16 citations · 39 across the 14 of their papers we have counts for
15 papers
GRADS: Graph-Regulated Agentic Data Synthesis with Controllable Difficulty
Gaocheng Yang, Ruijie Tao, Kai Ye +3
Deep research agents are increasingly deployed to retrieve precise information from large corpora, yet multi-hop, long-horizon tasks remain challenging. Synthetic data generation h…
Symbolon: Symbolic Execution by Learning Code Transformation
Jie Zhu, Penghui Li, Zhongxuan Li +4
Symbolic execution is a powerful program analysis technique with broad applications, such as vulnerability detection, security testing, and malware analysis. However, this techniqu…
Locus: Agentic Predicate Synthesis for Directed Fuzzing
Jie Zhu, Chihao Shen, Ziyang Li +3
Directed fuzzing aims to find program inputs that lead to specified target program states. It has broad applications, such as debugging system crashes, confirming reported bugs, an…
TurnaboutLLM: A Deductive Reasoning Benchmark from Detective Games
Yuan Yuan, Muyu He, Muhammad Adil Shahid +3
This paper introduces TurnaboutLLM, a novel framework and dataset for evaluating the deductive reasoning abilities of Large Language Models (LLMs) by leveraging the interactive gam…
Challenges and Paths Towards AI for Software Engineering
Alex Gu, Naman Jain, Wen-Ding Li +7
AI for software engineering has made remarkable progress recently, becoming a notable success within generative AI. Despite this, there are still many challenges that need to be ad…
Lobster: A GPU-Accelerated Framework for Neurosymbolic Programming
Paul Biberstein, Ziyang Li, Joseph Devietti +1
Neurosymbolic programs combine deep learning with symbolic reasoning to achieve better data efficiency, interpretability, and generalizability compared to standalone deep learning…
Relational Programming with Foundation Models
Ziyang Li, Jiani Huang, Jason Liu +6
Foundation models have vast potential to enable diverse AI applications. The powerful yet incomplete nature of these models has spurred a wide range of mechanisms to augment them w…
DISCRET: Synthesizing Faithful Explanations For Treatment Effect Estimation
Yinjun Wu, Mayank Keoliya, Kan Chen +7
Designing faithful yet accurate AI models is challenging, particularly in the field of individual treatment effect estimation (ITE). ITE prediction models deployed in critical sett…
Data-Efficient Learning with Neural Programs
Alaia Solko-Breslin, Seewon Choi, Ziyang Li +4
Many computational tasks can be naturally expressed as a composition of a DNN followed by a program written in a traditional programming language or an API call to an LLM. We call…
IRIS: LLM-Assisted Static Analysis for Detecting Security Vulnerabilities
Ziyang Li, Saikat Dutta, Mayur Naik
Software is prone to security vulnerabilities. Program analysis tools to detect them have limited effectiveness in practice due to their reliance on human labeled specifications. L…
Understanding the Effectiveness of Large Language Models in Detecting Security Vulnerabilities
Avishree Khare, Saikat Dutta, Ziyang Li +3
While automated vulnerability detection techniques have made promising progress in detecting security vulnerabilities, their scalability and applicability remain challenging. The r…
Improved Logical Reasoning of Language Models via Differentiable Symbolic Programming
Hanlin Zhang, Jiani Huang, Ziyang Li +2
Pre-trained large language models (LMs) struggle to perform logical reasoning reliably despite advances in scale and compositionality. In this work, we tackle this challenge throug…
Scallop: A Language for Neurosymbolic Programming
Ziyang Li, Jiani Huang, Mayur Naik
We present Scallop, a language which combines the benefits of deep learning and logical reasoning. Scallop enables users to write a wide range of neurosymbolic applications and tra…
LASER: A Neuro-Symbolic Framework for Learning Spatial-Temporal Scene Graphs with Weak Supervision
Jiani Huang, Ziyang Li, Mayur Naik +1
Supervised approaches for learning spatio-temporal scene graphs (STSG) from video are greatly hindered due to their reliance on STSG-annotated videos, which are labor-intensive to…
GREASE: A Generative Model for Relevance Search over Knowledge Graphs
Tianshuo Zhou, Ziyang Li, Gong Cheng +2
Relevance search is to find top-ranked entities in a knowledge graph (KG) that are relevant to a query entity. Relevance is ambiguous, particularly over a schema-rich KG like DBped…