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@@ -58,7 +58,7 @@ We also benchmarked [mradermacher's static Q2_K GGUF](https://huggingface.co/mra
 | prefill | 800 t/s | 22.6 t/s |
 | fast QA gate | 10/10, direct answers | 5/10 at short budget, 9/10 given 400 tokens — right answers buried under rambling |
 
-Upstream llama.cpp gained an expert cache on 2026-10-07 (#29887, `--moe-cache-mib`) — a real feature in the right direction, and it is not what this paragraph was written against. The distinction that still matters is reactive vs routing-aware: LRU pays the PCIe miss on the current token; Strata's cache predicts from the router before the miss (measured 85.9% hit). Third-party comparison on a 16 GB card with a 2-bit Flash-Next pack: llama.cpp + 8 GB expert cache 49.4 t/s vs Strata 85.8 t/s, prefill 206 vs 2,320 t/s (local-llm-lab, 2026-10-16). That gap is the engine, not the bits. For a llama.cpp-native path of this model, mradermacher's repo is the right link — this pack needs the Strata engine above (TQ2_T id 144 is not an upstream type).
+Upstream llama.cpp gained an expert cache on 2026-10-07 (#29887, `--moe-cache-mib`), and its eviction policy moved past plain LRU on 2026-11-06 (#31233, ClassLRU — an 8-class recency+frequency hybrid) — real features in the right direction. The distinction that still matters is reactive vs routing-aware: even a smarter eviction policy decides after the router has already missed; Strata's cache predicts from the router before the miss (measured 85.9% hit) and pre-fills from a learned profile. Third-party comparison on a 16 GB card with a 2-bit Flash-Next pack: llama.cpp + 8 GB expert cache 49.4 t/s vs Strata 85.8 t/s, prefill 206 vs 2,320 t/s (local-llm-lab, 2026-10-16). That gap is the engine, not the bits. For a llama.cpp-native path of this model, mradermacher's repo is the right link — this pack needs the Strata engine above (TQ2_T id 144 is not an upstream type).
 
 ## Is it still smart?