2 citations · 4 across the 26 of their papers we have counts for
6 papers · 1 filter
Understanding Reasoning in LLMs through Strategic Information Allocation under Uncertainty
Jeonghye Kim, Xufang Luo, Minbeom Kim +3
LLMs often exhibit Aha moments such as self-correction after tokens like "Wait," yet the underlying mechanism remains unclear. Standard LLMs collapse mainly through silent divergen…
SkillOpt: Executive Strategy for Self-Evolving Agent Skills
Yifan Yang, Ziyang Gong, Weiquan Huang +12
Agent skills today are hand-crafted, generated one-shot, or evolved through loosely controlled self-revision, none of which behaves like a deep-learning optimizer for the skill, an…
ProRe: A Proactive Reward System for GUI Agents via Reasoner-Actor Collaboration
Gaole Dai, Shiqi Jiang, Ting Cao +5
Reward is critical to the evaluation and training of large language models (LLMs). However, existing rule-based or model-based reward methods struggle to generalize to GUI agents,…
ReMe: Scaffolding Personalized Cognitive Training via Controllable LLM-Mediated Conversations
Zilong Wang, Nan Chen, Luna K. Qiu +6
Global aging calls for scalable and engaging cognitive interventions. Computerized cognitive training (CCT) is a promising non-pharmacological approach, yet many unsupervised progr…
Advancing Mobile GUI Agents: A Verifier-Driven Approach to Practical Deployment
Gaole Dai, Shiqi Jiang, Ting Cao +5
We propose V-Droid, a mobile GUI task automation agent. Unlike previous mobile agents that utilize Large Language Models (LLMs) as generators to directly generate actions at each s…
Agent Lightning: Train ANY AI Agents with Reinforcement Learning
Xufang Luo, Yuge Zhang, Zhiyuan He +5
We present Agent Lightning, a flexible and extensible framework that enables Reinforcement Learning (RL)-based training of Large Language Models (LLMs) for any AI agent. Unlike exi…