本期《TAI快报》探讨了五篇AI前沿论文： Transformers without Normalization：提出动态Tanh替代归一化层，简化Transformer设计并提升效率。 A Deep Reinforcement Learning Approach to Automated Stock Trading, using xLSTM Networks：用xLSTM改进AI炒股策略，收益和稳定性双赢。一种基于 xLSTM 网络的自动股票交易深度强化学习方法：利用 xLSTM 改进 AI 炒股策略，收益与稳定性双丰收。 Compute Optimal Scaling of Skills: Knowledge vs Reasoning：揭示知识问答偏爱大模型，代码生成依赖大数据的新规律。 Temporal Difference Flows：推出时间差分流，直接预测远期状态，突破长时预测瓶颈。 KV-Distill: Nearly Lossless Learnable Context Compression for LLMs：实现1000倍内存压缩，保持语言模型性能。KV-Distill：几乎无损的可学习上下文压缩，实现 1000 倍内存压缩，保持语言模型性能。完整推介：https://mp.weixin.qq.com/s/wA-FDESDa04UWsRfil9FMA
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