activity
20242026
most citedQwen2-VL: Enhancing Vision-Language Model's Perception of the World at Any Resolution

84 citations · 161 across the 27 of their papers we have counts for

collaborators
Showing 2025 · cs.CLShow all

7 papers · 2 filters

cs.CL2025

SWE-RM: Execution-free Feedback For Software Engineering Agents

KaShun Shum, Binyuan Hui, Jiawei Chen +6

Execution-based feedback like unit testing is widely used in the development of coding agents through test-time scaling (TTS) and reinforcement learning (RL). This paradigm require…

cs.CL2025

PlotCraft: Pushing the Limits of LLMs for Complex and Interactive Data Visualization

Jiajun Zhang, Jianke Zhang, Zeyu Cui +7

Recent Large Language Models (LLMs) have demonstrated remarkable proficiency in code generation. However, their ability to create complex visualizations for scaled and structured d…

cs.CL2025

VideoAgentTrek: Computer Use Pretraining from Unlabeled Videos

Dunjie Lu, Yiheng Xu, Junli Wang +12

Training computer-use agents requires massive amounts of GUI interaction data, but manually annotating action trajectories at scale is prohibitively expensive. We present VideoAgen…

cs.CL2025

IFEvalCode: Controlled Code Generation

Jian Yang, Wei Zhang, Shukai Liu +9

Code large language models (Code LLMs) have made significant progress in code generation by translating natural language descriptions into functional code; however, real-world appl…

cs.CL2025

SWE-Flow: Synthesizing Software Engineering Data in a Test-Driven Manner

Lei Zhang, Jiaxi Yang, Min Yang +6

We introduce **SWE-Flow**, a novel data synthesis framework grounded in Test-Driven Development (TDD). Unlike existing software engineering data that rely on human-submitted issues…

cs.CL2025

Multi-Agent Collaboration for Multilingual Code Instruction Tuning

Jian Yang, Wei Zhang, Jiaxi Yang +9

Recent advancement in code understanding and generation demonstrates that code LLMs fine-tuned on a high-quality instruction dataset can gain powerful capabilities to address wide-…

cs.CL2025★ 4 cited

CodeElo: Benchmarking Competition-level Code Generation of LLMs with Human-comparable Elo Ratings

Shanghaoran Quan, Jiaxi Yang, Bowen Yu +14

With the increasing code reasoning capabilities of existing large language models (LLMs) and breakthroughs in reasoning models like OpenAI o1 and o3, there is a growing need to dev…