1 citations · 5 across the 16 of their papers we have counts for
16 papers
DPIAgent: Divide, Protocol, Isolate for Agentic Reproduction Test Generation
Hao Liu, Steven Liu, Xin Zhang +8
Reproduction test generation, producing a failing-then-passing test that captures a reported bug, is a critical step in automated software engineering. Existing agentic methods tre…
ResearchStudio-Reel: Automate the Last Mile of Research from Paper to Poster, Video, and Blog
Lingao Xiao, Yalun Dai, Yangyu Huang +17
Despite growing automation, turning a paper into a coherent poster, talk video, and blog piece often remains a labor-intensive last mile. Recent systems increasingly generate multi…
ResearchStudio-Idea: An Evidence-Grounded Research-Ideation Skill Suite from ML Conference Outcomes
Qihao Zhao, Yangyu Huang, Yalun Dai +8
Large language models have made research ideation increasingly accessible, yet effective idea development requires more than generating candidate directions. Researchers must groun…
Demystifying Data Organization for Enhanced LLM Training
Yalun Dai, Yangyu Huang, Tongshen Yang +8
Large Language Models (LLMs) have revolutionized various fields, yet their training efficiency is heavily reliant on effective data curation. While data selection has been widely s…
TestExplora: Benchmarking LLMs for Proactive Bug Discovery via Repository-Level Test Generation
Steven Liu, Jane Luo, Xin Zhang +7
Given that Large Language Models (LLMs) are increasingly applied to automate software development, comprehensive software assurance spans three distinct goals: regression preventio…
X-Coder: Advancing Competitive Programming with Synthetic Tasks, Solutions, and Tests
Jie Wu, Haoling Li, Xin Zhang +8
Competitive programming remains challenging for code LLMs. Despite recent progress, many training pipelines still depend on scarce real-world data, raising concerns about scalabili…
Latent Sketchpad: Sketching Visual Thoughts to Elicit Multimodal Reasoning in MLLMs
Huanyu Zhang, Wenshan Wu, Chengzu Li +9
While Multimodal Large Language Models (MLLMs) excel at visual understanding, they often struggle in complex scenarios that require visual planning and imagination. Inspired by how…
RPG: A Repository Planning Graph for Unified and Scalable Codebase Generation
Jane Luo, Xin Zhang, Steven Liu +11
Large language models excel at generating individual functions or single files of code, yet generating complete repositories from scratch remains a fundamental challenge. This capa…
Data Efficacy for Language Model Training
Yalun Dai, Yangyu Huang, Xin Zhang +6
Data is fundamental to the training of language models (LM). Recent research has been dedicated to data efficiency, which aims to maximize performance by selecting a minimal or opt…
FEA-Bench: A Benchmark for Evaluating Repository-Level Code Generation for Feature Implementation
Wei Li, Xin Zhang, Zhongxin Guo +6
Implementing new features in repository-level codebases is a crucial application of code generation models. However, current benchmarks lack a dedicated evaluation framework for th…
Teaching Your Models to Understand Code via Focal Preference Alignment
Jie Wu, Haoling Li, Xin Zhang +8
Preference learning extends the performance of Code LLMs beyond traditional supervised fine-tuning by leveraging relative quality comparisons. In existing approaches, a set of n ca…
PEACE: Empowering Geologic Map Holistic Understanding with MLLMs
Yangyu Huang, Tianyi Gao, Haoran Xu +8
Geologic map, as a fundamental diagram in geology science, provides critical insights into the structure and composition of Earth's subsurface and surface. These maps are indispens…
EpiCoder: Encompassing Diversity and Complexity in Code Generation
Yaoxiang Wang, Haoling Li, Xin Zhang +10
Existing methods for code generation use code snippets as seed data, restricting the complexity and diversity of the synthesized data. In this paper, we introduce a novel feature t…
MMLU-CF: A Contamination-free Multi-task Language Understanding Benchmark
Qihao Zhao, Yangyu Huang, Tengchao Lv +8
Multiple-choice question (MCQ) datasets like Massive Multitask Language Understanding (MMLU) are widely used to evaluate the commonsense, understanding, and problem-solving abiliti…
RedStone: Curating General, Code, Math, and QA Data for Large Language Models
Yaoyao Chang, Lei Cui, Li Dong +13
Pre-training Large Language Models (LLMs) on high-quality, meticulously curated datasets is widely recognized as critical for enhancing their performance and generalization capabil…
WaveCoder: Widespread And Versatile Enhancement For Code Large Language Models By Instruction Tuning
Zhaojian Yu, Xin Zhang, Ning Shang +5
Recent work demonstrates that, after instruction tuning, Code Large Language Models (Code LLMs) can obtain impressive capabilities to address a wide range of code-related tasks. Ho…