1 citations · 1 across the 12 of their papers we have counts for
12 papers
Terminal-Bench: Benchmarking Agents on Hard, Realistic Tasks in Command Line Interfaces
Mike A. Merrill, Alexander G. Shaw, Nicholas Carlini +82
AI agents may soon become capable of autonomously completing valuable, long-horizon tasks in diverse domains. Current benchmarks either do not measure real-world tasks, or are not…
DenseAnnotate: Enabling Scalable Dense Caption Collection for Images and 3D Scenes via Spoken Descriptions
Xiaoyu Lin, Aniket Ghorpade, Hansheng Zhu +9
With the rapid adoption of multimodal large language models (MLLMs) across diverse applications, there is a pressing need for task-centered, high-quality training data. A key limit…
Explainable AI-Generated Image Detection RewardBench
Michael Yang, Shijian Deng, William T. Doan +4
Conventional, classification-based AI-generated image detection methods cannot explain why an image is considered real or AI-generated in a way a human expert would, which reduces…
ICEPool: Enhancing Graph Pooling Networks with Inter-cluster Connectivity
Michael Yang
Hierarchical Pooling Models have demonstrated strong performance in classifying graph-structured data. While numerous innovative methods have been proposed to design cluster assign…
Zoom-Refine: Boosting High-Resolution Multimodal Understanding via Localized Zoom and Self-Refinement
Xuan Yu, Dayan Guan, Yanfeng Gu
Multimodal Large Language Models (MLLM) often struggle to interpret high-resolution images accurately, where fine-grained details are crucial for complex visual understanding. We i…
REIC: RAG-Enhanced Intent Classification at Scale
Ziji Zhang, Michael Yang, Zhiyu Chen +6
Accurate intent classification is critical for efficient routing in customer service, ensuring customers are connected with the most suitable agents while reducing handling times a…
DeepEyes: Incentivizing "Thinking with Images" via Reinforcement Learning
Ziwei Zheng, Michael Yang, Jack Hong +5
Large Vision-Language Models excel at multimodal understanding but struggle to deeply integrate visual information into their predominantly text-based reasoning processes, a key ch…
The MASK Benchmark: Disentangling Honesty From Accuracy in AI Systems
Richard Ren, Arunim Agarwal, Mantas Mazeika +13
As large language models (LLMs) become more capable and agentic, the requirement for trust in their outputs grows significantly, yet at the same time concerns have been mounting th…
Uncovering Customer Issues through Topological Natural Language Analysis
Shu-Ting Pi, Sidarth Srinivasan, Yuying Zhu +2
E-commerce companies deal with a high volume of customer service requests daily. While a simple annotation system is often used to summarize the topics of customer contacts, thorou…
Teacher-Student Learning on Complexity in Intelligent Routing
Shu-Ting Pi, Michael Yang, Yuying Zhu +1
Customer service is often the most time-consuming aspect for e-commerce websites, with each contact typically taking 10-15 minutes. Effectively routing customers to appropriate age…
Contact Complexity in Customer Service
Shu-Ting Pi, Michael Yang, Qun Liu
Customers who reach out for customer service support may face a range of issues that vary in complexity. Routing high-complexity contacts to junior agents can lead to multiple tran…
Graph Relation Transformer: Incorporating pairwise object features into the Transformer architecture
Michael Yang, Aditya Anantharaman, Zachary Kitowski +1
Previous studies such as VizWiz find that Visual Question Answering (VQA) systems that can read and reason about text in images are useful in application areas such as assisting vi…