Real-time simulation and control systems

Derniers papiers

• A Unified Mamba--MoE Surrogate for Closed-Loop Simulation and Measurement-Window Forecasting of Inverter Transients

  Haoguang Wang, Huy Hoang Le, Akhila Kandivalasa, Christian Moya, Marcos Netto, Guang Lin · 18 août 2026

  This paper proposes a Mamba surrogate model with mixture-of-experts (MoE) routing to represent the transient dynamics of inverter-based resources. A Mamba surrogate model is a predictive machine learning model built on the Mamba architecture. MoE routing uses a router network to assign data-dependen…

• MoEGen: Mixture-of-Experts for Instance-Adaptive LoRA Generation

  Yiming Zeng, Lei Lu, Zexin Li, Zhuochun Li, Shuoqiu Li, Shuyi Liao, Xidong Wu, Zeyu Zhang, Minmei Wang, Yu Zhao, Tingting Yu, Shangqian Gao · 5 août 2026

  Parameter-efficient fine-tuning (PEFT) enables efficient adaptation of large language models, but existing MoE-based PEFT methods typically improve capacity by storing multiple full LoRA experts, causing adapter storage to grow linearly with the number of experts and restricting adaptation to a fixe…

• EvoLP: Self-Evolving Latency Predictor for Model Compression in Real-Time Edge Systems

  Shuo Huai, Hao Kong, Shiqing Li, Xiangzhong Luo, Ravi Subramaniam, Christian Makaya, Qian Lin, Weichen Liu · 13 juillet 2026

  Edge devices are increasingly utilized for deploying deep learning applications on embedded systems. The real-time nature of many applications and the limited resources of edge devices necessitate latency-targeted neural network compression. However, measuring latency on real devices is challenging …

• LP5X-PIM Sim: A High-Fidelity HW/SW Integrated Simulator for LPDDR5X-PIM

  SangHoon Cha, Jaewan Choi, Byeongho Kim, Yoonah Paik, Sukhan Lee, Kyomin Sohn · 2 juin 2026

  This tech note describes the architecture and execution results of the LPDDR5X-PIM simulator, developed by Samsung Electronics. Based on the latest research and internal specifications, the simulator provides a high-fidelity model of both the hardware data paths and the software control layers of th…

• Agentic Application in Power Grid Static Analysis: Automatic Code Generation and Error Correction

  Qinjuan Wang, Shan Yang, Yongli Zhu · 14 avril 2026

  This paper introduces an LLM agent that automates power grid static analysis by converting natural language into MATPOWER scripts. The framework utilizes DeepSeek-OCR to build an enhanced vector database from MATPOWER manuals. To ensure reliability, it devises a three-tier error-correction system: a…

• Perturb and Recover: Fine-tuning for Effective Backdoor Removal from CLIP

  Naman Deep Singh, Francesco Croce, Matthias Hein · 8 avril 2026

  Vision-Language models like CLIP have been shown to be highly effective at linking visual perception and natural language understanding, enabling sophisticated image-text capabilities, including strong retrieval and zero-shot classification performance. Their widespread use, as well as the fact that…

• TTA-DAME: Test-Time Adaptation with Domain Augmentation and Model Ensemble for Dynamic Driving Conditions

  Dongjae Jeon, Taeheon Kim, Seongwon Cho, Minhyuk Seo, Jonghyun Choi · 1 avril 2026

  Test-time Adaptation (TTA) poses a challenge, requiring models to dynamically adapt and perform optimally on shifting target domains. This task is particularly emphasized in real-world driving scenes, where weather domain shifts occur frequently. To address such dynamic changes, our proposed method,…

• EasySteer: A Unified Framework for High-Performance and Extensible LLM Steering

  Haolei Xu, Xinyu Mei, Yuchen Yan, Rui Zhou, Wenqi Zhang, Weiming Lu, Yueting Zhuang, Yongliang Shen · 3 mars 2026

  Large language model (LLM) steering has emerged as a promising paradigm for controlling model behavior at inference time through targeted manipulation of hidden states, offering a lightweight alternative to expensive retraining. However, existing steering frameworks suffer from critical limitations:…

• Scaling Offline Model-Based RL via Jointly-Optimized World-Action Model Pretraining

  Jie Cheng, Ruixi Qiao, Yingwei Ma, Binhua Li, Gang Xiong, Qinghai Miao, Yongbin Li, Yisheng Lv · 30 janvier 2026

  A significant aspiration of offline reinforcement learning (RL) is to develop a generalist agent with high capabilities from large and heterogeneous datasets. However, prior approaches that scale offline RL either rely heavily on expert trajectories or struggle to generalize to diverse unseen tasks.…

• Multi-View Oriented GPLVM: Expressiveness and Efficiency

  Zi Yang, Ying Li, Zhidi Lin, Michael Minyi Zhang, Pablo M. Olmos · 16 décembre 2025

  The multi-view Gaussian process latent variable model (MV-GPLVM) aims to learn a unified representation from multi-view data but is hindered by challenges such as limited kernel expressiveness and low computational efficiency. To overcome these issues, we first introduce a new duality between the sp…

• LiLaN: A Linear Latent Network as the Solution Operator for Real-Time Solutions to Stiff Nonlinear Ordinary Differential Equations

  William Cole Nockolds, C. G. Krishnanunni, Tan Bui-Thanh, Xianxhu Tang · 20 novembre 2025

  Solving stiff ordinary differential equations (StODEs) requires sophisticated numerical solvers, which are often computationally expensive. In general, traditional explicit time integration schemes with restricted time step sizes are not suitable for StODEs, and one must resort to costly implicit me…

• OODTE: A Differential Testing Engine for the ONNX Optimizer

  Nikolaos Louloudakis, Ajitha Rajan · 14 novembre 2025

  With over 760 stars on GitHub and being part of the official ONNX repository, the ONNX Optimizer is the default tool for applying graph-based optimizations to ONNX models. Despite its widespread use, its ability to maintain model accuracy during optimization has not been thoroughly investigated. In …

• Efficient Model Development through Fine-tuning Transfer

  Pin-Jie Lin, Rishab Balasubramanian, Fengyuan Liu, Nikhil Kandpal, Tu Vu · 7 novembre 2025

  Modern LLMs struggle with efficient updates, as each new pretrained model version requires repeating expensive alignment processes. This challenge also applies to domain- or languagespecific models, where fine-tuning on specialized data must be redone for every new base model release. In this paper,…

• Decoupled Multi-Predictor Optimization for Inference-Efficient Model Tuning

  Liwei Luo, Shuaitengyuan Li, Dongwei Ren, Qilong Wang, Pengfei Zhu, Qinghua Hu · 6 novembre 2025

  Recently, remarkable progress has been made in large-scale pre-trained model tuning, and inference efficiency is becoming more crucial for practical deployment. Early exiting in conjunction with multi-stage predictors, when cooperated with a parameter-efficient fine-tuning strategy, offers a straigh…