most citedRobobench: A Comprehensive Evaluation Benchmark for Multimodal Large Language Models as Embodied Brain

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13 papers

cs.RO20261 cited

Robobench: A Comprehensive Evaluation Benchmark for Multimodal Large Language Models as Embodied Brain

Yulin Luo, Chun-Kai Fan, Menghang Dong +19

Building robots that can perceive, reason, and act in dynamic, unstructured environments remains a central challenge. Recent embodied systems often follow a dual-system paradigm, w…

cs.CV2026

Rethinking VLM Representation for VLA Initialization

Weifeng Lin, Siyuan Huang, Hao Li +5

Vision-Language-Action (VLA) models widely adopt pretrained Vision-Language Models (VLMs) as policy backbones, yet it remains unclear what kind of pretrained VLM representation is…

cs.CV2026

MC-LLaVA: Multi-Concept Personalized Vision-Language Model

Ruichuan An, Sihan Yang, Renrui Zhang +10

Current vision-language models (VLMs) show exceptional abilities across diverse tasks, such as visual question answering. To enhance user experience, recent studies have investigat…

cs.CV2026

LoVR: A Benchmark for Long Video Retrieval in Multimodal Contexts

Qifeng Cai, Hao Liang, Zhaoyang Han +6

Long videos contain a vast amount of information, making video-text retrieval an essential and challenging task in multimodal learning. However, existing benchmarks suffer from lim…

cs.CV2025

Are Video Models Ready as Zero-Shot Reasoners? An Empirical Study with the MME-CoF Benchmark

Ziyu Guo, Xinyan Chen, Renrui Zhang +7

Recent video generation models can produce high-fidelity, temporally coherent videos, indicating that they may encode substantial world knowledge. Beyond realistic synthesis, they…

cs.CV2025

UniCTokens: Boosting Personalized Understanding and Generation via Unified Concept Tokens

Ruichuan An, Sihan Yang, Renrui Zhang +10

Personalized models have demonstrated remarkable success in understanding and generating concepts provided by users. However, existing methods use separate concept tokens for under…