most citedA Survey on Agentic Multimodal Large Language Models

1 citations · 1 across the 4 of their papers we have counts for

collaborators

6 papers

cs.CV2025

Proto-Former: Unified Facial Landmark Detection by Prototype Transformer

Shengkai Hu, Haozhe Qi, Jun Wan +4

Recent advances in deep learning have significantly improved facial landmark detection. However, existing facial landmark detection datasets often define different numbers of landm…

cs.CV20251 cited

A Survey on Agentic Multimodal Large Language Models

Huanjin Yao, Ruifei Zhang, Jiaxing Huang +8

With the recent emergence of revolutionary autonomous agentic systems, research community is witnessing a significant shift from traditional static, passive, and domain-specific AI…

cs.LG2025

Intra-Trajectory Consistency for Reward Modeling

Chaoyang Zhou, Shunyu Liu, Zengmao Wang +4

Reward models are critical for improving large language models (LLMs), particularly in reinforcement learning from human feedback (RLHF) or inference-time verification. Current rew…

cs.AI2025

Consistent Paths Lead to Truth: Self-Rewarding Reinforcement Learning for LLM Reasoning

Kongcheng Zhang, Qi Yao, Shunyu Liu +5

Recent advances of Reinforcement Learning (RL) have highlighted its potential in complex reasoning tasks, yet effective training often relies on external supervision, which limits…

cs.CL2025

Supervised Optimism Correction: Be Confident When LLMs Are Sure

Junjie Zhang, Rushuai Yang, Shunyu Liu +5

In this work, we establish a novel theoretical connection between supervised fine-tuning and offline reinforcement learning under the token-level Markov decision process, revealing…

cs.CL2025

SeRL: Self-Play Reinforcement Learning for Large Language Models with Limited Data

Wenkai Fang, Shunyu Liu, Yang Zhou +5

Recent advances have demonstrated the effectiveness of Reinforcement Learning (RL) in improving the reasoning capabilities of Large Language Models (LLMs). However, existing works…