1 citations · 2 across the 15 of their papers we have counts for
19 papers
AdamPM: Memory-Efficient LLM Optimization via Moment Pooling
Xiaowei Ji, Lishan Xu, Jianing An +3
Adam is widely used for training large language models (LLMs) owing to its strong optimization performance and training stability, but its element-wise estimation for the first and…
Activation-Adjacent Layer Normalization: Effects on Approximation Power
Yunhao Ni, Yuxin Guo, Jie Luo +2
Layer normalization (LN) and root mean square normalization (RMSNorm) depend on their inputs and remain nonlinear at inference. Inserting either next to an activation may therefore…
Kolmogorov-Arnold Networks: Curse of Dimensionality and Structural Bias
Yunhao Ni, Yuxin Guo, Jie Luo +2
Kolmogorov-Arnold networks (KANs) have a dimension-independent approximation exponent when the target admits a sufficiently smooth representation by univariate edge functions. We a…
TPCD: Tone-Pressure Contrastive Decoding and the Label-Free Gating Bottleneck in Vision-Language Models
Jinkun Zhao, Kui Zhang, Wenjun Wu
High-pressure prompts can push vision-language models (VLMs) into unsupported commitments, such as reading illegible text, reporting indeterminate times, or affirming absent object…
Enjoy Your Layer Normalization with the Computational Efficiency of RMSNorm
Yuxin Guo, Yihao Yue, Yunhao Ni +4
Layer normalization (LN) is a fundamental component in modern deep learning, but its per-sample centering and scaling introduce non-negligible inference overhead. RMSNorm improves…
RailVQA: A Benchmark and Framework for Efficient Interpretable Visual Cognition in Automatic Train Operation
Sen Zhang, Runmei Li, Shizhuang Deng +7
As Automatic Train Operation (ATO) advances toward GoA4 and beyond, it increasingly depends on efficient, reliable cab-view visual perception and decision-oriented inference to ens…
EventMemAgent: Hierarchical Event-Centric Memory for Online Video Understanding with Adaptive Tool Use
Siwei Wen, Zhangcheng Wang, Xingjian Zhang +2
Online video understanding requires models to perform continuous perception and long-range reasoning within potentially infinite visual streams. Its fundamental challenge lies in t…
RLLaVA: An RL-central Framework for Language and Vision Assistants
Lei Zhao, Zihao Ma, Boyu Lin +3
We present an RL-central framework for Language and Vision Assistants (RLLaVA) with its formulation of Markov decision process (MDP). RLLaVA decouples RL algorithmic logic from mod…
Tool-Augmented Hybrid Ensemble Reasoning with Distillation for Bilingual Mathematical Problem Solving
Peiqing Lu, Yuan Zhang, Haoyun Zhang +3
Bilingual mathematical problem solving needs a clear link between language reasoning and symbolic calculation. Large language models often handle language well but are weak in accu…
What Color Is the Text? A Benchmark for Hallucination Induced by Image-Embedded Prompt
Jinkun Zhao, Lei Huang, Haixin Ge +1
We introduce Embedded Stroop, a controlled diagnostic paradigm for measuring image-embedded prompt interference in Multimodal Large Language Models (MLLMs), where the query is rend…
IMAGINE: Integrating Multi-Agent System into One Model for Complex Reasoning and Planning
Xikai Zhang, Bo Wang, Likang Xiao +4
Although large language models (LLMs) have made significant strides across various tasks, they still face significant challenges in complex reasoning and planning. For example, eve…
EDGE-GRPO: Entropy-Driven GRPO with Guided Error Correction for Advantage Diversity
Xingjian Zhang, Siwei Wen, Wenjun Wu +1
Large Language Models (LLMs) have made remarkable progress in enhancing step-by-step reasoning through reinforcement learning. However, the Group Relative Policy Optimization (GRPO…
CoE-Ops: Collaboration of LLM-based Experts for AIOps Question-Answering
Jinkun Zhao, Yuanshuai Wang, Xingjian Zhang +6
With the rapid evolution of artificial intelligence, AIOps has emerged as a prominent paradigm in DevOps. Lots of work has been proposed to improve the performance of different AIO…
VoQA: Visual-only Question Answering
Jianing An, Luyang Jiang, Jie Luo +2
Visual understanding requires interpreting both natural scenes and the textual information that appears within them, motivating tasks such as Visual Question Answering (VQA). Howev…
Parallel Layer Normalization for Universal Approximation
Yunhao Ni, Yuxin Guo, Yuhe Liu +4
Layer Normalization (LN) is usually studied for its role in optimization, but it also acts as a multivariate nonlinearity between affine layers. We investigate how grouping LN unit…
TinyLLaVA-Video-R1: Towards Smaller LMMs for Video Reasoning
Xingjian Zhang, Siwei Wen, Wenjun Wu +1
Recently, improving the reasoning ability of large multimodal models (LMMs) through reinforcement learning has made great progress. However, most existing works are based on highly…
Spot the Fake: Large Multimodal Model-Based Synthetic Image Detection with Artifact Explanation
Siwei Wen, Junyan Ye, Peilin Feng +7
With the rapid advancement of Artificial Intelligence Generated Content (AIGC) technologies, synthetic images have become increasingly prevalent in everyday life, posing new challe…
TinyLLaVA-Video: Towards Smaller LMMs for Video Understanding with Group Resampler
Xingjian Zhang, Xi Weng, Yihao Yue +3
Video behavior recognition and scene understanding are fundamental tasks in multimodal intelligence, serving as critical building blocks for numerous real-world applications. Throu…
Bench-CoE: a Framework for Collaboration of Experts from Benchmark
Yuanshuai Wang, Xingjian Zhang, Jinkun Zhao +5
Large Language Models (LLMs) are key technologies driving intelligent systems to handle multiple tasks. To meet the demands of various tasks, an increasing number of LLMs-driven ex…