291 citations · 595 across the 20 of their papers we have counts for
23 papers
Heterogeneous Agentic Coevolution
Wenqian Ye, Ziwei Guan, Zi Yang +14
Recent advances in self-improving LLM agents have demonstrated the potential of AI systems that drive their own intelligence. However, many domain-specific applications rely on spe…
LabBook: Harnessing Experimental History for Efficient LLM-Driven Discovery
Bo Yuan, Wenqian Ye, Zelin Zhao +4
Evolutionary approaches to LLM-driven discovery often generate new programs from a small set of selected ancestors. This keeps contexts manageable but can omit useful evidence from…
ProofEvolve: Neuro-Symbolic Evolution for Formal Automated Theorem Proving
Wenqian Ye, Ziwei Guan, Eric Xie +6
Automated theorem proving offers a natural foundation for recursive self-improvement in scientific discovery. However, existing neural provers do not fully preserve this recursive…
A Sober Look at Agentic Misalignment in Automated Workflows
Wenqian Ye, Bo Yuan, Zhichao Xu +4
We study a class of emergent misalignment in multi-agent systems (MAS), with a focus on automated workflows, which we refer to agentic misalignment. Although these systems can solv…
SKI-SAT: A CMOS-compatible Hardware for Solving SAT Problems
Ahmet Yusuf Salim, Bart Selman, Henry Kautz +2
Nature-inspired computation is receiving increasing attention. Various Ising machine implementations have recently been proven to be effective in solving numerous combinatorial opt…
Uncovering the socioeconomic facets of human mobility
Hugo Barbosa, Surendra Hazarie, Brian Dickinson +6
Given the rapid recent trend of urbanization, a better understanding of how urban infrastructure mediates socioeconomic interactions and economic systems is of vital importance. Wh…
Detecting Individuals with Depressive Disorder fromPersonal Google Search and YouTube History Logs
Boyu Zhang, Anis Zaman, Rupam Acharyya +3
Depressive disorder is one of the most prevalent mental illnesses among the global population. However, traditional screening methods require exacting in-person interviews and may…
The Relationship between Deteriorating Mental Health Conditions and Longitudinal Behavioral Changes in Google and YouTube Usages among College Students in the United States during COVID-19: Observational Study
Anis Zaman, Boyu Zhang, Ehsan Hoque +2
Mental health problems among the global population are worsened during the coronavirus disease (COVID-19). How individuals engage with online platforms such as Google Search and Yo…
SemEval-2020 Task 7: Assessing Humor in Edited News Headlines
Nabil Hossain, John Krumm, Michael Gamon +1
This paper describes the SemEval-2020 shared task "Assessing Humor in Edited News Headlines." The task's dataset contains news headlines in which short edits were applied to make t…
Individual-level Anxiety Detection and Prediction from Longitudinal YouTube and Google Search Engagement Logs
Anis Zaman, Boyu Zhang, Vincent Silenzio +2
Anxiety disorder is one of the world's most prevalent mental health conditions, arising from complex interactions of biological and environmental factors and severely interfering o…
Explaining Local, Global, And Higher-Order Interactions In Deep Learning
Samuel Lerman, Chenliang Xu, Charles Venuto +1
We present a simple yet highly generalizable method for explaining interacting parts within a neural network's reasoning process. First, we design an algorithm based on cross deriv…
Inferring Nighttime Satellite Imagery from Human Mobility
Brian Dickinson, Gourab Ghoshal, Xerxes Dotiwalla +2
Nighttime lights satellite imagery has been used for decades as a uniform, global source of data for studying a wide range of socioeconomic factors. Recently, another more terrestr…
Stimulating Creativity with FunLines: A Case Study of Humor Generation in Headlines
Nabil Hossain, John Krumm, Tanvir Sajed +1
Building datasets of creative text, such as humor, is quite challenging. We introduce FunLines, a competitive game where players edit news headlines to make them funny, and where t…
CupQ: A New Clinical Literature Search Engine
Jesse Wang, Henry Kautz
A new clinical literature search engine, called CupQ, is presented. It aims to help clinicians stay updated with medical knowledge. Although PubMed is currently one of the most wid…
Predicting Acute Kidney Injury at Hospital Re-entry Using High-dimensional Electronic Health Record Data
Samuel J. Weisenthal, Caroline Quill, Samir Farooq +2
Acute Kidney Injury (AKI), a sudden decline in kidney function, is associated with increased mortality, morbidity, length of stay, and hospital cost. Since AKI is sometimes prevent…
Analyzing Uncivil Speech Provocation and Implicit Topics in Online Political News
Rijul Magu, Nabil Hossain, Henry Kautz
Online news has made dissemination of information a faster and more efficient process. Additionally, the shift from a print medium to an online interface has enabled user interacti…
Tales of Two Cities: Using Social Media to Understand Idiosyncratic Lifestyles in Distinctive Metropolitan Areas
Tianran Hu, Eric Bigelow, Jiebo Luo +1
Lifestyles are a valuable model for understanding individuals' physical and mental lives, comparing social groups, and making recommendations for improving people's lives. In this…
Inferring Fine-grained Details on User Activities and Home Location from Social Media: Detecting Drinking-While-Tweeting Patterns in Communities
Nabil Hossain, Tianran Hu, Roghayeh Feizi +3
Nearly all previous work on geo-locating latent states and activities from social media confounds general discussions about activities, self-reports of users participating in those…
Job-related discourse on social media
Tong Liu, Christopher M. Homan, Cecilia Ovesdotter Alm +3
Working adults spend nearly one third of their daily time at their jobs. In this paper, we study job-related social media discourse from a community of users. We use both crowdsour…
Location-Based Reasoning about Complex Multi-Agent Behavior
Adam Sadilek, Henry Kautz
Recent research has shown that surprisingly rich models of human activity can be learned from GPS (positional) data. However, most effort to date has concentrated on modeling singl…
Reasoning Under the Principle of Maximum Entropy for Modal Logics K45, KD45, and S5
Tivadar Papai, Henry Kautz, Daniel Stefankovic
We propose modal Markov logic as an extension of propositional Markov logic to reason under the principle of maximum entropy for modal logics K45, KD45, and S5. Analogous to propos…
A Bayesian Approach to Tackling Hard Computational Problems
Eric J. Horvitz, Yongshao Ruan, Carla P. Gomes +3
We are developing a general framework for using learned Bayesian models for decision-theoretic control of search and reasoningalgorithms. We illustrate the approach on the specific…
Towards Understanding and Harnessing the Potential of Clause Learning
P. Beame, H. Kautz, A. Sabharwal
Efficient implementations of DPLL with the addition of clause learning are the fastest complete Boolean satisfiability solvers and can handle many significant real-world problems,…