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Zora Zhiruo Wang

9 accepted papers

2026

OpenAgentSafety: A Comprehensive Framework For Evaluating Real-World AI Agent Safety

Sanidhya Vijayvargiya, Aditya Bharat Soni, Xuhui Zhou, Zora Zhiruo Wang, Nouha Dziri, Graham Neubig +1
ICLR 2026poster

Recent advances in AI agents capable of solving complex, everyday tasks-- from software engineering to customer service-- have enabled deployment in real-world settings, but their possibilities for unsafe behavior demands rigorous evaluation. While prior benchmarks have attempted to assess agent saf…

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2025

AutoPresent: Designing Structured Visuals from Scratch

Jiaxin Ge, Zora Zhiruo Wang, Xuhui Zhou, Yi-Hao Peng, Sanjay Subramanian, Qinyue Tan +5
CVPR 2025poster

Designing structured visuals such as presentation slides is essential for communicative needs, necessitating both content creation and visual planning skills. In this work, we tackle the challenge of automated slide generation, where models produce slide presentations from natural language (NL) inst…

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2025

Benchmarking Failures in Tool-Augmented Language Models

Eduardo Treviño, Hugo Contant, James Ngai, Graham Neubig, Zora Zhiruo Wang
NAACL 2025long

The integration of tools has extended the capabilities of language models (LMs) beyond vanilla text generation to versatile scenarios. However, tool-augmented language models (TaLMs) often assume ‘perfect’ information access and tool availability, which may not hold in the real world. To systematica…

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2025

CodeRAG-Bench: Can Retrieval Augment Code Generation?

Zora Zhiruo Wang, Akari Asai, Xinyan Velocity Yu, Frank F. Xu, Yiqing Xie, Graham Neubig +1
NAACL 2025findings

While language models (LMs) excel at generating code, many programs are difficult to generate using only parametric knowledge. Despite the success of retrieval-augmented generation (RAG) in text-centric tasks, its potential for code generation remains under-explored. This work introduces CodeRAG-ben…

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2025

CowPilot: A Framework for Autonomous and Human-Agent Collaborative Web Navigation

Faria Huq, Zora Zhiruo Wang, Frank F. Xu, Tianyue Ou, Shuyan Zhou, Jeffrey P. Bigham +1
NAACL 2025system demonstrations

While much work on web agents emphasizes the promise of autonomously performing tasks on behalf of users, in reality, agents often fallshort on complex tasks in real-world contexts and modeling user preference. This presents an opportunity for humans to collaborate with the agent and leverage the ag…

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2025

RAGGED: Towards Informed Design of Scalable and Stable RAG Systems

Jennifer Hsia, Afreen Shaikh, Zora Zhiruo Wang, Graham Neubig
ICML 2025poster

Retrieval-augmented generation (RAG) enhances language models by integrating external knowledge, but its effectiveness is highly dependent on system configuration. Improper retrieval settings can degrade performance, making RAG less reliable than closed-book generation. In this work, we introduce RA…

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2025

TheAgentCompany: Benchmarking LLM Agents on Consequential Real World Tasks

Frank F. Xu, Yufan Song, Boxuan Li, Yuxuan Tang, Kritanjali Jain, Mengxue Bao +15
NeurIPS 2025poster

We interact with computers on an everyday basis, be it in everyday life or work, and many aspects of work can be done entirely with access to a computer and the Internet. At the same time, thanks to improvements in large language models (LLMs), there has also been a rapid development in AI agents th…

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2025

cAST: Enhancing Code Retrieval-Augmented Generation with Structural Chunking via Abstract Syntax Tree

Yilin Zhang, Xinran Zhao, Zora Zhiruo Wang, Chenyang Yang, Jiayi Wei, Tongshuang Wu
EMNLP 2025

Retrieval-Augmented Generation (RAG) has become essential for large-scale code generation, grounding predictions in external code corpora to improve factuality. However, a critical yet underexplored aspect of RAG pipelines is chunking—the process of dividing documents into retrievable units. Existin

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