1 citations · 1 across the 7 of their papers we have counts for
7 papers
PalmClaw: A Native On-Device Agent Framework for Mobile Phones
Hongru Cai, Yongqi Li, Ran Wei +1
Large Language Model (LLM) agents have moved beyond generating responses to executing multi-step tasks by calling tools, observing the results, and iteratively deciding the next ac…
Agent-as-a-Judge
Runyang You, Hongru Cai, Caiqi Zhang +5
LLM-as-a-Judge has revolutionized AI evaluation by leveraging large language models for scalable assessments. However, as evaluands become increasingly complex, specialized, and mu…
One Adapts to Any: Meta Reward Modeling for Personalized LLM Alignment
Hongru Cai, Yongqi Li, Tiezheng Yu +4
Alignment of Large Language Models (LLMs) aims to align outputs with human preferences, and personalized alignment further adapts models to individual users. This relies on persona…
Omni-R1: Towards the Unified Generative Paradigm for Multimodal Reasoning
Dongjie Cheng, Yongqi Li, Zhixin Ma +5
Multimodal Large Language Models (MLLMs) are making significant progress in multimodal reasoning. Early approaches focus on pure text-based reasoning. More recent studies have inco…
Exploring Training and Inference Scaling Laws in Generative Retrieval
Hongru Cai, Yongqi Li, Ruifeng Yuan +4
Generative retrieval reformulates retrieval as an autoregressive generation task, where large language models (LLMs) generate target documents directly from a query. As a novel par…
Large Language Models Empowered Personalized Web Agents
Hongru Cai, Yongqi Li, Wenjie Wang +4
Web agents have emerged as a promising direction to automate Web task completion based on user instructions, significantly enhancing user experience. Recently, Web agents have evol…
Revolutionizing Text-to-Image Retrieval as Autoregressive Token-to-Voken Generation
Yongqi Li, Hongru Cai, Wenjie Wang +5
Text-to-image retrieval is a fundamental task in multimedia processing, aiming to retrieve semantically relevant cross-modal content. Traditional studies have typically approached…