3 citations · 7 across the 11 of their papers we have counts for
12 papers
Self-Organizing Agent Teams Learn to Reason Together
Aneesh Pappu, Mirac Suzgun, Yongchan Kwon +5
Collective intelligence depends not only on what team members know, but also on how they organize their work. When the structure of a solution is unknown, useful roles and division…
Harnessing the Collective Intelligence of AI Agents in the Wild for New Discoveries
Federico Bianchi, Yongchan Kwon, Aneesh Pappu +1
Scientific discovery is often a collective process: researchers share partial results, inspect failed attempts, and build on each other's ideas over long time horizons. Recent AI s…
Automated Benchmark Auditing for AI Agents and Large Language Models
Junlin Wang, Federico Bianchi, Shang Zhu +4
Modern AI benchmarks operate at a complexity that outpaces traditional verification methods. Tasks authored by domain experts often contain implicit assumptions, incomplete environ…
Voice "Cloning" is Style Transfer
Kaitlyn Zhou, Federico Bianchi, Martijn Bartelds +3
Artificially generated speech is increasingly embedded in everyday life. Voice cloning in particular enables applications where identity preservation is important, such as completi…
What LLMs Think When You Don't Tell Them What to Think About?
Yongchan Kwon, James Zou
Characterizing the behavior of large language models (LLMs) across diverse settings is critical for reliable monitoring and AI safety. However, most existing analyses rely on topic…
DSGym: A Holistic Framework for Evaluating and Training Data Science Agents
Fan Nie, Junlin Wang, Harper Hua +6
Data science agents promise to accelerate discovery and insight-generation by turning data into executable analyses and findings. Yet existing data science benchmarks fall short du…
To Err Is Human: Systematic Quantification of Errors in Published AI Papers via LLM Analysis
Federico Bianchi, Yongchan Kwon, Zachary Izzo +2
How many mistakes do published AI papers contain? Peer-reviewed publications form the foundation upon which new research and knowledge are built. Errors that persist in the literat…
ReasonIF: Large Reasoning Models Fail to Follow Instructions During Reasoning
Yongchan Kwon, Shang Zhu, Federico Bianchi +2
The ability of large language models (LLMs) to follow user instructions is central to their reliability, safety, and usefulness. While prior studies assess instruction adherence in…
A Survey on Data Markets
Jiayao Zhang, Yuran Bi, Mengye Cheng +15
Data is the new oil of the 21st century. The growing trend of trading data for greater welfare has led to the emergence of data markets. A data market is any mechanism whereby the…
Rethinking Data Shapley for Data Selection Tasks: Misleads and Merits
Jiachen T. Wang, Tianji Yang, James Zou +2
Data Shapley provides a principled approach to data valuation and plays a crucial role in data-centric machine learning (ML) research. Data selection is considered a standard appli…
Proper Dataset Valuation by Pointwise Mutual Information
Rui Ray Chen, Xuan Qi, Yuxin Chen +3
Data plays a central role in advancements in modern artificial intelligence, with high-quality data emerging as a key driver of model performance. This has prompted the development…
Data Acquisition: A New Frontier in Data-centric AI
Lingjiao Chen, Bilge Acun, Newsha Ardalani +8
As Machine Learning (ML) systems continue to grow, the demand for relevant and comprehensive datasets becomes imperative. There is limited study on the challenges of data acquisiti…