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20232026
most citedLLM4SR: A Survey on Large Language Models for Scientific Research

5 citations · 9 across the 13 of their papers we have counts for

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Showing 2026Show all

8 papers · 1 filter

cs.LG2026

Terrarium: Growing Personal Assistants via Scalable Synthesis of App Worlds

Mengzhou Wu, Yuzhe Guo, Jiawen Tao +9

Advances in large language models (LLMs) have brought the vision of a general-purpose personal assistant closer to reality. Such assistants could provide a unified natural-language…

cs.SE2026

TileCas: Cascading Kernel Development Across Programming Abstractions

Xinyu Xiao, Zihe Song, Dezhi Ran +2

Programming abstractions have traditionally been designed for humans, but code generation is increasingly performed by models and autonomous agents. We investigate abstractions for…

cs.AI2026

ContextSniper: AntTrail's Token-Efficient Code Memory for Repository-Level Program Repair

Chiwang Luk, Matin Mohammad Najafi, Zhifeng Jia +6

Large language model agents can repair real repository issues, but they often spend large context budgets on whole-file reads, broad searches, and long terminal outputs where usefu…

cs.SE2026

BackendForge: Benchmarking Agentic End-to-End Code Generation with Backend Services

Yuzhe Guo, Mengzhou Wu, Yuan Cao +4

Large language models (LLMs) are increasingly used in agentic coding settings, where they can inspect files, execute commands, run tests, observe failures, and iteratively revise c…

cs.SE2026

Understanding Automated Web GUI Testing: An Empirical Study Across Exploration Strategies and State Abstractions

Chenxu Liu, Wei Yang, Ying Zhang +1

Automated web GUI testing (AWGT) relies on exploration strategies that exercise web applications through GUI actions to maximize code coverage, spanning traditional model-based, re…

cs.AI2026

An Empirical Study and Theoretical Explanation on Task-Level Model-Merging Collapse

Yuan Cao, Dezhi Ran, Yuzhe Guo +5

Model merging unifies independently fine-tuned LLMs from the same base, enabling reuse and integration of parallel development efforts without retraining. However, in practice we o…

cs.AI2026

GUI-GENESIS: Automated Synthesis of Efficient Environments with Verifiable Rewards for GUI Agent Post-Training

Yuan Cao, Dezhi Ran, Mengzhou Wu +9

Post-training GUI agents in interactive environments is critical for developing generalization and long-horizon planning capabilities. However, training on real-world applications…

cs.SE2026

Synthesizing File-Level Data for Unit Test Generation with Chain-of-Thoughts via Self-Debugging

Ziyue Hua, Tianyu Chen, Yeyun Gong +8

Automatic unit test (UT) generation is essential for software quality assurance, but existing approaches--including symbolic execution, search-based approaches, and recent LLM-base…