4 citations · 4 across the 10 of their papers we have counts for
11 papers
Recall Begets Recall: Recurrent Memory Ranking for Personalized LLMs
Pengyue Jia, Shida Yan, Shunyu Liu +4
Memory ranking is the stage in personalized Large Language Models (LLMs) that decides which of the candidate memories returned by a retriever enter the limited context, so that the…
Better, Faster: Harnessing Self-Improvement in Large Reasoning Models
Qihuang Zhong, Liang Ding, Juhua Liu +3
Self-improvement training enables the large reasoning models (LRMs) to improve themselves by self-generating reasoning trajectories as training data without external supervision. H…
ROAR: Retrieval Opportunity-Aware Refinement for Zero-Shot Time Series Forecasting
Jinjin Chi, Lei Feng, Lulu Zhang +7
Retrieval augmentation provides time series forecasters with historical continuations, yet even candidates that outperform the base forecast may fail to improve the final predictio…
Distillation Traps and Guards: A Calibration Knob for LLM Distillability
Weixiao Zhan, Yongcheng Jing, Leszek Rutkowski +1
Knowledge distillation (KD) transfers capabilities from large language models (LLMs) to smaller students, yet it can fail unpredictably and also underpins model leakage risks. Our…
Resource-constrained Amazons chess decision framework integrating large language models and graph attention
Tianhao Qian, Zhuoxuan Li, Jinde Cao +2
Artificial intelligence has advanced significantly through the development of intelligent game-playing systems, providing rigorous testbeds for decision-making, strategic planning,…
Alternating Gradient Flow Utility: A Unified Metric for Structural Pruning and Dynamic Routing in Deep Networks
Tianhao Qian, Zhuoxuan Li, Jinde Cao +2
Efficient deep learning traditionally relies on static heuristics like weight magnitude or activation awareness (e.g., Wanda, RIA). While successful in unstructured settings, we ob…
Disentangled Representation Learning via Flow Matching
Jinjin Chi, Taoping Liu, Mengtao Yin +5
Disentangled representation learning aims to capture the underlying explanatory factors of observed data, enabling a principled understanding of the data-generating process. Recent…
Rethinking the Role of Dynamic Sparse Training for Scalable Deep Reinforcement Learning
Guozheng Ma, Lu Li, Zilin Wang +4
Scaling neural networks has driven breakthrough advances in machine learning, yet this paradigm fails in deep reinforcement learning (DRL), where larger models often degrade perfor…
MAPO: Mixed Advantage Policy Optimization
Wenke Huang, Quan Zhang, Yiyang Fang +11
Recent advances in reinforcement learning for foundation models, such as Group Relative Policy Optimization (GRPO), have significantly improved the performance of foundation models…
Sequential, Parallel and Consecutive Hybrid Evolutionary-Swarm Optimization Metaheuristics
Piotr Urbańczyk, Aleksandra Urbańczyk, Magdalena Król +2
The goal of this paper is twofold. First, it explores hybrid evolutionary-swarm metaheuristics that combine the features of PSO and GA in a sequential, parallel and consecutive man…
SRD: Reinforcement-Learned Semantic Perturbation for Backdoor Defense in VLMs
Shuhan Xu, Siyuan Liang, Hongling Zheng +6
Visual language models (VLMs) have made significant progress in image captioning tasks, yet recent studies have found they are vulnerable to backdoor attacks. Attackers can inject…