docs(bench): document MLX exact recall experiment

docs/benchmarks/apple-uma-mlx-exact-recall.md

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+# Apple UMA MLX Exact CGA Recall Experiment
+
+ADR-0235 Lane 3 introduces an optional, benchmark-only MLX exact-recall experiment for CORE's Cl(4,1) CGA recall workload.
+
+This is **not** a serving backend. It does not replace Python or Rust as the semantic source of truth. It does not use ANN, HNSW, approximate recall, sampling, CoreML, or Neural Engine acceleration.
+
+## What it measures
+
+`benchmarks/apple_uma_mlx_exact_recall.py` measures one narrow workload:
+
+```text
+Deterministic (N, 32) float32 fixture matrix
++ deterministic length-32 query
+→ MLX exact diagonal CGA score vector
+→ score vector copied back to NumPy
+→ canonical stable top-k ordering
+→ parity check against algebra.backend.vault_recall
+```
+
+The MLX path computes exact scores only. The final top-k ordering is intentionally kept in NumPy/Python so the experiment does not depend on MLX sorting/top-k API behavior and can reuse CORE's canonical descending-score / ascending-index tie break.
+
+## Run
+
+```bash
+uv run python -m benchmarks.apple_uma_mlx_exact_recall --json
+```
+
+With MLX unavailable, the report must skip cleanly with an explicit reason.
+
+With MLX available, the report emits cases for the standard Apple UMA recall sizes and includes:
+
+- MLX import status and default device when observable
+- `N`, `top_k`, dtype, and contiguity
+- p50/p95/mean timing and rows/sec
+- copy-in boundary: NumPy fixture to MLX array
+- copy-out boundary: MLX score vector to NumPy
+- parity against `algebra.backend.vault_recall`
+- top result preview and canonical preview
+
+## Non-claims
+
+This experiment does **not** claim:
+
+- MLX is a semantic backend
+- MLX is serving-authorized
+- CoreML or Neural Engine acceleration
+- zero-copy everywhere
+- ANN or approximate recall
+- token-generation throughput
+- Apple endorsement, sponsorship, or product integration
+
+## Validation
+
+```bash
+uv run python -m pytest -q tests/test_apple_uma_mlx_exact_recall.py
+uv run python -m benchmarks.apple_uma_mlx_exact_recall --json
+```
+
+When MLX is installed on Apple Silicon, also run:
+
+```bash
+CORE_BACKEND=rust uv run python -m benchmarks.apple_uma_mlx_exact_recall --json
+```
+
+The `parity.parity_pass` field must be true for every emitted case before using results in any Apple-facing material.