8 citations · 8 across the 5 of their papers we have counts for
7 papers
Toward User-Centered Agentic Recommendation: When Does Platform Competition Benefit Users?
Kehan Zheng, Deyao Hong, Qian Li +3
Conventional recommender systems operate within platform-specific catalogs, limiting access to potentially valuable resources across platforms. We propose user-centered agentic rec…
AI Cosmos: An Agentic Infra for Situated Learning
AI Cosmos Team, Jinfeng Zhou, Yuxuan Chen +32
Large language models (LLMs) can improve task performance during learning, yet these gains do not necessarily reflect learners' ability to thereafter apply the knowledge and skills…
The User Asks, Platforms Compete: How Agentic Recommendation Markets Take Shape
Deyao Hong, Kehan Zheng, Qian Li +3
Online recommendation has traditionally taken place after a user enters a platform, which determines the candidate pool and the ranking shown to the user. LLM-based user agents ena…
You Live More Than Once: Towards Hierarchical Skill Meta-Evolving
Xujun Li, Kehan Zheng, Mingyuan Zhao +7
Test-time skill evolving is regarded as a new paradigm for enhancing deployed agentic systems. Existing works mainly focus on hard-coded skill evolving strategies or parametric lea…
Reasoning to Rank: An End-to-End Solution for Exploiting Large Language Models for Recommendation
Kehan Zheng, Deyao Hong, Qian Li +4
Recommender systems are tasked to infer users' evolving preferences and rank items aligned with their intents, which calls for in-depth reasoning beyond pattern-based scoring. Rece…
Beyond Nash Equilibrium: Bounded Rationality of LLMs and humans in Strategic Decision-making
Kehan Zheng, Jinfeng Zhou, Hongning Wang
Large language models are increasingly used in strategic decision-making settings, yet evidence shows that, like humans, they often deviate from full rationality. In this study, we…
Black-Box Prompt Optimization: Aligning Large Language Models without Model Training
Jiale Cheng, Xiao Liu, Kehan Zheng +5
Large language models (LLMs) have shown impressive success in various applications. However, these models are often not well aligned with human intents, which calls for additional…