2 citations · 5 across the 23 of their papers we have counts for
8 papers · 1 filter
SLPO: Scaling Latent Reasoning via a Surrogate Policy
Runyang You, Zhiyuan Liu, Yongqi Li +1
Reinforcement learning with verifiable rewards has become the predominant recipe for eliciting test-time scaling in explicit Chain-of-Thought reasoners. Yet this scaling path remai…
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…
TInR: Exploring Tool-Internalized Reasoning in Large Language Models
Qiancheng Xu, Yongqi Li, Fan Liu +3
Tool-Integrated Reasoning (TIR) has emerged as a promising direction by extending Large Language Models' (LLMs) capabilities with external tools during reasoning. Existing TIR meth…
Where and What: Reasoning Dynamic and Implicit Preferences in Situated Conversational Recommendation
Dongding Lin, Jian Wang, Yongqi Li +1
Situated conversational recommendation (SCR), which utilizes visual scenes grounded in specific environments and natural language dialogue to deliver contextually appropriate recom…
AR-Omni: A Unified Autoregressive Model for Any-to-Any Generation
Dongjie Cheng, Ruifeng Yuan, Yongqi Li +5
Real-world perception and interaction are inherently multimodal, encompassing not only language but also vision and speech, which motivates the development of "Omni" MLLMs that sup…
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…