Merge t18-dropout into main

Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>

# Conflicts:
#	README.md
#	crates/xtrain-autodiff/tests/autograd.rs
#	crates/xtrain-model/src/model.rs
#	crates/xtrain-train/src/bin/train.rs
#	crates/xtrain-train/src/train_loop.rs
#	docs/evolution.md

crates/xtrain-autodiff/src/ops.rs

@@ -140,6 +140,31 @@ pub fn swiglu(gate: &Var, up: &Var) -> Var {
     mul(&silu(gate), up)
 }
 
+/// Dropout (Phase T18). With probability `p` zero each element, scale the kept
+/// ones by `1/(1-p)` (inverted dropout — `E[out] == x`). The keep/drop mask is
+/// drawn by a counter-based RNG from `(seed, element index)`, so it is fully
+/// determined by `seed` (same `seed` ⇒ same mask: stable across the T13 recompute
+/// re-run, and held fixed across the ± perturbation of a finite-diff grad-check).
+/// Forward caches the per-element scale `mask`; **backward applies the same mask**
+/// (`dx = d ⊙ mask`), making dropout a fixed elementwise linear map of `x`.
+///
+/// `p == 0` is a no-op: returns `x.clone()` (no node added) so the default graph
+/// is bit-identical to the no-dropout path. eval-time identity is handled by the
+/// caller simply not invoking dropout (the model's train/eval switch).
+pub fn dropout(x: &Var, p: f32, seed: u64) -> Var {
+    if p == 0.0 {
+        return x.clone();
+    }
+    let (out, mask) = x.value().dropout(p, seed);
+    Var::from_op(
+        out,
+        vec![x.clone()],
+        Box::new(move |d, parents| {
+            Var::push_grad(&parents[0], Tensor::dropout_backward(d, &mask));
+        }),
+    )
+}
+
 /// RoPE (rotate_half) over `x:[tokens,heads,head_dim]` with per-sequence position
 /// `row % period` (`period` = sequence length; `period == tokens` for a single
 /// sequence). Orthogonal map, so the backward is the inverse rotation of `dy` — no