activity
20232026
most citedWhiteFox: White-Box Compiler Fuzzing Empowered by Large Language Models

64 citations · 116 across the 21 of their papers we have counts for

collaborators

23 papers

cs.SE2026

Graphectory Viewer: A Tool for Process-Centric Analysis of Agentic Software Trajectories

Charlie Jyu, Shuyang Liu, Reyhaneh Jabbarvand

We present Graphectory Viewer, a web-based tool for interactive, process-centric analysis of software-agent trajectories. Building on the Graphectory representation introduced in o…

cs.SE2026

Online Monitoring and Corrective Steering of Programming Agents

Shuyang Liu, Saman Dehghan, Ji Young Kim +3

Fixing GitHub issues in large-scale projects is a long-horizon task, especially when a fix requires changes across multiple locations or the issue description lacks the information…

cs.SE2026

Unlocking Model Potentials Through Adaptive Multi-Agent Scaffolding for Efficient Issue Resolution

Yang Chen, Aliya Ahmad, Yiheng Zhou +1

Resolving issues with ambiguous and incomplete descriptions, particularly concerning complex bugs, requires a sophisticated, long-horizon workflow. Agents must navigate codebases t…

cs.SE2026

From Plan to Action: How Well Do Agents Follow the Plan?

Shuyang Liu, Saman Dehghan, Jatin Ganhotra +2

Agents are commonly instructed to follow a task-specific plan for guidance. However, it is unknown to what extent agents actually follow instructed plans. Without such an analysis,…

cs.SE2026

ReCodeAgent: A Multi-agent Workflow for Language-Agnostic Translation and Validation of Large-Scale Repositories

Ali Reza Ibrahimzada, Brandon Paulsen, Daniel Kroening +1

Most repository-level code translation and validation techniques have been evaluated on a single source-target programming language (PL) pair, owing to the complex engineering effo…

cs.SE2026

Narrowing the Complexity Gap in the Evaluation of Large Language Models

Yang Chen, Shuyang Liu, Reyhaneh Jabbarvand

Evaluating Large Language Models (LLMs) with respect to real-world code complexity is essential. Otherwise, there is a risk of overestimating LLMs' programming abilities based on s…