activity
20192026
most citedRelational Programming with Foundation Models

6 citations · 8 across the 4 of their papers we have counts for

collaborators

6 papers

cs.CR2026

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…

cs.CL2025

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…

cs.SE20252 cited

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…

cs.PL2025

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…

cs.AI20246 cited

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…

cs.IR2019

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…