3 citations · 3 across the 8 of their papers we have counts for
8 papers
FaithRL: Learning to Reason Faithfully through Step-Level Faithfulness Maximization
Runquan Gui, Yafu Li, Xiaoye Qu +3
Reinforcement Learning with Verifiable Rewards (RLVR) has markedly improved the performance of Large Language Models (LLMs) on tasks requiring multi-step reasoning. However, most R…
How Much Reasoning Do Retrieval-Augmented Models Add beyond LLMs? A Benchmarking Framework for Multi-Hop Inference over Hybrid Knowledge
Junhong Lin, Bing Zhang, Song Wang +4
Large language models (LLMs) continue to struggle with knowledge-intensive questions that require up-to-date information and multi-hop reasoning. Augmenting LLMs with hybrid extern…
Evo-1: Lightweight Vision-Language-Action Model with Preserved Semantic Alignment
Tao Lin, Yilei Zhong, Yuxin Du +11
Vision-Language-Action (VLA) models have emerged as a powerful framework that unifies perception, language, and control, enabling robots to perform diverse tasks through multimodal…
MIND: A Multi-agent Framework for Zero-shot Harmful Meme Detection
Ziyan Liu, Chunxiao Fan, Haoran Lou +2
The rapid expansion of memes on social media has highlighted the urgent need for effective approaches to detect harmful content. However, traditional data-driven approaches struggl…
LLaVA-SP: Enhancing Visual Representation with Visual Spatial Tokens for MLLMs
Haoran Lou, Chunxiao Fan, Ziyan Liu +2
The architecture of multimodal large language models (MLLMs) commonly connects a vision encoder, often based on CLIP-ViT, to a large language model. While CLIP-ViT works well for c…
RedOne: Revealing Domain-specific LLM Post-Training in Social Networking Services
Fei Zhao, Chonggang Lu, Yue Wang +22
As a primary medium for modern information dissemination, social networking services (SNS) have experienced rapid growth, which has proposed significant challenges for platform con…
Can Multimodal Large Language Models Understand Spatial Relations?
Jingping Liu, Ziyan Liu, Zhedong Cen +5
Spatial relation reasoning is a crucial task for multimodal large language models (MLLMs) to understand the objective world. However, current benchmarks have issues like relying on…
VL-UR: Vision-Language-guided Universal Restoration of Images Degraded by Adverse Weather Conditions
Ziyan Liu, Yuxu Lu, Huashan Yu +1
Image restoration is critical for improving the quality of degraded images, which is vital for applications like autonomous driving, security surveillance, and digital content enha…