Attention Residuals: How Kimi Is Rethinking Transformer Depth

<p>Most "multimodal" AI products today are really a relay team. One model plans, then hands the job to an image model, a video model, a detector, or a robot policy. Reka's new

<h1> Kimi Linear: How Moonshot AI Built a Hybrid Attention Architecture That Beats Full Attention </h1> <p>The standard Transformer attention mechanism has a well-known problem:

arXiv:2603.22300v1 Announce Type: cross Abstract: Scaling Transformers to ultra-long contexts is bottlenecked by the $O(n^2 d)$ cost of self-attention. Existing methods reduce

<p>Last week, a Baseten inference engineer who goes by <a href="https://x.com/waterloo_intern/status/2081762065392541951" rel="noopener noreferrer">@waterloo_intern</a> published

arXiv:2608.01075v1 Announce Type: new Abstract: Depth-routing residual architectures allow Transformer layers to retrieve earlier representations instead of inheriting only the

<!-- SC_OFF --><div class="md"><p><a href="https://www.reddit.com/r/MachineLearning/?f=flair_name%3A%22Research%22"></a>Macrocosmos has released a paper on ResBM (Residual

<a href="https://news.ycombinator.com/item?id=49082022">Comments</a>

<!-- SC_OFF --><div class="md"><p><em>Scaling language models to handle longer input sequences typically necessitates large key-value (KV) caches, resulting in substantial memory