The biggest bottleneck on letting LLMs handle long contexts is the quadratic complexity of softmax attention. In the past year, delta-rule-based recurrent linear attention has exploded with multiple variants after Mamba / RWKV — DeltaNet, Gated DeltaNet, Kimi Delta Attention, Gated DeltaNet-2 — but the read/write control memory decay differences between them are nearly opaque, and engineering teams can't decide which to pick. On July 8, ETH Zurich's Cerruti et al. re-expressed four recurrent linear attentions using a unified recurrent-memory notation in arXiv:2607.07953, and ran a horizontal comparison at matched settings of 350M parameters and 15B tokens. Three key observations: one, Kimi Delta Attention + the Muon optimizer has the lowest final validation loss, reflecting Moonshot's lead in long-context architectures; two, pure Gated DeltaNet + AdamW has the highest training throughput, while hybrid (mixing softmax and recurrent layers) wins slightly on loss but throughput drops significantly; three, the Muon optimizer stably beats AdamW across all matched architectures, suggesting the community should revisit optimizer choice. The team also proposes a lightweight cross-layer routing mechanism, CLVR (Cross-Layer Value Routing): route the previous layer's write value to the next layer's hidden stream, rather than the more intuitive write error. On both DeltaNet and Gated DeltaNet, CLVR reduces final validation loss. The real value of this paper isn't any single breakthrough, but pulling the past year's fragmented linear attention experiments onto the same controlled-variable table — but it doesn't measure inference latency, while what really matters for production is per-token inference speed at 1k–100k context. The companion code is open-sourced and worth reproducing into downstream long-context models.