Quantitative Models and Construction Methods 1. Model Name: RNN-LIN - **Model Construction Idea**: Simplify the traditional GRU model by using a linear RNN structure, reducing parameter complexity while maintaining competitive performance[2][17][20] - **Model Construction Process**: - The model uses a linear RNN structure with only a forget gate and an output gate. The hidden state is updated without non-linear activation functions - Equations: $ h_{t} = f_{t} \otimes h_{t-1} + (1 - f_{t}) \otimes c_{t} $ $ y_{t} = o_{t} \otimes h_{t} $ $ f_{t} = Sigmoid(x_{t}W_{f}) $ $ o_{t} = Sigmoid(x_{t}W_{o}) $ $ c_{t} = SiLU(x_{t}W_{c}) $ - $f_{t}$: Forget gate - $o_{t}$: Output gate - $c_{t}$: Candidate state[20][21] - The model reduces parameters by approximately 50% compared to GRU[21] - **Evaluation**: The linear RNN model shows slightly weaker performance than GRU but remains competitive. Adding GLU modules improves its performance significantly[22][53] 2. Model Name: DecompGRN - **Model Construction Idea**: Extend the linear RNN by integrating cross-sectional information directly into the RNN gating mechanism, enabling simultaneous modeling of temporal and cross-sectional data[2][50] - **Model Construction Process**: - The first RNN layer outputs individual stock representations at each time step - Cross-sectional information is incorporated by grouping stocks based on market capitalization and calculating group de-meaned values - The second RNN layer combines temporal and cross-sectional information in the forget and output gates - Equations: $ h_{t} = f_{t} \otimes h_{t-1} + (1 - f_{t}) \otimes c_{t} $ $ y_{t} = o_{t} \otimes h_{t} $ $ f_{t} = Sigmoid(x_{t}W_{f}) $ $ o_{t} = Sigmoid(x_{t}W_{o}) $ $ c_{t} = SiLU(x_{t}W_{c}) $ - $f_{t}$: Forget gate - $o_{t}$: Output gate - $c_{t}$: Candidate state[50][55] - **Evaluation**: DecompGRN outperforms the GRU baseline in terms of RankIC and RankICIR while maintaining only 43% of the GRU's parameter count[74][53] --- Model Backtest Results 1. RNN-LIN - **RankIC**: - CSI All Share: 0.13 - CSI 300: 0.10 - CSI 500: 0.09 - CSI 1000: 0.12[36][37] - **RankICIR**: - CSI All Share: 1.08 - CSI 300: 0.62 - CSI 500: 0.71 - CSI 1000: 0.96[36][37] - **IC Win Rate**: - CSI All Share: 0.88 - CSI 300: 0.74 - CSI 500: 0.78 - CSI 1000: 0.86[36][37] - **Annualized Return (Top Group)**: - CSI All Share: 42.59% - CSI 300: 28.59% - CSI 500: 23.68% - CSI 1000: 32.81%[42] 2. DecompGRN - **RankIC**: - CSI All Share: 0.141 - CSI 300: 0.099 - CSI 500: 0.098 - CSI 1000: 0.127[55][58] - **RankICIR**: - CSI All Share: 1.26 - CSI 300: 0.65 - CSI 500: 0.77 - CSI 1000: 1.08[55][58] - **IC Win Rate**: - CSI All Share: 0.89 - CSI 300: 0.74 - CSI 500: 0.78 - CSI 1000: 0.88[55][58] - **Annualized Return (Top Group)**: - CSI All Share: 57.68% - CSI 300: 31.69% - CSI 500: 26.9% - CSI 1000: 40.35%[57][58] --- Index Enhancement Test Results (DecompGRN) - **Annualized Excess Return**: - CSI 300: 10.24% - CSI 500: 10.05% - CSI 1000: 19.58%[75][85] - **Tracking Error**: - CSI 300: 5.07 - CSI 500: 6.1 - CSI 1000: 6.75[75][85] - **Cumulative Excess Return (as of 2025-08-27)**: - CSI 300: 3.93% - CSI 500: 6.72% - CSI 1000: 18.26%[75][85]

Core Viewpoint - The article discusses the development of NeuralOS, a neural network-driven operating system that can simulate a graphical user interface (GUI) similar to Windows, predicting the next frame of screen images based on user interactions [1][2][4]. Group 1: NeuralOS Development - NeuralOS was inspired by a prediction from expert Karpathy about the future of AI-driven GUIs, which will be fluid, magical, and interactive [4][5]. - The research team from the University of Waterloo and the National Research Council of Canada created a demo version of NeuralOS [5][6]. Group 2: Technical Mechanism - NeuralOS utilizes two core components: Recurrent Neural Networks (RNN) for tracking computer state changes and a Renderer for generating corresponding screen images [7][8]. - The training process involved using extensive video recordings of user interactions with the Ubuntu XFCE system, including both random and realistic user behaviors [10][11]. Group 3: Performance Evaluation - The model demonstrated high accuracy in predicting screen states, with most predictions aligning closely with actual states, although it struggled with rapid keyboard inputs [14][15]. - The interface changes generated by NeuralOS during continuous operations appeared nearly indistinguishable from a real system, showcasing its potential for realistic simulations [15]. Group 4: Research Team - The research team consists of five members, with four being of Chinese descent, highlighting a diverse background in AI and machine learning [17][19][21][23][27][29]. Group 5: Future Implications - The development of NeuralOS suggests a shift towards dynamic, AI-generated operating systems, moving away from traditional static interfaces [37].