Card: add the measured Pixel 8a rows (GPU vs CPU) that the Android sweep produced

README.md

@@ -170,6 +170,17 @@ Task-level parity against the PyTorch reference on the demo prompt with four lan
 
 On the iPhone 17 Pro the int8 file reproduces the desktop outputs **bit-exactly** — cosine 1.000000, max absolute difference 0.0.
 
+### Android (Pixel 8a)
+
+Android figures use the standard TFLite [`benchmark_model`](https://ai.google.dev/edge/litert/models/measurement) on a **Pixel 8a** (Tensor G3, Android 16) — 5 warm-up runs then 20 timed runs, the signature selected explicitly with `--signature_to_run_for`, CPU at 4 threads.
+
+| Signature | GPU (OpenCL) | CPU (XNNPACK, 4 threads) |
+|---|---|---|
+| `route_128` | 348 ms | **133 ms** |
+| `route_512` | 1558 ms | **611 ms** |
+
+**Use the CPU on Android.** The OpenCL delegate claims almost none of the graph — 3 of the 24 nodes in the selected signature's subgraph — and every signature lands 2.6–2.8× slower on the GPU than on the CPU. This is the opposite of the vision models in this collection, which are GPU-resident by design.
+
 ## License
 
 LFM Open License v1.0 (see `LICENSE`, unchanged from the base model). Note the license's commercial-use threshold (Section 5). This repository redistributes converted **Derivative Works** of LiquidAI/LFM2.5-Encoder-350M-Prompt-Router with modification notices per Section 4; all credit for the model to [Liquid AI](https://www.liquid.ai/).