MMI Value and a Modality Presence Score (MPS). Experiments on five frontier multimodal models show low MPS scores, indicating limited modality availability, and confirm that LLM judges can reliably assess output correctness against human‑blind rubric scoring on 70.8% of cases.
By Rohit Patel, Dieuwke Hupkes, Sloan Strader