7 papers
BackendForge: Benchmarking Agentic End-to-End Code Generation with Backend Services
Yuzhe Guo, Mengzhou Wu, Yuan Cao +4
The paper presents BackendForge, a benchmark that evaluates how well agentic large language models can generate complete, deployable backend services from OpenAPI specifications, u…
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…
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…
UI-Oceanus: Scaling GUI Agents with Synthetic Environmental Dynamics
Mengzhou Wu, Yuzhe Guo, Yuan Cao +16
Scaling generalist GUI agents is hindered by the data scalability bottleneck of expensive human demonstrations and the "distillation ceiling" of synthetic teacher supervision. To t…
KernelBand: Steering LLM-based Kernel Optimization via Hardware-Aware Multi-Armed Bandits
Dezhi Ran, Shuxiao Xie, Mingfang Ji +9
High-performance GPU kernels are critical for efficient LLM serving, yet their optimization remains a bottleneck requiring deep system expertise. While code LLMs show promise in ge…
From User Interface to Agent Interface: Efficiency Optimization of UI Representations for LLM Agents
Dezhi Ran, Zhi Gong, Yuzhe Guo +10
While Large Language Model (LLM) agents show great potential for automated UI navigation such as automated UI testing and AI assistants, their efficiency has been largely overlooke…