1 citations · 1 across the 5 of their papers we have counts for
7 papers
Science Earth: Towards A Planet-Scale Operating System for AI-Native Scientific Discovery
Zhe Zhao, Haibin Wen, Yingcheng Wu +10
Scientific discovery demands intelligence, perseverance, and serendipity across vast search spaces. Today, top scientific capabilities remain siloed--one AI system for biological a…
Wisdom of Committee: Diverse Distillation from Large Foundation Models and Domain Experts
Zichang Liu, Qingyun Liu, Yuening Li +6
Knowledge distillation from foundation models to compact domain models is challenging due to substantial gaps in capacity, architecture, and modality. For example, in our experimen…
Scaling Agentic Capabilities via Grounded Interaction Synthesis
Wenhang Shi, Jinhao Dong, Yiren Chen +4
General agentic intelligence hinges on the ability to interact with diverse real-world tools to complete complex tasks, a capability fundamentally tied to the quality of interactio…
Training Prompt Matters: State-Adaptive Optimization for Robust Fine-Tuning
Wenhang Shi, Yiren Chen, Shuqing Bian +5
While prompt engineering is instrumental in maximizing the capabilities of Large Language Models (LLMs) during inference, the role of prompts during training remains critically und…
ANDES: Agent Native Data Evolving Synthesis Tool for Autonomous Instruction Alignment
Zhengyang Zhao, Shengjie Ye, Lu Ma +3
AI agents are increasingly being tasked with automating AI research itself, particularly the critical post-training phase that transforms base LLMs into aligned assistants. However…
Self-Evolving LLMs via Continual Instruction Tuning
Jiazheng Kang, Le Huang, Cheng Hou +3
In real-world industrial settings, large language models (LLMs) must learn continually to keep pace with diverse and evolving tasks, requiring self-evolution to refine knowledge un…