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20242026
most citedLLMDet: Learning Strong Open-Vocabulary Object Detectors under the Supervision of Large Language Models

2 citations · 3 across the 14 of their papers we have counts for

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7 papers · 1 filter

cs.AI2026

MMPostTrainBench: Benchmarking Autonomous Research for Multimodal Post-Training

Yuxin Liu, Yuxuan Wang, Zhenxin Lei +9

Autonomous research seeks sustained model improvements through iterative experimentation and feedback. LLM agents show promise in automating machine learning and language-model pos…

cs.CV2026

OmniReasoning: Pushing the Limits of Audio-Visual Joint Reasoning

Junming Lin, Yuxuan Wang, Zhenxin Lei +11

Recent advances have enabled unified omni-modal models in understanding audio, vision, and language. However, existing benchmarks, training data, and learning methods largely treat…

cs.SD2026

OmniEcho: Audio-Visual Spatial Understanding for Omni-Modal Embodied Agents

Ruixun Liu, Yuxuan Wang, Jiacheng Xie +10

Humans can effortlessly localize the direction of a sound source and integrate it with visual cues for reasoning, yet this remains challenging for embodied agents. In particular, i…

cs.SE2026

Omni2Web: Benchmarking Audiovisual Website Development

Minghao Han, Zhenghao Xing, Xize Cheng +7

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 hi…

eess.AS2026

OmniVChat: Synthesizing, Benchmarking, and Training for Native Audio-Visual Dialogue

Haolin He, Yunfei Chu, Qi Chen +15

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 au…

cs.CV2026

Native Active Perception as Reasoning for Omni-Modal Understanding

Zhenghao Xing, Ruiyang Xu, Yuxuan Wang +8

Passive models for long video understanding typically rely on a "watch-it-all" paradigm, processing frames uniformly regardless of query difficulty, causing computational cost to g…

cs.CV2026

Pest-Thinker: Learning to Think and Reason like Entomologists via Reinforcement Learning

Xueheng Li, Yu Wang, Tao Hu +6

Pest-induced crop losses pose a major threat to global food security and sustainable agricultural development. While recent advances in Multimodal Large Language Models (MLLMs) hav…