1 citations · 1 across the 8 of their papers we have counts for
8 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…
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…
Memory OS of AI Agent
Jiazheng Kang, Mingming Ji, Zhe Zhao +1
Large Language Models (LLMs) face a crucial challenge from fixed context windows and inadequate memory management, leading to a severe shortage of long-term memory capabilities and…
Efficient Multi-task Prompt Tuning for Recommendation
Ting Bai, Le Huang, Yue Yu +4
With the expansion of business scenarios, real recommender systems are facing challenges in dealing with the constantly emerging new tasks in multi-task learning frameworks. In thi…
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…