📄 中文摘要
提出了一种混合神经融合架构，模拟人类核糖体的动态tRNA修饰循环，基于高带宽内存（HBM）平台。该框架结合了双向长短期记忆（Bi-LSTM）预测器和强化学习（RL）控制器，能够实时重新分配内存银行，相较于传统静态调度实现了48%的吞吐量提升。通过使用3200个实验核糖体动力学数据集和GPU加速的蒙特卡洛模拟引擎，展示了神经融合策略在优化甲基转移酶位置方面的有效性，将周期时间方差降低至3.2%，并提高了氮基的保真度。
自适应神经融合框架用于实时核糖体仿生高带宽内存并行甲基转移
📄 English Summary
**Adaptive Neuro‑Fusion Framework for Real‑Time Ribosome‑Mimetic HBM Parallel Methyl Transfer**
A hybrid neuro-fusion architecture is proposed to emulate the dynamic tRNA-modification cycle of the human ribosome within a high-bandwidth memory (HBM) substrate. The framework integrates a bi-directional long short-term memory (Bi-LSTM) predictor with a reinforcement-learning (RL) controller that reallocates memory banks in real-time, achieving a 48% throughput improvement over conventional static scheduling. Utilizing a corpus of 3,200 experimental ribosomal kinetic datasets and a GPU-accelerated Monte-Carlo simulation engine, the neuro-fusion policy optimizes methyl-transferase placement, reducing cycle-time variance to 3.2% and increasing nitrogenous-base fidelity.