7 citations · 13 across the 10 of their papers we have counts for
11 papers
3DCodeVerse: Exploring 3D Worlds as Code
Yipeng Gao, Ziyao Zeng, Jinfa Huang +16
We introduce 3DCodeVerse, a research ecosystem that enables coding agents to create editable and verifiable 3D content at scale. 3DCodeVerse comprises large-scale, high-quality dat…
3DCodeBench: Benchmarking Agentic Procedural 3D Modeling Via Code
Yipeng Gao, Lei Shu, Genzhi Ye +5
Procedural 3D modeling through code is emerging as a versatile paradigm, offering deterministic, engine-ready, and precisely editable assets that neural 3D generators inherently la…
Ego2Web: A Web Agent Benchmark Grounded in Egocentric Videos
Shoubin Yu, Lei Shu, Antoine Yang +6
Multimodal AI agents are increasingly automating complex real-world workflows that involve online web execution. However, current web-agent benchmarks suffer from a critical limita…
UISim: An Interactive Image-Based UI Simulator for Dynamic Mobile Environments
Jiannan Xiang, Yun Zhu, Lei Shu +6
Developing and testing user interfaces (UIs) and training AI agents to interact with them are challenging due to the dynamic and diverse nature of real-world mobile environments. E…
WebQuest: A Benchmark for Multimodal QA on Web Page Sequences
Maria Wang, Srinivas Sunkara, Gilles Baechler +5
The rise of powerful multimodal LLMs has enhanced the viability of building web agents which can, with increasing levels of autonomy, assist users to retrieve information and compl…
Improve Mathematical Reasoning in Language Models by Automated Process Supervision
Liangchen Luo, Yinxiao Liu, Rosanne Liu +9
Complex multi-step reasoning tasks, such as solving mathematical problems or generating code, remain a significant hurdle for even the most advanced large language models (LLMs). V…
Accelerating Inference of Retrieval-Augmented Generation via Sparse Context Selection
Yun Zhu, Jia-Chen Gu, Caitlin Sikora +8
Large language models (LLMs) augmented with retrieval exhibit robust performance and extensive versatility by incorporating external contexts. However, the input length grows linea…
Beyond Sparse Rewards: Enhancing Reinforcement Learning with Language Model Critique in Text Generation
Meng Cao, Lei Shu, Lei Yu +4
Reinforcement learning (RL) can align language models with non-differentiable reward signals, such as human preferences. However, a major challenge arises from the sparsity of thes…
SiRA: Sparse Mixture of Low Rank Adaptation
Yun Zhu, Nevan Wichers, Chu-Cheng Lin +8
Parameter Efficient Tuning has been an prominent approach to adapt the Large Language Model to downstream tasks. Most previous works considers adding the dense trainable parameters…
Fusion-Eval: Integrating Assistant Evaluators with LLMs
Lei Shu, Nevan Wichers, Liangchen Luo +4
Evaluating natural language systems poses significant challenges, particularly in the realms of natural language understanding and high-level reasoning. In this paper, we introduce…
Critique Ability of Large Language Models
Liangchen Luo, Zi Lin, Yinxiao Liu +4
Critical thinking is essential for rational decision-making and problem-solving. This skill hinges on the ability to provide precise and reasoned critiques and is a hallmark of hum…