11 papers
InterLV-Search: Benchmarking Interleaved Multimodal Agentic Search
Bohan Hou, Jiuning Gu, Jiayan Guo +5
Existing benchmarks for multimodal agentic search evaluate multimodal search and visual browsing, but visual evidence is either confined to the input or treated as an answer endpoi…
MMR1: Enhancing Multimodal Reasoning with Variance-Aware Sampling and Open Resources
Sicong Leng, Jing Wang, Jiaxi Li +12
Large multimodal reasoning models have achieved rapid progress, but their advancement is constrained by two major limitations: the absence of open, large-scale, high-quality long c…
RynnEC: Bringing MLLMs into Embodied World
Ronghao Dang, Yuqian Yuan, Yunxuan Mao +6
We introduce RynnEC, a video multimodal large language model designed for embodied cognition. Built upon a general-purpose vision-language foundation model, RynnEC incorporates a r…
WorldVLA: Towards Autoregressive Action World Model
Jun Cen, Chaohui Yu, Hangjie Yuan +9
We present WorldVLA, an autoregressive action world model that unifies action and image understanding and generation. Our WorldVLA intergrates Vision-Language-Action (VLA) model an…
EOC-Bench: Can MLLMs Identify, Recall, and Forecast Objects in an Egocentric World?
Yuqian Yuan, Ronghao Dang, Long Li +8
The emergence of multimodal large language models (MLLMs) has driven breakthroughs in egocentric vision applications. These applications necessitate persistent, context-aware under…
Benchmarking Multimodal Mathematical Reasoning with Explicit Visual Dependency
Zhikai Wang, Jiashuo Sun, Wenqi Zhang +4
Recent advancements in Large Vision-Language Models (LVLMs) have significantly enhanced their ability to integrate visual and linguistic information, achieving near-human proficien…
Embodied-Reasoner: Synergizing Visual Search, Reasoning, and Action for Embodied Interactive Tasks
Wenqi Zhang, Mengna Wang, Gangao Liu +10
Recent advances in deep thinking models have demonstrated remarkable reasoning capabilities on mathematical and coding tasks. However, their effectiveness in embodied domains which…
ECBench: Can Multi-modal Foundation Models Understand the Egocentric World? A Holistic Embodied Cognition Benchmark
Ronghao Dang, Yuqian Yuan, Wenqi Zhang +7
The enhancement of generalization in robots by large vision-language models (LVLMs) is increasingly evident. Therefore, the embodied cognitive abilities of LVLMs based on egocentri…
2.5 Years in Class: A Multimodal Textbook for Vision-Language Pretraining
Wenqi Zhang, Hang Zhang, Xin Li +6
Compared to image-text pair data, interleaved corpora enable Vision-Language Models (VLMs) to understand the world more naturally like humans. However, such existing datasets are c…
Breaking the Memory Barrier: Near Infinite Batch Size Scaling for Contrastive Loss
Zesen Cheng, Hang Zhang, Kehan Li +6
Contrastive loss is a powerful approach for representation learning, where larger batch sizes enhance performance by providing more negative samples to better distinguish between s…
The Curse of Multi-Modalities: Evaluating Hallucinations of Large Multimodal Models across Language, Visual, and Audio
Sicong Leng, Yun Xing, Zesen Cheng +7
Recent advancements in large multimodal models (LMMs) have significantly enhanced performance across diverse tasks, with ongoing efforts to further integrate additional modalities…