25 citations · 28 across the 15 of their papers we have counts for
4 papers · 1 filter
ToolLoop: Closed-Loop Tool-Use Data Synthesis via Decomposed Generation and Dynamic Self-Feedback
Min Zeng, Yuzhou Liu, Zhenyu Cao +5
High-quality tool-use data is critical for training language models to interact effectively with external tools. However, existing synthetic approaches typically follow a generate-…
VISA: Agentic Self-Evolving Data Synthesis for Multimodal Instruction Following
Min Zeng, Guanxin Tan, Libin Cen +5
Multimodal instruction-following models require training data that is accurate, diverse, verifiable, and challenging. Existing synthesis pipelines typically follow a one-pass gener…
UI-Genie: A Self-Improving Approach for Iteratively Boosting MLLM-based Mobile GUI Agents
Han Xiao, Guozhi Wang, Yuxiang Chai +12
In this paper, we introduce UI-Genie, a self-improving framework addressing two key challenges in GUI agents: verification of trajectory outcome is challenging and high-quality tra…
GenieBlue: Integrating both Linguistic and Multimodal Capabilities for Large Language Models on Mobile Devices
Xudong Lu, Yinghao Chen, Renshou Wu +10
Recent advancements in Multimodal Large Language Models (MLLMs) have enabled their deployment on mobile devices. However, challenges persist in maintaining strong language capabili…