11 citations · 38 across the 35 of their papers we have counts for
37 papers
FrontierChallenge: Evaluating Scientific Workflow Completion
Liangcai Su, Zhaopeng Feng, Zhuo Chen +14
Scientific agents increasingly analyze data, execute code, and produce research artifacts, yet most benchmarks emphasize final answers, isolated programs, or a single domain. We in…
Apodex 1.1: Scaling Agentic Intelligence for Complex Work
B. An, B. Li, B. Wang +72
General-purpose language models can reason and synthesize knowledge, but complex work also requires sustained interaction with files, information sources, and executable code, toge…
Apodex Discovery: Reality Benchmarks and Environments for Evaluating and Building Discoverative Artificial Intelligence
Brian Wang, Bin Feng, Xiaoman Pan +26
Apollo did not reach the Moon merely because its engineers could solve difficult equations. It succeeded by turning a distant ambition into a mission architecture of explicit objec…
ParaVT: Taming the Tool Prior Paradox for Parallel Tool Use in Agentic Video Reinforcement Learning
Zuhao Yang, Kaichen Zhang, Sudong Wang +7
Training large multimodal models (LMMs) via reinforcement learning (RL) to natively invoke video-processing tools (e.g., cropping) has become a promising route to long-video unders…
Argus: Evidence Assembly for Scalable Deep Research Agents
Zhen Zhang, Liangcai Su, Zhuo Chen +7
Deep research agents have achieved remarkable progress on complex information seeking tasks. Even long ReAct style rollouts explore only a single trajectory, while recent state of…
Visual Generation in the New Era: An Evolution from Atomic Mapping to Agentic World Modeling
Keming Wu, Zuhao Yang, Kaichen Zhang +24
Recent visual generation models have made major progress in photorealism, typography, instruction following, and interactive editing, yet they still struggle with spatial reasoning…
MOOSE-Star: Unlocking Tractable Training for Scientific Discovery by Breaking the Complexity Barrier
Zonglin Yang, Lidong Bing
While large language models (LLMs) show promise in scientific discovery, existing research focuses on inference or feedback-driven training, leaving the direct modeling of the gene…
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…
Pruning General Large Language Models into Customized Expert Models
Yirao Zhao, Guizhen Chen, Kenji Kawaguchi +2
Large language models (LLMs) have revolutionized natural language processing, yet their substantial model sizes often require substantial computational resources. To preserve compu…
Analyzing LLMs' Knowledge Boundary Cognition Across Languages Through the Lens of Internal Representations
Chenghao Xiao, Hou Pong Chan, Hao Zhang +4
While understanding the knowledge boundaries of LLMs is crucial to prevent hallucination, research on the knowledge boundaries of LLMs has predominantly focused on English. In this…
Babel: Open Multilingual Large Language Models Serving Over 90% of Global Speakers
Yiran Zhao, Chaoqun Liu, Yue Deng +8
Large language models (LLMs) have revolutionized natural language processing (NLP), yet open-source multilingual LLMs remain scarce, with existing models often limited in language…
LongPO: Long Context Self-Evolution of Large Language Models through Short-to-Long Preference Optimization
Guanzheng Chen, Xin Li, Michael Qizhe Shieh +1
Large Language Models (LLMs) have demonstrated remarkable capabilities through pretraining and alignment. However, superior short-context LLMs may underperform in long-context scen…
SeaExam and SeaBench: Benchmarking LLMs with Local Multilingual Questions in Southeast Asia
Chaoqun Liu, Wenxuan Zhang, Jiahao Ying +3
This study introduces two novel benchmarks, SeaExam and SeaBench, designed to evaluate the capabilities of Large Language Models (LLMs) in Southeast Asian (SEA) application scenari…
FINEREASON: Evaluating and Improving LLMs' Deliberate Reasoning through Reflective Puzzle Solving
Guizhen Chen, Weiwen Xu, Hao Zhang +6
Many challenging reasoning tasks require not just rapid, intuitive responses, but a more deliberate, multi-step approach. Recent progress in large language models (LLMs) highlights…
Finding the Sweet Spot: Preference Data Construction for Scaling Preference Optimization
Yao Xiao, Hai Ye, Linyao Chen +4
Iterative data generation and model retraining are widely used to align large language models (LLMs). It typically involves a policy model to generate on-policy responses and a rew…
VideoLLaMA 3: Frontier Multimodal Foundation Models for Image and Video Understanding
Boqiang Zhang, Kehan Li, Zesen Cheng +12
In this paper, we propose VideoLLaMA3, a more advanced multimodal foundation model for image and video understanding. The core design philosophy of VideoLLaMA3 is vision-centric. T…
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…
VideoRefer Suite: Advancing Spatial-Temporal Object Understanding with Video LLM
Yuqian Yuan, Hang Zhang, Wentong Li +9
Video Large Language Models (Video LLMs) have recently exhibited remarkable capabilities in general video understanding. However, they mainly focus on holistic comprehension and st…
Stabilize the Latent Space for Image Autoregressive Modeling: A Unified Perspective
Yongxin Zhu, Bocheng Li, Hang Zhang +3
Latent-based image generative models, such as Latent Diffusion Models (LDMs) and Mask Image Models (MIMs), have achieved notable success in image generation tasks. These models typ…
Chain of Ideas: Revolutionizing Research Via Novel Idea Development with LLM Agents
Long Li, Weiwen Xu, Jiayan Guo +11
Effective research ideation is a critical step for scientific research. However, the exponential increase in scientific literature makes it challenging for researchers to stay curr…
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…
Reasoning Paths Optimization: Learning to Reason and Explore From Diverse Paths
Yew Ken Chia, Guizhen Chen, Weiwen Xu +3
Advanced models such as OpenAI o1 exhibit impressive problem-solving capabilities through step-by-step reasoning. However, they may still falter on more complex problems, making er…