一种新的Transformer改进：只改中间20%的层加个循环机制，就能让模型推理更持久，而且可以直接改装现有模型不用从头训。
T2MLR是一种基于Transformer的潜在推理架构，将前一token的中间层表示缓存并注入当前token的早期层，使抽象中间计算能跨解码步骤持续。在自然语言预训练和多跳推理微调中，T2MLR始终优于同等数据和参数的Transformer基线。仅对20%的网络应用中层循环就能超过全层循环。改装1.7B预训练Transformer并微调后，数学推理性能显著提升。
T^2MLR: Transformer with Temporal Middle-Layer Recurrence
Transformer reasoning is limited by autoregressive decoding, which repeat edly compresses rich hidden computation through token space and makes it difficult for intermediate reasoning states to persist across time. We in troduce Transformers with Temporal Middle-Layer Recurrence (T2MLR), a transformers-based latent reasoning architecture that fuses a cached middle layer representation from the previous token directly into an earlier layer of the current token position, enabling abstract intermediate computation to persist across decoding steps with little inference overhead. Across natural-language pretraining and multi-hop reasoning finetuning, T2MLR consistently outperforms data- and parameter-matched Transformer base lines. Moreover, applying recurrence to only a localized middle-layer block (as little as 20% of the network) often outperforms full-layer recurrence. Im portantly, T2MLR does not require pretraining from scratch: retrofitting the recurrent pathway into an existing pretrained 1.7B Transformer and briefly finetuning substantially improves math reasoning, lowering the barrier to practical adoption. These results suggest that effective latent reasoning in Transformers does not require looping over all layers as in previous works, but can instead emerge more strongly from targeted middle-layer recurrence.