variants: replace arrow's dense block with diagonal-plus-low-rank core

antipasto_arrow -> antipasto_dplr. The arrowhead's dense b x b block is the wrong
shape: b^2 params, mixes only the top-b, and sits on the S-scaled coords so its
perturbation is amplified by the largest singular values (block=128 collapsed to
45.7% at the gain's lr). Replace it with LoRA's lesson -- a low-rank core inside
the frozen basis, ADDED to the gain:

    DeltaW = U [diag(S_eff) + coeff * B A] Vh,   A:(k,r) B:(r,k), B=0 at init

The low-rank part mixes the whole top-r subspace for 2*r*k params (k=LoRA's rank),
and being additive (not * diag(S)) it is S-independent -- the amplification edge is
gone by construction. Diagonal gain unchanged; identity at init from B=0 and g=0.

Wired through benchmark (antipasto_lora_rank, run_id __k suffix), justfile, cost_report,
smoke (green, dplr attaches/trains/round-trips). Arrow code removed; its run results
stay on disk for comparison.

Co-Authored-By: Claudypoo <noreply@anthropic.com>

tests/test_metamath_smoke.py

@@ -32,12 +32,12 @@ SPEC.loader.exec_module(benchmark)
 
 VARIANTS = ["lora", "pissa", "delora", "ia3", "ia3_ff", "dora", "hra", "eva",
             "antipasto", "antipasto_rot", "antipasto_ablate", "antipasto_corda",
-            "antipasto_arrow", "road"]
+            "antipasto_dplr", "road"]
 # Variants that fail loud when attached on a bnb-loaded base (read dense weight in init).
 # delora/eva also read weight but currently silently dequant -- they produce sane attach,
 # so we don't expect a raise from them in the attach-only smoke.
 BNB_RAISERS = {"pissa", "dora", "antipasto", "antipasto_rot", "antipasto_ablate",
-               "antipasto_corda", "antipasto_arrow"}
+               "antipasto_corda", "antipasto_dplr"}
 TINY_MODEL = "hf-internal-testing/tiny-random-LlamaForCausalLM"
 
 HAS_CUDA = torch.cuda.is_available()
@@ -59,7 +59,7 @@ def quick_cfg(variant: str, tmp_path: Path, quantization: str = "none") -> "benc
         quantization=quantization,
         r=4,
         alpha=8,
-        antipasto_block=2,  # antipasto_arrow needs block < r (r=4 here)
+        antipasto_lora_rank=2,  # antipasto_dplr needs 0 < lora_rank <= r (r=4 here)
         target_name=target_name,
         layers="all",
         steps=2,