2 citations · 6 across the 14 of their papers we have counts for
15 papers
GLM-5: from Vibe Coding to Agentic Engineering
GLM-5-Team, :, Aohan Zeng +184
We present GLM-5, a next-generation foundation model designed to transition the paradigm of vibe coding to agentic engineering. Building upon the agentic, reasoning, and coding (AR…
Towards Sparse Video Understanding and Reasoning
Chenwei Xu, Zhen Ye, Shang Wu +8
We present \revise (\underline{Re}asoning with \underline{Vi}deo \underline{S}parsity), a multi-round agent for video question answering (VQA). Instead of uniformly sampling frames…
Efficient Paths and Dense Rewards: Probabilistic Flow Reasoning for Large Language Models
Yan Liu, Feng Zhang, Zhanyu Ma +6
High-quality chain-of-thought has demonstrated strong potential for unlocking the reasoning capabilities of large language models. However, current paradigms typically treat the re…
UserLM-R1: Modeling Human Reasoning in User Language Models with Multi-Reward Reinforcement Learning
Feng Zhang, Shijia Li, Chunmao Zhang +7
User simulators serve as the critical interactive environment for agent post-training, and an ideal user simulator generalizes across domains and proactively engages in negotiation…
Semantic-Geometric Task Representations for Bimanual Manipulation from Human Demonstrations to Robot Action Planning
Franziska Herbert, Vignesh Prasad, Han Liu +2
Learning structured task representations from human demonstrations is essential for bimanual manipulation, where action ordering, object involvement, and interaction geometry vary…
FloodSQL-Bench: A Retrieval-Augmented Benchmark for Geospatially-Grounded Text-to-SQL
Hanzhou Liu, Kai Yin, Zhitong Chen +2
Existing Text-to-SQL benchmarks primarily focus on single-table queries or limited joins in general-purpose domains, and thus fail to reflect the complexity of domain-specific, mul…
Data-Centric Visual Development for Self-Driving Labs
Anbang Liu, Guanzhong Hu, Jiayi Wang +2
Self-driving laboratories offer a promising path toward reducing the labor-intensive, time-consuming, and often irreproducible workflows in the biological sciences. Yet their strin…
LeVR: A Modular VR Teleoperation Framework for Imitation Learning in Dexterous Manipulation
Zhengyang Kris Weng, Matthew L. Elwin, Han Liu
We introduce LeVR, a modular software framework designed to bridge two critical gaps in robotic imitation learning. First, it provides robust and intuitive virtual reality (VR) tel…
Sci2Pol: Evaluating and Fine-tuning LLMs on Scientific-to-Policy Brief Generation
Weimin Wu, Alexander C. Furnas, Eddie Yang +5
We propose Sci2Pol-Bench and Sci2Pol-Corpus, the first benchmark and training dataset for evaluating and fine-tuning large language models (LLMs) on policy brief generation from a…
BioMARS: A Multi-Agent Robotic System for Autonomous Biological Experiments
Yibo Qiu, Zan Huang, Zhiyu Wang +7
Large language models (LLMs) and vision-language models (VLMs) have the potential to transform biological research by enabling autonomous experimentation. Yet, their application re…
The Amazon Nova Family of Models: Technical Report and Model Card
Amazon AGI, Aaron Langford, Aayush Shah +783
We present Amazon Nova, a new generation of state-of-the-art foundation models that deliver frontier intelligence and industry-leading price performance. Amazon Nova Pro is a highl…
NoveltyBench: Evaluating Language Models for Humanlike Diversity
Yiming Zhang, Harshita Diddee, Susan Holm +5
Language models have demonstrated remarkable capabilities on standard benchmarks, yet they struggle increasingly from mode collapse, the inability to generate diverse and novel out…
Can Domain Experts Rely on AI Appropriately? A Case Study on AI-Assisted Prostate Cancer MRI Diagnosis
Chacha Chen, Han Liu, Jiamin Yang +11
Despite the growing interest in human-AI decision making, experimental studies with domain experts remain rare, largely due to the complexity of working with domain experts and the…
Sequential LLM Framework for Fashion Recommendation
Han Liu, Xianfeng Tang, Tianlang Chen +9
The fashion industry is one of the leading domains in the global e-commerce sector, prompting major online retailers to employ recommendation systems for product suggestions and cu…
Let's Ask GNN: Empowering Large Language Model for Graph In-Context Learning
Zhengyu Hu, Yichuan Li, Zhengyu Chen +4
Textual Attributed Graphs (TAGs) are crucial for modeling complex real-world systems, yet leveraging large language models (LLMs) for TAGs presents unique challenges due to the gap…