5 citations · 5 across the 7 of their papers we have counts for
11 papers
A Theoretical Analysis of Environment-Aware Out-of-Distribution Detection for Vision-Language Models
Salimeh Sekeh, Mary Wisell
Continual vision-language models (VLMs) encounter a stream of environments and must also reject inputs that do not belong to the environment at hand. Class information from earlier…
World Embedding Benchmark
Yiqi Liu, Ruifeng Yuan, Yang Wang +7
Physical fidelity has received increasing attention in world models and video generation, yet how video representations encode physical information remains less understood. We intr…
AutoDataBench: A Data-centric Testbed for Accelerating Auto Research
Ruifeng Yuan, Yizhi Li, Yaxin Du +9
Existing auto-research benchmarks often entangle multiple sources of improvement, including training frameworks, hyperparameters, compute budgets, and data, making it difficult to…
On the Design Fundamentals of Pixel Text Representation Learning
Chaohao Yuan, Ruifeng Yuan, Zhuoxu Huang +4
Text-rich visual inputs require models that can read, retrieve, and compress language directly in pixel space, yet existing pixel-text encoders struggle with fixed resolution pretr…
Understanding the Behaviors of Environment-aware Information Retrieval
Ruifeng Yuan, Chaohao Yuan, David Dai +4
Recent retrieval-augmented generation (RAG) approaches have demonstrated strong capability in handling complex queries, yet current research overlooks a critical challenge: differe…
MVEB: Massive Video Embedding Benchmark
Adnan El Assadi, Roman Solomatin, Isaac Chung +13
We introduce the Massive Video Embedding Benchmark (MVEB), a 23-task benchmark for video embeddings spanning classification, zero-shot classification, clustering, pair classificati…
Decouple before Integration: Test-time Synthesis of SFT and RLVR Task Vectors
Chaohao Yuan, Chenghao Xiao, Yu Rong +2
SFT and RLVR represent two fundamental yet distinct paradigms for LLM post-training, each excelling in distinct dimensions. SFT expands knowledge breadth while RLVR enhances reason…
Scaling Language-Centric Omnimodal Representation Learning
Chenghao Xiao, Hou Pong Chan, Hao Zhang +3
Recent multimodal embedding approaches leveraging multimodal large language models (MLLMs) fine-tuned with contrastive learning (CL) have shown promising results, yet the underlyin…
VL-Cogito: Progressive Curriculum Reinforcement Learning for Advanced Multimodal Reasoning
Ruifeng Yuan, Chenghao Xiao, Sicong Leng +9
Reinforcement learning has proven its effectiveness in enhancing the reasoning capabilities of large language models. Recent research efforts have progressively extended this parad…
Lingshu: A Generalist Foundation Model for Unified Multimodal Medical Understanding and Reasoning
LASA Team, Weiwen Xu, Hou Pong Chan +16
Multimodal Large Language Models (MLLMs) have demonstrated impressive capabilities in understanding common visual elements, largely due to their large-scale datasets and advanced t…
ReasonMed: A 370K Multi-Agent Generated Dataset for Advancing Medical Reasoning
Yu Sun, Xingyu Qian, Weiwen Xu +8
Reasoning-based large language models have excelled in mathematics and programming, yet their potential in knowledge-intensive medical question answering remains underexplored and…