most citedThe Landscape of Agentic Reinforcement Learning for LLMs: A Survey

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5 papers

cs.AI20261 cited

The Landscape of Agentic Reinforcement Learning for LLMs: A Survey

Guibin Zhang, Hejia Geng, Xiaohang Yu +22

The emergence of agentic reinforcement learning (Agentic RL) marks a paradigm shift from conventional reinforcement learning applied to large language models (LLM RL), reframing LL…

cs.LG2025

Khan-GCL: Kolmogorov-Arnold Network Based Graph Contrastive Learning with Hard Negatives

Zihu Wang, Boxun Xu, Hejia Geng +1

Graph contrastive learning (GCL) has demonstrated great promise for learning generalizable graph representations from unlabeled data. However, conventional GCL approaches face two…

cs.MA2025

ReSo: A Reward-driven Self-organizing LLM-based Multi-Agent System for Reasoning Tasks

Heng Zhou, Hejia Geng, Xiangyuan Xue +5

Multi-agent systems (MAS) have emerged as a promising approach for enhancing the reasoning capabilities of large language models in complex problem-solving; however, current MAS fr…

cs.NE2025

HoSNN: Adversarially-Robust Homeostatic Spiking Neural Networks with Adaptive Firing Thresholds

Hejia Geng, Peng Li

While spiking neural networks (SNNs) offer a promising neurally-inspired model of computation, they are vulnerable to adversarial attacks. We present the first study that draws ins…

cs.NE2024

DS2TA: Denoising Spiking Transformer with Attenuated Spatiotemporal Attention

Boxun Xu, Hejia Geng, Yuxuan Yin +1

Vision Transformers (ViT) are current high-performance models of choice for various vision applications. Recent developments have given rise to biologically inspired spiking transf…