1 citations · 1 across the 3 of their papers we have counts for
5 papers
DeepKnown-Guard: A Proprietary Model-Based Safety Response Framework for AI Agents
Qi Li, Jianjun Xu, Pingtao Wei +8
With the widespread application of Large Language Models (LLMs), their associated security issues have become increasingly prominent, severely constraining their trustworthy deploy…
Count Counts: Motivating Exploration in LLM Reasoning with Count-based Intrinsic Rewards
Xuan Zhang, Ruixiao Li, Zhijian Zhou +7
Reinforcement Learning (RL) has become a compelling way to strengthen the multi step reasoning ability of Large Language Models (LLMs). However, prevalent RL paradigms still lean o…
Prompt-aware classifier free guidance for diffusion models
Xuanhao Zhang, Chang Li
Diffusion models have achieved remarkable progress in image and audio generation, largely due to Classifier-Free Guidance. However, the choice of guidance scale remains underexplor…
Learn the Ropes, Then Trust the Wins: Self-imitation with Progressive Exploration for Agentic Reinforcement Learning
Yulei Qin, Xiaoyu Tan, Zhengbao He +13
Reinforcement learning (RL) is the dominant paradigm for sharpening strategic tool use capabilities of LLMs on long-horizon, sparsely-rewarded agent tasks, yet it faces a fundament…
Universal Retrieval for Multimodal Trajectory Modeling
Xuan Zhang, Ziyan Jiang, Rui Meng +5
Trajectory data, capturing human actions and environmental states across various modalities, holds significant potential for enhancing AI agent capabilities, particularly in GUI en…