Targeted controls show that tool-invocation scores alone do not establish event grounding and that reminder coverage depends on response history and frequency, and motivate developing and evaluating streaming assistants for timely action, initiative under general user needs, and coverage of entire response sequences.
Yin-Song Yan, Ling Wang, Yuxuan Wang et al.· 0 citations
Deep research agents synthesize open-web evidence into long reports, but current benchmarks do not evaluate research centered on a source video. We introduce Video-Centered Deep Research Report Generation. Given a video and user request, an agent identifies research questions, retrieves external multimodal evidence, an...
Jia-Hao Meng, Qi-Ze Yang, Yuxuan Wang et al.· 0 citations
Screen-recorded web editing requests contain weak deictic expressions such as ``this''and ``there,''whose referents depend on speech, cursor trajectories, page state, and edit history. Such requests require intent recovery beyond the explicit specifications assumed by many existing web-editing benchmarks. We introduce...
Multimodal large language models have become the dominant paradigm for visual understanding, but incur substantial costs by encoding inputs into dense, fixed-size patch tokens. However, visual information is unevenly distributed: some regions require fine-grained detail, while others admit compact representations. Down...
Yuan Feng, Qi-Ze Yang, Rui-Zhe Chen et al.· 1 citation
Autonomous research seeks sustained model improvements through iterative experimentation and feedback. LLM agents show promise in automating machine learning and language-model post-training, but their ability to sustain multimodal improvement remains unclear. We introduce MMPostTrainBench, a benchmark spanning eight t...
Yu-Xin Liu, Yuxuan Wang, Zhen-Xin Lei et al.· 0 citations
OmniVChat-Studio, a multi-agent data engine for synthesizing single- and multi-turn audio-visual dialogues and OmniVChat-RL, a reinforcement learning reward design that jointly targets reply correctness, efficiency, and style in OmniVChat.
Haolin He, Yun-Fei Chu, Qi Chen et al.· 0 citations
Recent advances have enabled unified omni-modal models in understanding audio, vision, and language. However, existing benchmarks, training data, and learning methods largely treat the modalities independently, leaving the capability of audio-visual joint reasoning poorly evaluated and insufficiently elicited. We addre...
Junming Lin, Yuxuan Wang, Zhen-Xin Lei et al.· 0 citations
This work introduces LongAudioSpan, a benchmark that spans both duration and depth: it pairs audio from 10 minutes to over 2 hours with 3,240 questions across three cognitive levels, namely perception, understanding, and reasoning.
OmniEchoBench, a unified benchmark for spatial audio-visual perception and audio-vision-language navigation, and a spatially aware omni-modal model, which introduces an FOA spatial encoder alongside a pretrained semantic audio pathway.
Omni Demand Understanding is established as a distinct multimodal contextual inference problem: given an interaction stream, a model must detect whether a user demand is present and infer intent from multimodal and conversational context, and ODU is evaluated along five dimensions, covering both single-turn and multi-t...
Qi Chen, Yun-Fei Chu, Haolin He et al.· 1 citation· ⚡1
We define OmniVChat (Omni Video Chat) as the task of native audio-visual dialogue between a user and an omni model. In OmniVChat, omni models directly and simultaneously receive audio and video from a user and return text. The user's query is embedded in the audio and video, without a separate text question, external c...
Haolin He, Yunfei Chu, Qi Chen et al.· 0 citations
This work introduces AudioSpan, a benchmark that spans both duration and depth: it pairs audio from 10 minutes to over 2 hours with 3,240 questions across three cognitive levels, namely perception, understanding, and reasoning.
Wen Huang, Yunfei Chu, Meng Gao et al.· 0 citations
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