diff --git a/squish/catalog.py b/squish/catalog.py
index 6d701eee..4c4ce97a 100644
--- a/squish/catalog.py
+++ b/squish/catalog.py
@@ -1220,9 +1220,11 @@ def _hf_list_files(repo: str, token: str | None = None) -> list[str]:  # pragma:
 
         try:
             return list(list_repo_files(repo, token=token))
-        except Exception:
+        except Exception as exc:  # noqa: BLE001 — HF listing; [] is a documented non-fatal fallback
+            _LOG.debug("list_repo_files(%s) failed: %s", repo, exc)
             return []
-    except Exception:
+    except Exception as exc:  # noqa: BLE001 — optional HF dependency/import; [] is non-fatal
+        _LOG.debug("HF repo file listing unavailable for %s: %s", repo, exc)
         return []
 
 
diff --git a/squish/quant/hqq.py b/squish/quant/hqq.py
index 8117ac2e..99811369 100644
--- a/squish/quant/hqq.py
+++ b/squish/quant/hqq.py
@@ -284,18 +284,25 @@ def decode(self, tensor: HQQTensor) -> np.ndarray:
         else:
             dim_size = rows
             other_dim = cols
+            # encode() stores axis-1 codes transposed back to the original shape
+            # (rows, cols); undo that so the group reshape below operates along
+            # the quantized axis as (other_dim, dim_size).
+            codes = codes.T
 
         n_groups = tensor.scale.shape[-1]
-        group_size_actual = max(1, (dim_size + n_groups - 1) // n_groups)
+        # Use the group size encode actually used — recomputing it as
+        # ceil(dim_size / n_groups) is wrong whenever dim_size is not an exact
+        # multiple of group_size, misaligning every group against its scale/zero.
+        group_size = cfg.group_size if cfg.group_size != -1 else dim_size
 
-        padded = n_groups * group_size_actual
+        padded = n_groups * group_size
         if codes.shape[-1] < padded:
             codes_pad = np.zeros((other_dim, padded), dtype=np.float32)
             codes_pad[:, : codes.shape[-1]] = codes
         else:
             codes_pad = codes
 
-        codes_g = codes_pad.reshape(other_dim, n_groups, group_size_actual)
+        codes_g = codes_pad.reshape(other_dim, n_groups, group_size)
         scales = tensor.scale[:, :, np.newaxis]   # (O, G, 1)
         zeros = tensor.zero[:, :, np.newaxis]
         W_hat = codes_g * scales + zeros
diff --git a/squish/streaming/streaming_sink.py b/squish/streaming/streaming_sink.py
index 0920453b..68b1eec3 100644
--- a/squish/streaming/streaming_sink.py
+++ b/squish/streaming/streaming_sink.py
@@ -80,21 +80,23 @@ class SinkStats:
     Attributes:
         n_tokens_seen: Total tokens added since last :meth:`SinkKVCache.reset`.
         n_evictions: Number of tokens evicted from the rolling window.
+        window_size: Rolling-window capacity, used as the util_fraction denominator.
     """
 
     n_tokens_seen: int = 0
     n_evictions: int = 0
+    window_size: int = 0
 
     @property
     def util_fraction(self) -> float:
         """Fraction of the rolling window currently occupied (0–1).
 
-        Returns 0.0 before any tokens are added.  Value is based on
-        ``n_tokens_seen`` relative to the window size, clamped to [0.0, 1.0].
-        This is a snapshot metric only — callers needing exact occupancy
-        should inspect :attr:`SinkKVCache.n_recent` directly.
+        Returns 0.0 before any tokens are added.  Value is ``n_tokens_seen``
+        relative to the window size, clamped to [0.0, 1.0]. This is a snapshot
+        metric only — callers needing exact occupancy should inspect
+        :attr:`SinkKVCache.n_recent` directly.
         """
-        return min(1.0, float(self.n_tokens_seen) / max(1, self.n_tokens_seen))
+        return min(1.0, float(self.n_tokens_seen) / max(1, self.window_size))
 
     @property
     def total_tokens_held(self) -> int:
@@ -237,6 +239,7 @@ def get_stats(self) -> SinkStats:
         return SinkStats(
             n_tokens_seen=self._n_tokens_seen,
             n_evictions=self._n_evictions,
+            window_size=self._config.window_size,
         )
 
     # ------------------------------------------------------------ convenience
diff --git a/tests/quant/test_hqq_decode_group_size.py b/tests/quant/test_hqq_decode_group_size.py
new file mode 100644
index 00000000..1250df5a
--- /dev/null
+++ b/tests/quant/test_hqq_decode_group_size.py
@@ -0,0 +1,57 @@
+"""Regression: HQQ decode must use the stored config group size.
+
+decode() recomputed group_size as ceil(dim_size / n_groups), which differs from
+the real group size whenever dim_size is not an exact multiple of it — every
+group then misaligned against its scale/zero and reconstruction error blew up.
+"""
+from __future__ import annotations
+
+import numpy as np
+import pytest
+
+from squish.quant.hqq import HQQConfig, HQQQuantizer
+
+
+def _rel_err(a: np.ndarray, b: np.ndarray) -> float:
+    return float(np.linalg.norm(a - b) / np.linalg.norm(a))
+
+
+@pytest.mark.parametrize("dim,group_size", [(100, 30), (130, 64), (96, 32), (100, 25)])
+@pytest.mark.parametrize("axis", [0, 1])
+def test_decode_roundtrip_non_divisible_group(dim, group_size, axis):
+    rng = np.random.default_rng(0)
+    w = (rng.standard_normal((dim, 4)) if axis == 1
+         else rng.standard_normal((4, dim))).astype(np.float32)
+    q = HQQQuantizer(HQQConfig(bits=4, group_size=group_size, axis=axis))
+    recon = q.decode(q.encode(w))
+    assert recon.shape == w.shape
+    # 4-bit HQQ on unit Gaussian keeps relative error well under 0.15.
+    assert _rel_err(w, recon) < 0.15
+
+
+def test_axis1_roundtrip_was_broken_before_fix():
+    # decode() never transposed the stored axis-1 codes back, so axis=1 raised
+    # a broadcast error end-to-end (even on aligned dims).
+    rng = np.random.default_rng(5)
+    w = rng.standard_normal((96, 4)).astype(np.float32)
+    q = HQQQuantizer(HQQConfig(bits=4, group_size=32, axis=1))
+    recon = q.decode(q.encode(w))
+    assert recon.shape == w.shape
+    assert _rel_err(w, recon) < 0.15
+
+
+def test_non_divisible_was_broken_before_fix():
+    # Sharpened guard: the non-aligned case used to be ~0.30 rel error.
+    rng = np.random.default_rng(1)
+    w = rng.standard_normal((4, 100)).astype(np.float32)
+    q = HQQQuantizer(HQQConfig(bits=4, group_size=30, axis=0))
+    assert _rel_err(w, q.decode(q.encode(w))) < 0.15
+
+
+def test_full_row_group_size_minus_one():
+    rng = np.random.default_rng(2)
+    w = rng.standard_normal((4, 100)).astype(np.float32)
+    q = HQQQuantizer(HQQConfig(bits=4, group_size=-1, axis=0))
+    recon = q.decode(q.encode(w))
+    assert recon.shape == w.shape
+    assert np.isfinite(recon).all()
diff --git a/tests/streaming/test_sink_util_fraction.py b/tests/streaming/test_sink_util_fraction.py
new file mode 100644
index 00000000..d8fd5158
--- /dev/null
+++ b/tests/streaming/test_sink_util_fraction.py
@@ -0,0 +1,29 @@
+"""Regression: SinkStats.util_fraction must divide by window size, not itself.
+
+The formula was n_tokens_seen / max(1, n_tokens_seen), which is 1.0 for any
+nonzero count regardless of window size — so a barely-used cache reported 100%
+utilization.
+"""
+from __future__ import annotations
+
+from squish.streaming.streaming_sink import SinkConfig, SinkKVCache, SinkStats
+
+
+def test_partial_window_reports_real_fraction():
+    assert SinkStats(n_tokens_seen=5, window_size=256).util_fraction == 5 / 256
+
+
+def test_zero_tokens_is_zero():
+    assert SinkStats(n_tokens_seen=0, window_size=256).util_fraction == 0.0
+
+
+def test_overfull_is_clamped_to_one():
+    assert SinkStats(n_tokens_seen=512, window_size=256).util_fraction == 1.0
+
+
+def test_get_stats_populates_window_size():
+    cache = SinkKVCache(SinkConfig(n_sink_tokens=4, window_size=128), n_heads=2, head_dim=8)
+    stats = cache.get_stats()
+    assert stats.window_size == 128
+    # A fresh cache has seen no tokens → 0 utilization (not 1.0).
+    assert stats.util_fraction == 0.0