Fei Wang

Dysco: Dynamic Subspace Boosting to Mitigate LoRA Interference in Federated Learning

Haobo Zhang, Jiankun Wang, Suraj Rajendran, Weishen Pan, Lam Tsoi, Yong Chen, et al.

Federated fine-tuning of large pre-trained models increasingly relies on Low-Rank Adaptation (LoRA) to reduce communication and computation, but heterogeneous clients can make adapter aggregation unstable. We identify the data-parameter interference as a geometric source of this…Read more

ABot-AgentOS: A General Robotic Agent OS with Lifelong Multi-modal Memory

Jiayi Tian, Shiao Liu, Yuting Xu, Jia Lu, Zihao Guan, Honglin Han, et al.

Recent VLM and VLA systems have improved robotic perception and action prediction, yet long-horizon embodied agents still require a general runtime layer for reasoning, memory, tool use, verification, and cross-embodiment execution. We present ABot-AgentOS, a general robotic Agen…Read more

TextGaze: Prompting Gaze Target Estimation with Textual Scene Cues

Junhui She, Fei Wang, Kun Li, Yiqi Nie, Yuxin Liu, Zhangling Duan, et al.

Gaze target estimation aims to infer the position of a person's gaze within a scene. Within mainstream design logic, multi-branch methods require extra supervision and annotations, while streamlined designs prioritize low-level visual saliency over true gaze intent. The former le…Read more

Zeus: Towards Tuning-Free Foundation Model for Time Series Analysis

Yisong Fu, Zezhi Shao, Chengqing Yu, Yujie Li, Yongjun Xu, Xueqi Cheng, et al.

We present Zeus, a unified tuning-free Time Series Foundation Model (TSFM) that delivers superior performance across diverse analysis tasks without any task-specific fine-tuning. Unlike prior studies that primarily focus on zero-shot forecasting but require task-specific tuning f…Read more

Unraveling rate-strengthening and fracture-weakening effects in fractured rock masses: A hybrid bonded particle model-discrete fracture network and physics-inspired machine learning framework

Changtai Zhou, Jiadong Qiu, Murat Karakus, Chengzilong Wang, Fan Feng, Fei Wang, et al.

Beyond Activation Alignment:The Alignment-Diversity Tradeoff in Task-Aware LLM Quantization

Fei Wang, Chao Xue, Taoran Liu, Li Shen, Ye Liu, ChangXing Ding

Mixed-precision quantization (MPQ) has become a key technique for deploying large language models under stringent memory and compute constraints. We first identify a phenomenon that we term the Perplexity Illusion: layers ranked as important by perplexity-based sensitivity show l…Read more

Rethinking the Role of Feature Engineering and Learning Strategies in Few-Shot Hidden Emotion Recognition

Xiaochuan Guo, Jihao Gu, Haixu Liu, Yuxin Liu, Qi Wang, Yufei Wang, et al.

In this paper, we present the solution developed by our team, XInsight Lab, which achieved first place in Track 3 of the 4th EI-MIGA-IJCAI Challenge with a test accuracy of 0.76923. To address the challenge of weak and sparse implicit emotion evidence in long videos, this paper e…Read more

Domain-Decomposed Randomized Neural Networks for Partial Differential Equations in Unbounded Domains

Haixin Wang, Haoning Dang, Fei Wang, Shimin Guo

Partial differential equations on unbounded domains are challenging because the exterior region must be represented without excessive truncation error. Truncation-based methods often require problem-dependent artificial boundary conditions, while global spectral bases may be inef…Read more

Randomized neural operator for parametric PDEs with fast training and conformal uncertainty quantification

Zirui Deng, Jingbo Sun, Deyu Meng, Fei Wang

Repeatedly solving parametric PDEs is essential for uncertainty quantification, design optimization and inverse problems, but conventional neural operators require expensive non-convex training. We introduce PCA--RaNN, a randomized latent neural operator that combines PCA-based d…Read more

Fourier Neural Operators with Least-Squares Readout Refit for Learning Random Obstacle-to-Solution Maps

Chenhui Zhu, Fei Wang

We study operator learning for random obstacle-to-solution maps arising from elliptic variational inequalities with finite-band self-affine random obstacle fields. Instead of introducing an explicit truncated stochastic parametrization of the random input, we learn the map direct…Read more