3 citations · 3 across the 31 of their papers we have counts for
12 papers · 1 filter
APT-ClaritySet: A Large-Scale, High-Fidelity Labeled Dataset for APT Malware with Alias Normalization and Graph-Based Deduplication
Zhenhao Yin, Hanbing Yan, Huishu Lu +4
Large-scale, standardized datasets for Advanced Persistent Threat (APT) research are scarce, and inconsistent actor aliases and redundant samples hinder reproducibility. This paper…
AnchorTP: Resilient LLM Inference with State-Preserving Elastic Tensor Parallelism
Wendong Xu, Chujie Chen, He Xiao +8
Large Language Model (LLM) inference services demand exceptionally high availability and low latency, yet multi-GPU Tensor Parallelism (TP) makes them vulnerable to single-GPU fail…
DoPE: Denoising Rotary Position Embedding
Jing Xiong, Liyang Fan, Hui Shen +4
Positional encoding is essential for large language models (LLMs) to represent sequence order, yet recent studies show that Rotary Position Embedding (RoPE) can induce massive acti…
Find Your Optimal Teacher: Personalized Data Synthesis via Router-Guided Multi-Teacher Distillation
Hengyuan Zhang, Shiping Yang, Xiao Liang +8
Training student models on synthetic data generated by strong teacher models is a promising way to distilling the capabilities of teachers. However, recent studies show that strong…
ATTS: Asynchronous Test-Time Scaling via Conformal Prediction
Jing Xiong, Qiujiang Chen, Fanghua Ye +11
Large language models (LLMs) benefit from test-time scaling but are often hampered by high inference latency. Speculative decoding is a natural way to accelerate the scaling proces…
LongEmotion: Measuring Emotional Intelligence of Large Language Models in Long-Context Interaction
Weichu Liu, Jing Xiong, Yuxuan Hu +10
Large language models (LLMs) have made significant progress in Emotional Intelligence (EI) and long-context modeling. However, existing benchmarks often overlook the fact that emot…
Enhancing Test-Time Scaling of Large Language Models with Hierarchical Retrieval-Augmented MCTS
Alex ZH Dou, Zhongwei Wan, Dongfei Cui +6
Test-time scaling has emerged as a promising paradigm in language modeling, leveraging additional computational resources at inference time to enhance model performance. In this wo…
From Objects to Anywhere: A Holistic Benchmark for Multi-level Visual Grounding in 3D Scenes
Tianxu Wang, Zhuofan Zhang, Ziyu Zhu +5
3D visual grounding has made notable progress in localizing objects within complex 3D scenes. However, grounding referring expressions beyond objects in 3D scenes remains unexplore…
SRPO: Enhancing Multimodal LLM Reasoning via Reflection-Aware Reinforcement Learning
Zhongwei Wan, Zhihao Dou, Che Liu +11
Multimodal large language models (MLLMs) have shown promising capabilities in reasoning tasks, yet still struggle with complex problems requiring explicit self-reflection and self-…
PhyX: Does Your Model Have the "Wits" for Physical Reasoning?
Hui Shen, Taiqiang Wu, Qi Han +16
Existing benchmarks fail to capture a crucial aspect of intelligence: physical reasoning, the integrated ability to combine domain knowledge, symbolic reasoning, and understanding…
SwingArena: Competitive Programming Arena for Long-context GitHub Issue Solving
Wendong Xu, Jing Xiong, Chenyang Zhao +16
We present SwingArena, a competitive evaluation framework for Large Language Models (LLMs) that closely mirrors real-world software development workflows. Unlike traditional static…
ParallelComp: Parallel Long-Context Compressor for Length Extrapolation
Jing Xiong, Jianghan Shen, Chuanyang Zheng +7
Extrapolating ultra-long contexts (text length >128K) remains a major challenge for large language models (LLMs), as most training-free extrapolation methods are not only severely…