diff --git a/src/api/inference.py b/src/api/inference.py
index d82492a..1db7a26 100644
--- a/src/api/inference.py
+++ b/src/api/inference.py
@@ -26,7 +26,10 @@
     .env({"IMAGE_REV": "6"})
     .add_local_dir(str(Path(__file__).resolve().parent.parent), "/root/interscript-ml", copy=True)
     # deterministic mounts for the index-driven resolver + the index itself
-    .add_local_file(str(Path(__file__).resolve().parent / "model_resolution.py"), "/root/model_resolution.py")
+    .add_local_file(
+        str(Path(__file__).resolve().parent / "model_resolution.py"),
+        "/root/model_resolution.py",
+    )
     .add_local_file(str(_REPO_ROOT / "models.yaml"), "/root/models.yaml")
 )
 
diff --git a/src/api/model_resolution.py b/src/api/model_resolution.py
index 629faeb..1807487 100644
--- a/src/api/model_resolution.py
+++ b/src/api/model_resolution.py
@@ -13,9 +13,10 @@
 
 from __future__ import annotations
 
-import yaml
 from pathlib import Path
 
+import yaml
+
 
 def load_index(path: str | Path = "models.yaml") -> dict:
     with open(path, encoding="utf-8") as fh:
diff --git a/src/gpu/modal_distill.py b/src/gpu/modal_distill.py
index 1094499..19f6749 100644
--- a/src/gpu/modal_distill.py
+++ b/src/gpu/modal_distill.py
@@ -1617,7 +1617,7 @@ def probe_pkm_gates(spec_id: str = "ara-diac-small-pkm") -> dict:
         raise RuntimeError(f"no step checkpoints under {out_root}")
 
     _ensure_src_path()
-    from gpu.pkm import ProductKeyMemory, inject_pkm
+    from gpu.pkm import inject_pkm
 
     student = AutoModelForSeq2SeqLM.from_pretrained(spec["student_init"])
     inject_pkm(student, **spec["pkm"])
diff --git a/src/gpu/muon.py b/src/gpu/muon.py
index 4431dfc..b27d836 100644
--- a/src/gpu/muon.py
+++ b/src/gpu/muon.py
@@ -41,16 +41,27 @@ def __init__(self, params, lr: float = 0.01, momentum: float = 0.95,
                  weight_decay: float = 0.0) -> None:
         super().__init__(
             list(params),
-            dict(lr=lr, momentum=momentum, nesterov=nesterov,
-                 ns_steps=ns_steps, weight_decay=weight_decay, adamw=False),
+            {
+                "lr": lr,
+                "momentum": momentum,
+                "nesterov": nesterov,
+                "ns_steps": ns_steps,
+                "weight_decay": weight_decay,
+                "adamw": False,
+            },
         )
 
     def add_adamw_group(self, params, lr: float = 1e-4, betas=(0.9, 0.999),
                         weight_decay: float = 0.0) -> None:
         """Embedding-like parameters: standard AdamW math, shared
         scheduler (the cosine scales every group's lr)."""
-        self.add_param_group(dict(params=list(params), lr=lr, betas=tuple(betas),
-                                  weight_decay=weight_decay, adamw=True))
+        self.add_param_group({
+            "params": list(params),
+            "lr": lr,
+            "betas": tuple(betas),
+            "weight_decay": weight_decay,
+            "adamw": True,
+        })
 
     @torch.no_grad()
     def step(self, closure=None):  # noqa: ARG002
diff --git a/src/imf/parity.py b/src/imf/parity.py
index 62ff899..ef33ed3 100644
--- a/src/imf/parity.py
+++ b/src/imf/parity.py
@@ -277,9 +277,8 @@ def run_margin_analysis(model, zip_path, pairs, max_len: int = 256) -> MarginRep
 def write_margin_report(report: MarginReport, out_path: Path | str) -> Path:
     """Emit the margin analysis as JSON next to a release zip (diagnostic
     artifact; the release gate remains the CER parity block)."""
-    from dataclasses import asdict
-
     import json
+    from dataclasses import asdict
 
     out_path = Path(out_path)
     out_path.parent.mkdir(parents=True, exist_ok=True)
diff --git a/tests/test_decode_guard.py b/tests/test_decode_guard.py
index 679d85e..282b7b3 100644
--- a/tests/test_decode_guard.py
+++ b/tests/test_decode_guard.py
@@ -22,24 +22,24 @@ def __init__(self):
         self.step = 0
 
     def get_outputs(self):
-        class O:
+        class Out:
             def __init__(self, name):
                 self.name = name
 
-        return [O("logits"), O("present_k"), O("present_v")]
+        return [Out("logits"), Out("present_k"), Out("present_v")]
 
     def get_inputs(self):
-        class I:
+        class In:
             def __init__(self, name, shape, typ="tensor(float)"):
                 self.name = name
                 self.shape = shape
                 self.type = typ
 
         return [
-            I("input_ids", [1, "seq"]),
-            I("encoder_hidden_states", [1, "seq", 4]),
-            I("past_k", [1, 4, "past", 8]),
-            I("past_v", [1, 4, "past", 8]),
+            In("input_ids", [1, "seq"]),
+            In("encoder_hidden_states", [1, "seq", 4]),
+            In("past_k", [1, 4, "past", 8]),
+            In("past_v", [1, 4, "past", 8]),
         ]
 
     def run(self, _, feeds):
diff --git a/tests/test_imf_parity.py b/tests/test_imf_parity.py
index 60c5f99..3510cef 100644
--- a/tests/test_imf_parity.py
+++ b/tests/test_imf_parity.py
@@ -23,7 +23,6 @@
     make_fixture_checkpoint,
 )
 from imf.parity import (  # noqa: E402
-    MarginReport,
     ParityReport,
     _margin_stats,
     run_margin_analysis,