Add FlashMLA tutorial and diagrams

README.md

@@ -23,8 +23,9 @@ vehicle is **TIRx** (Tensor IR next), a Python DSL for writing GPU kernels at th
   layout model (`TileLayout`, named axes, swizzle).
 - **Part III — GEMM: Tiled to SOTA.** A tiled GEMM built up through TMA pipelining,
   persistent scheduling, warp specialization, and 2-CTA clusters.
-- **Part IV — Flash Attention 4.** A complete attention kernel built from the Part III techniques:
-  two MMAs with softmax between them, online-softmax rescaling, causal masking, and GQA.
+- **Part IV — Attention Kernels.** Flash Attention 4 and FlashMLA connect the Part III techniques
+  into complete attention pipelines, including online softmax, latent KV representations, sparse
+  gathers, warp specialization, and cooperative two-CTA execution.
 - **Appendices.** TIRx language reference, [reproducible GPU benchmarking and
   profiling](appendix/benchmarking_gpu_kernels.md), compiler internals, and asynchronous-kernel
   debugging.