AVTrace is a diagnostic suite designed to evaluate audio‑visual temporal reasoning in omni models. It covers tasks such as onset and span grounding, synchronization, next‑step prediction, cross‑modal localization, chain parsing, and event‑conditioned comprehension, providing 34,114 training examples and balanced development and test splits. Five open omni models were tested, all scoring below the majority‑label baseline on synchronization verification and showing low performance on chain parsing and event‑conditioned tasks, while parameter‑efficient temporal post‑training improved some metrics.
By Longyin Zhang, Parth Sakhare Mahendra, Chengwei Wei, Ning Zhang, Lim Ming Chong, Sirui He, Ai Ti Aw
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
arXiv:2609.15128v1 Announce Type: new
Abstract: Streaming omni-modal models must decide what and when to answer from the video chunks and synchronized audio observed so far. Visual cues often support...
By Enjun Du, Siyi Liu, Ziyu Zheng, Jingyu Li, Yiwen Guo, Yongqi Zhang, Difan Zou
arXiv:2607. 03050v1 Announce Type: cross Abstract: Omni modal large language models (OmniLLMs) have attracted wide attention for their ability to jointly process audio and video, but they generate large token sequences under audio-visual inputs, leading to substantial inference cost.
By Shijie Cao, Qingyu Zhang, Boxi Yu, Yuzhong Zhang, Boxi Cao, Yaojie Lu, Hongyu Lin, Xianpei Han, Le Sun
arXiv:2610.16135v1 Announce Type: cross
Abstract: Omni-modal large language models (Omni-LLMs) extend LLMs to process heterogeneous modalities (e.g., vision, audio, and IMU signals) alongside languag...
By Na Min An, Taekyung Kim, Jaemyung Yu, Hyunjung Shim, Dongyoon Han
LEAP is a framework for long audio‑video question answering that avoids encoding entire recordings by dividing them into fixed‑duration blocks. It performs a lightweight localization pass on each block to score short candidate windows, then pools the highest‑ranked windows for a single bounded answer pass, keeping the answer input and peak context independent of recording length. The method trains both a localization LoRA and an answer LoRA, supports causal streaming queries, and achieves significant performance gains over baseline models on multiple AVQA benchmarks.
By Juyi Lin, Zhiqiang Lao, Jiali Cui, Lin Zhao, Pu Zhao, Dichang Zhang, Arman Akbari, Yu Qi, Xinru Jiang, Yanzhi Wang, Heather Yu, Liang Peng
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
Youtu-Parsing-Omni is an omni‑modal parser that transforms documents, images, audio, and video into structured records grounded in space and time, enabling agents to read, locate, and cite evidence across modalities. It uses a single encoder—initialized from a pre‑trained language model—to encode all modalities with shared weights in a unified space‑time coordinate system, while a few fusion layers allow video frames to attend to concurrent audio for cross‑modal fusion and temporal alignment. The system unifies ten parsing tasks under one schema, employing a self‑evolving post‑training procedure called OmniSchema‑Aware On‑Policy Distillation (OSAD) that co‑evolves the parser and its supervision, achieving state‑of‑the‑art performance on OmniDocBench v1.6 and leading open‑weight models on OmniParsingBench, with strong results on audio, video, chart‑parsing, and chemical‑fingerprint agreement.
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
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
The paper introduces ReTurn, a benchmark of 7,000 multimodal tasks designed to evaluate how models selectively use conversational history. ReTurn includes pairs that test task-carrying history (Reconfirm/Reground) and evidence-carrying history (Retrieve/Rebind), each preserving the same target question, media, and answer. Experiments on 13 vision‑language and audio‑language models show a drop in open‑ended accuracy from 93.7% to 72.3% when history is involved, revealing that high question recall does not guarantee effective task application and that competing media can misdirect responses.
By Shuoyang Sun, Kerui Gu, Hao Fang, Shaoli Huang, Bin Chen
arXiv:2611.06083v1 Announce Type: new
Abstract: Omni models answer questions about videos by jointly processing sampled frames and audio in one turn, with progress driven largely by scaling model siz...
By Peiran Wu, Fangfei Gou, Zhaoxi Mu, Changhao Zhang, Jian Liu, Weiqiang Wang, Yuntao Wang, Yuanchun Shi, Junxiao Shen