1 citations · 1 across the 9 of their papers we have counts for
11 papers
ActiveSaddler: Automated Curriculum Learning for Agent Harness Optimization
Sungho Park, Wonjoong Kim, Jue Zhang +8
Automated harness optimization can substantially improve LLM agents by iteratively updating their prompts, tool interfaces, and control logic from execution feedback. However, exis…
AutoSaddler: Automatic Harness Optimization with Durable Updates from Agent Execution Traces
Sungho Park, Wonjoong Kim, Rongyuan Tan +10
LLM agents remain unreliable on long-horizon tasks, where small local failures can compound over extended interactions and lead to overall task failure. Although external harnesses…
ORACLE-SWE: Quantifying the Contribution of Critical Information Signals on SWE Agents
Kenan Li, Qirui Jin, Liao Zhu +13
Recent advances in Language Model (LM) agents have increasingly focused on repository-level software engineering (SWE) tasks, leading to various agentic workflows, training methods…
SWE-Edit: Rethinking Code Editing for Efficient SWE-Agent
Yikai Zhang, Jiaxin Pei, Kenan Li +9
Large language model agents have made strong progress on software engineering, yet current systems suffer from a context coupling problem: the standard code editing interface confl…
RepoLaunch: Automating Build and Management of Code Repositories across Languages and Platforms
Kenan Li, Rongzhi Li, Linghao Zhang +17
Language model (LM) agents have driven substantial progress in automated software engineering (SWE), yet building and testing software repositories at scale remains a largely manua…
Sphinx: Benchmarking and Modeling for LLM-Driven Pull Request Review
Daoan Zhang, Shuo Zhang, Zijian Jin +3
Pull request (PR) review is essential for ensuring software quality, yet automating this task remains challenging due to noisy supervision, limited contextual understanding, and in…
DevBench: A Realistic, Developer-Informed Benchmark for Code Generation Models
Adarsh Kumarappan, Pareesa Ameneh Golnari, Wen Wen +5
DevBench is a telemetry-driven benchmark designed to evaluate Large Language Models (LLMs) on realistic code completion tasks. It includes 1,800 evaluation instances across six pro…
Lita: Light Agent Uncovers the Agentic Coding Capabilities of LLMs
Hankun Dai, Maoquan Wang, Mengnan Qi +6
Large language models (LLMs) are increasingly being applied to programming tasks, ranging from single-turn code completion to autonomous agents. Current code agent designs frequent…
SWE-bench Goes Live!
Linghao Zhang, Shilin He, Chaoyun Zhang +12
The issue-resolving task, where a model generates patches to fix real-world bugs, has emerged as a critical benchmark for evaluating the capabilities of large language models (LLMs…
Skeleton-Guided-Translation: A Benchmarking Framework for Code Repository Translation with Fine-Grained Quality Evaluation
Xing Zhang, Jiaheng Wen, Fangkai Yang +11
The advancement of large language models has intensified the need to modernize enterprise applications and migrate legacy systems to secure, versatile languages. However, existing…
DI-BENCH: Benchmarking Large Language Models on Dependency Inference with Testable Repositories at Scale
Linghao Zhang, Junhao Wang, Shilin He +13
Large Language Models have advanced automated software development, however, it remains a challenge to correctly infer dependencies, namely, identifying the internal components and…