Author name cluster

Gaole Dai

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6 papers

2 author rows

AAAI Conference 2026 Conference Paper

Decomposing the Neurons: Activation Sparsity via Mixture of Experts for Continual Test Time Adaptation

Rongyu Zhang
Aosong Cheng
Yulin Luo
Gaole Dai
Huanrui Yang
Jiaming Liu
Ran Xu
Li Du

Continual Test-Time Adaptation (CTTA), which aims to adapt the pre-trained model to ever-evolving target domains, emerges as an important task for vision models. As current vision models appear to be heavily biased towards texture, continuously adapting the model from one domain distribution to another can result in serious catastrophic forgetting. Drawing inspiration from the the encoding characteristics of neuron activation in neural networks, we propose the Mixture-of-Activation-Sparsity-Experts (MoASE) for the CTTA task. Given the distinct reaction of neurons with low and high activation to domain-specific and agnostic features, MoASE decomposes the neural activation into high-activation and low-activation components in each expert with a Spatial Differentiable Dropout (SDD). Based on the decomposition, we devise a Domain-Aware Router (DAR) that utilizes domain information to adaptively weight experts that process the post-SDD sparse activations, and the Activation Sparsity Gate (ASG) that adaptively assigns feature selection thresholds of the SDD for different experts for more precise feature decomposition. Finally, we introduce a Homeostatic-Proximal (HP) loss to maintain update consistency between the teacher and student experts to prevent error accumulation. Extensive experiments substantiate that MoASE achieves state-of-the-art performance in both classification and segmentation tasks.

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AAAI Conference 2026 Conference Paper

Learning Systems Expansion with Efficient Heterogeneity-aware Knowledge Transfer

Gaole Dai
Huatao Xu
Yifan Yang
Rui Tan
Mo Li

Modern AI services must continually adapt to newly joined domains, yet delivering high-quality customized models is hampered by label sparsity, domain shifts, and tight budgets. We formulate this challenge as the learning system expansion problem and introduce HaT, an efficient heterogeneity-aware knowledge-transfer framework. HaT first selects a small set of high-quality source models with minimal overhead, and then fuses their imperfect predictions through a sample-wise attention mixer. Later, it adaptively distills the fused knowledge into target models via a knowledge dictionary. Extensive experiments on different tasks and modalities show that HaT outperforms state-of-the-art baselines by up to 16.5% accuracy, and saves 31.1% training time and up to 93.0% traffic.

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AAAI Conference 2026 Conference Paper

MoLe-VLA: Dynamic Layer-skipping Vision Language Action Model via Mixture-of-Layers for Efficient Robot Manipulation

Rongyu Zhang
Menghang Dong
Yuan Zhang
Liang Heng
Xiaowei Chi
Gaole Dai
Li Du
Dan Wang

Vision-Language-Action (VLA) models enable robotic systems to perform embodied tasks but face deployment challenges due to the high computational demands of the dense Large Language Models (LLMs), with existing early-exit-based sparsification methods often overlooking the critical semantic role of final layers in downstream tasks. Aligning with the recent breakthrough of the Shallow Brain Hypothesis (SBH) in neuroscience and the mixture of experts in model sparsification, we conceptualize each LLM layer as an expert and propose a Mixture-of-LayEr Vision Language Action model (MoLe-VLA or simply MoLe) architecture for dynamic LLM layer activation. Specifically, we introduce a Spatial-Temporal Aware Router (STAR) for MoLe to selectively activate only parts of the layers based on the robot’s current state, mimicking the brain's distinct signal pathways specialized for cognition and causal reasoning. Additionally, to compensate for the cognition ability of LLM lost during the layer-skipping, we devise a Cognitive self-Knowledge Distillation (CogKD) to enhance the understanding of task demands and generate task-relevant action sequences by leveraging cognition features. Extensive experiments in RLBench simulations and real-world environments demonstrate the superiority of MoLe-VLA in both efficiency and performance, improving the mean success rate by 9.7% across ten simulation tasks while accelerating inference by 36.8% over OpenVLA.

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NeurIPS Conference 2025 Conference Paper

Orochi: Versatile Biomedical Image Processor

Gaole Dai
Chenghao Zhou
Yu Zhou
Rongyu Zhang
Yuan Zhang
Chengkai Hou
Tiejun Huang
Jianxu Chen

Deep learning has emerged as a pivotal tool for accelerating research in the life sciences, with the low-level processing of biomedical images (e. g. , registration, fusion, restoration, super-resolution) being one of its most critical applications. Platforms such as ImageJ (Fiji) and napari have enabled the development of customized plugins for various models. However, these plugins are typically based on models that are limited to specific tasks and datasets, making them less practical for biologists. To address this challenge, we introduce Orochi, the first application-oriented, efficient, and versatile image processor designed to overcome these limitations. Orochi is pre-trained on patches/volumes extracted from the raw data of over 100 publicly available studies using our Random Multi-scale Sampling strategy. We further propose Task-related Joint-embedding Pre-Training (TJP), which employs biomedical task-related degradation for self-supervision rather than relying on Masked Image Modelling (MIM), which performs poorly in downstream tasks such as registration. To ensure computational efficiency, we leverage Mamba's linear computational complexity and construct Multi-head Hierarchy Mamba. Additionally, we provide a three-tier fine-tuning framework (Full, Normal, and Light) and demonstrate that Orochi achieves comparable or superior performance to current state-of-the-art specialist models, even with lightweight parameter-efficient options. We hope that our study contributes to the development of an all-in-one workflow, thereby relieving biologists from the overwhelming task of selecting among numerous models. Our pre-trained weights and code will be released.

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ICML Conference 2025 Conference Paper

SAN: Hypothesizing Long-Term Synaptic Development and Neural Engram Mechanism in Scalable Model's Parameter-Efficient Fine-Tuning

Gaole Dai
Chun-Kai Fan
Yiming Tang
Zhi Zhang 0009
Yuan Zhang 0020
Yulu Gan
Qizhe Zhang
Cheng-Ching Tseng

Advances in Parameter-efficient Fine-tuning (PEFT) bridged the performance gap with Full Fine-Tuning (FFT) through sophisticated analysis of pre-trained parameter spaces. Starting from drawing insights from Neural Engrams (NE) in Biological Neural Networks (BNNs), we establish a connection between the low-rank property observed during PEFT’s parameter space shifting and neurobiological mechanisms. This observation leads to our proposed method, S ynapse and N euron ( SAN ), which decomposes and propagates the scaling component from anterior feature adjustment vectors towards posterior weight matrices. Our approach is theoretically grounded in Long-Term Potentiation/Depression (LTP/D) phenomena, which govern synapse development through neurotransmitter release modulation. Extensive experiments demonstrate its effectiveness: on vision tasks across VTAB, FGVC, and GIC (25 datasets) using ViT, Swin-T and ConvNeXt architectures, SAN outperforms FFT up to 8. 7% and LoRA by 3. 2%; on language tasks using Commonsense Reasoning (8 datasets) with LLaMA models (all generations), surpassing ChatGPT up to 8. 5% and LoRA by 4. 7%; on vision-language tasks using Visual Instruction Tuning (7 datasets) with LLaVA models, it exceeds FFT up to 2. 4% and LoRA by 1. 9%. Our code and W&B log will be released

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NeurIPS Conference 2025 Conference Paper

UniCTokens: Boosting Personalized Understanding and Generation via Unified Concept Tokens

Ruichuan An
Sihan Yang
Renrui Zhang
zijun shen
Ming Lu
Gaole Dai
Hao Liang
Ziyu Guo

Personalized models have demonstrated remarkable success in understanding and generating concepts provided by users. However, existing methods use separate concept tokens for understanding and generation, treating these tasks in isolation. This may result in limitations for generating images with complex prompts. For example, given the concept $\langle bo\rangle$, generating "$\langle bo\rangle$ wearing its hat" without additional textual descriptions of its hat. We call this kind of generation \textit{\textbf{personalized attribute-reasoning generation}}. To address the limitation, we present UniCTokens, a novel framework that effectively integrates personalized information into a unified vision language model (VLM) for understanding and generation. UniCTokens trains a set of unified concept tokens to leverage complementary semantics, boosting two personalized tasks. Moreover, we propose a progressive training strategy with three stages: understanding warm-up, bootstrapping generation from understanding, and deepening understanding from generation to enhance mutual benefits between both tasks. To quantitatively evaluate the unified VLM personalization, we present UnifyBench, the first benchmark for assessing concept understanding, concept generation, and attribute-reasoning generation. Experimental results on UnifyBench indicate that UniCTokens shows competitive performance compared to leading methods in concept understanding, concept generation, and achieving state-of-the-art results in personalized attribute-reasoning generation. Our research demonstrates that enhanced understanding improves generation, and the generation process can yield valuable insights into understanding. Our code and dataset will be released at: \href{https: //github. com/arctanxarc/UniCTokens}{https: //github. com/arctanxarc/UniCTokens}.

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