4 citations · 6 across the 8 of their papers we have counts for
9 papers
What Git Does Not Record: Provenance Legibility in Two AI-Assisted Research Repositories
Koutian Wu, Zesen Huang, Hejia Geng
Publishers and funders increasingly ask authors to disclose how AI tools contributed to a piece of research. In computational work the repository is where an author or an auditor w…
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…
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…
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…
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…
DISTA: Denoising Spiking Transformer with intrinsic plasticity and spatiotemporal attention
Boxun Xu, Hejia Geng, Yuxuan Yin +1
Among the array of neural network architectures, the Vision Transformer (ViT) stands out as a prominent choice, acclaimed for its exceptional expressiveness and consistent high per…
UPAR: A Kantian-Inspired Prompting Framework for Enhancing Large Language Model Capabilities
Hejia Geng, Boxun Xu, Peng Li
Large Language Models (LLMs) have demonstrated impressive inferential capabilities, with numerous research endeavors devoted to enhancing this capacity through prompting. Despite t…
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…
Composing Recurrent Spiking Neural Networks using Locally-Recurrent Motifs and Risk-Mitigating Architectural Optimization
Wenrui Zhang, Hejia Geng, Peng Li
In neural circuits, recurrent connectivity plays a crucial role in network function and stability. However, existing recurrent spiking neural networks (RSNNs) are often constructed…