10 papers
One Run Is Not an Idea: The Implementation Lottery in Automated Research
Jingjie Ning, Shanshan Zhong, Xiaochuan Li +2
The paper studies how automated research systems can draw misleading conclusions when they rely on a single implementation of an idea, introducing the concept of an "implementation…
Auto Research for Materials: Auditable AI-Scientist Workflows with Held-Out Transfer
Jingjie Ning, Xiaochuan Li, Shanshan Zhong +2
Auto Research uses language-model agents to propose, implement, and evaluate machine-learning changes in a closed loop, but is usually judged by its terminal pipeline. A terminal s…
Closed-loop Auto Research for Molecular Property Prediction: Discovering and Certifying Generalizable Improvements
Jingjie Ning, Xiaochuan Li, Ji Zeng +2
Closed-loop Auto Research extends automated machine learning from fixed-dataset fitting to changing the research workflow, with language-model agents editing representations and mo…
Beyond Parallel Sampling: Diverse Query Initialization for Agentic Search
Sidhaarth Murali, João Coelho, Jingjie Ning +3
Test-time scaling for agentic search typically increases depth (i.e., more turns and tokens per trajectory) or breadth (i.e., more parallel rollouts). Here we focus on breadth scal…
Auto Research with Specialist Agents Develops Effective and Non-Trivial Training Recipes
Jingjie Ning, Xiaochuan Li, Ji Zeng +2
We study auto research as a closed empirical loop driven by external measurement. Each submitted trial carries a hypothesis, an executable code edit, an evaluator-owned outcome, an…
Agentic Search in the Wild: Intents and Trajectory Dynamics from 14M+ Real Search Requests
Jingjie Ning, João Coelho, Yibo Kong +5
LLM-powered search agents are increasingly being used for multi-step information seeking tasks, yet the IR community lacks empirical understanding of how agentic search sessions un…