1 citations · 1 across the 13 of their papers we have counts for
14 papers
From Token-Max to Outcome-Max: How You Use AI Determines Its Productivity
Chen Xu, Mengqiao Liu, Beibei Li +1
Generative artificial intelligence (AI) models can perform increasingly complex tasks, yet greater AI usage does not necessarily translate into proportional productivity gains. We…
Mechanism Design for Generative Engines: From Exploitation toward Win-Win Outcomes
Chen Xu, Zitian Guo, Chenyan Xiong
Generative engines are reshaping the web ecosystem by making citations a key mechanism for allocating attention, attribution, and downstream value. This creates a strategic tension…
The Implementation Lottery: Auditing Idea Reliability in Automated Research
Jingjie Ning, Shanshan Zhong, Xiaochuan Li +2
Automated research agents use program scores to judge ideas. We call variation in this evidence across implementations the implementation lottery. We introduce an Idea Reliability…
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…
Effective Reinforcement Learning for Agentic Search by Recycling Zero-Variance Queries During Training
João Coelho, João Magalhães, Bruno Martins +1
The use of GRPO-style algorithms has become the standard strategy for training LLM search agents under outcome-only rewards. With these algorithms, a query contributes to parameter…
AgentWebBench: Benchmarking Multi-Agent Coordination in Agentic Web
Shanshan Zhong, Kate Shen, Chenyan Xiong
Agentic Web is an emerging paradigm where autonomous agents help users use online information. As the paradigm develops, content providers are also deploying agents to manage their…
Efficient Dataset Selection for Continual Adaptation of Generative Recommenders
Cathy Jiao, Juan Elenter, Praveen Ravichandran +7
Recommendation systems must continuously adapt to evolving user behavior, yet the volume of data generated in large-scale streaming environments makes frequent full retraining impr…
An Economic Framework for Generative Engines: Advertising or Subscription?
Luyang Zhang, Cathy Jiao, Beibei Li +1
Generative Engines (GEs) such as ChatGPT and Google's AI Overviews are rapidly reshaping search economics by delivering synthesized responses that allow users to bypass third-party…
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…
Beneficial Reasoning Behaviors in Agentic Search and Effective Post-training to Obtain Them
Jiahe Jin, Abhijay Paladugu, Chenyan Xiong
Agentic search requires large language models (LLMs) to perform multi-step search to solve complex information-seeking tasks, imposing unique challenges on their reasoning capabili…
Aligning Web Query Generation with Ranking Objectives via Direct Preference Optimization
João Coelho, Bruno Martins, João Magalhães +1
Neural retrieval models excel in Web search, but their training requires substantial amounts of labeled query-document pairs, which are costly to obtain. With the widespread availa…
DeepResearchGym: A Free, Transparent, and Reproducible Evaluation Sandbox for Deep Research
João Coelho, Jingjie Ning, Jingyuan He +8
Deep research systems represent an emerging class of agentic information retrieval methods that generate comprehensive and well-supported reports to complex queries. However, most…
Dwell in the Beginning: How Language Models Embed Long Documents for Dense Retrieval
João Coelho, Bruno Martins, João Magalhães +2
This study investigates the existence of positional biases in Transformer-based models for text representation learning, particularly in the context of web document retrieval. We b…