1 citations · 2 across the 8 of their papers we have counts for
16 papers
When Elo Lies: Hidden Biases in Codeforces-Based Evaluation of Large Language Models
Shenyu Zheng, Ximing Dong, Xiaoshuang Liu +6
As Large Language Models (LLMs) achieve breakthroughs in complex reasoning, Codeforces-based Elo ratings have emerged as a prominent metric for evaluating competitive programming c…
SWE-Effi: Re-Evaluating Software AI Agent System Effectiveness Under Resource Constraints
Zhiyu Fan, Kirill Vasilevski, Dayi Lin +6
The advancement of large language models (LLMs) and code agents has demonstrated significant potential to assist software engineering (SWE) tasks, such as autonomous issue resoluti…
RepoForge: Training a SOTA Fast-thinking SWE Agent with an End-to-End Data Curation Pipeline Synergizing SFT and RL at Scale
Zhilong Chen, Chengzong Zhao, Boyuan Chen +9
Training software engineering (SWE) LLMs is bottlenecked by expensive infrastructure, inefficient evaluation pipelines, scarce training data, and costly quality control. We present…
Towards Reliable Generation of Executable Workflows by Foundation Models
Sogol Masoumzadeh, Keheliya Gallaba, Dayi Lin +1
Recent advancements in Foundation Models (FMs) have demonstrated significant progress in processing complex natural language to perform intricate tasks. Successfully executing thes…
An Empirical Study of Testing Practices in Open Source AI Agent Frameworks and Agentic Applications
Mohammed Mehedi Hasan, Hao Li, Emad Fallahzadeh +3
Foundation model (FM)-based AI agents are rapidly gaining adoption across diverse domains, but their inherent non-determinism and non-reproducibility pose testing and quality assur…
SPICE: An Automated SWE-Bench Labeling Pipeline for Issue Clarity, Test Coverage, and Effort Estimation
Gustavo A. Oliva, Gopi Krishnan Rajbahadur, Aaditya Bhatia +7
High-quality labeled datasets are crucial for training and evaluating foundation models in software engineering, but creating them is often prohibitively expensive and labor-intens…