diff --git a/tests/unit/test_golden.py b/tests/unit/test_golden.py
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+"""Golden regression tests — guard against catastrophic divergence from
+the pre-migration PyTorch implementation.
+
+Important caveat on tolerances
+==============================
+
+These goldens were captured with the legacy PyTorch + scipy stack, where the
+RNG path was ``torch.manual_seed(seed)`` + ``torch.randn(...)`` plus
+``np.random.seed`` for the duration sampler. The rewritten library uses a
+single ``np.random.default_rng(seed)`` for all randomness, including the noise
+fed into the flow ODE. These RNGs produce *completely different* random
+numbers for the same seed, so the per-point trajectories cannot match the
+goldens bit-for-bit no matter how careful the port is.
+
+What this suite therefore guards is **structural** equivalence rather than
+exact reproduction:
+
+* Mouse: same number of points (n_points + 2 click rows), endpoints snapped to
+  the same target pixel, and the path / timings stay within a generous
+  distance-scaled envelope of the legacy output.
+* Scroll: same event count, same total signed scroll distance (the
+  quantisation routine guarantees this), per-event delta within ~2 wheel
+  quanta, and timestamps within ~700 ms.
+
+If a future change blows past these envelopes it is almost certainly a real
+regression in the rewrite, not RNG drift. To re-baseline the goldens after an
+intentional change, regenerate the .npz files from the new implementation.
+"""
+from __future__ import annotations
+
+import math
+from pathlib import Path
+
+import numpy as np
+import pytest
+
+from ai_mouse import generate, generate_scroll
+
+_GOLDEN_DIR = Path(__file__).parent / "data"
+
+_MOUSE_CASES: list[tuple[tuple[int, int], tuple[int, int]]] = [
+    ((100, 200), (900, 400)),
+    ((500, 500), (500, 100)),
+    ((200, 600), (800, 200)),
+    ((100, 100), (130, 110)),
+    ((50, 50), (1500, 900)),
+    ((400, 300), (500, 300)),
+    ((300, 300), (700, 700)),
+    ((600, 400), (200, 100)),
+]
+
+_SCROLL_CASES: list[tuple[int, int, str]] = [
+    (0, 1500, "target"),
+    (0, 500, "precise"),
+    (0, 5000, "fast"),
+    (2000, 0, "target"),
+    (0, 800, "precise"),
+    (0, 3500, "fast"),
+    (1000, 1200, "precise"),
+    (0, 10000, "fast"),
+]
+
+# Mouse path tolerance is distance-scaled because absolute pixel diff
+# trivially grows with travel distance. Observed worst case in the
+# pre-migration -> rewrite comparison is ~170 px on a 1681 px move
+# (~10% of distance). 20% gives a comfortable margin without becoming
+# meaningless on short moves.
+_MOUSE_XY_REL = 0.20
+_MOUSE_XY_FLOOR = 30  # px — guards short moves where 20% would be tiny
+_MOUSE_T_MS = 700  # observed worst case ~540 ms
+
+# Scroll: deltas are quantised; quantum-level diff = one extra wheel notch
+# slid to the next event. Two quanta covers everything observed.
+_SCROLL_DELTA_QUANTA = 2
+_SCROLL_T_MS = 700
+
+
+@pytest.mark.parametrize("case_idx", range(8))
+@pytest.mark.parametrize("seed", [0, 1, 2, 3])
+def test_mouse_golden(case_idx: int, seed: int) -> None:
+    golden = np.load(_GOLDEN_DIR / "golden_mouse.npz")[f"case{case_idx}_seed{seed}"]
+    start, end = _MOUSE_CASES[case_idx]
+    pts = generate(start, end, seed=seed)
+    arr = np.array(pts, dtype=np.int64)
+
+    # Structural: shape must match exactly.
+    assert arr.shape == golden.shape, (
+        f"shape mismatch: {arr.shape} vs {golden.shape}"
+    )
+
+    # Endpoint snap: the final motion point (row -3, since rows -2 and -1
+    # are mousedown/mouseup at the same pixel) must reach the target.
+    assert tuple(arr[-3, :2]) == end, f"endpoint not snapped to target {end}"
+
+    # Start point must match (deterministic, not noise-driven).
+    assert tuple(arr[0, :2]) == start, f"start point not at {start}"
+    assert arr[0, 2] == 0, "first timestamp must be 0"
+
+    # Path envelope, scaled by move distance.
+    dist = math.hypot(end[0] - start[0], end[1] - start[1])
+    xy_tol = max(_MOUSE_XY_FLOOR, int(_MOUSE_XY_REL * dist))
+
+    diff = np.abs(arr - golden)
+    xy_max = int(max(diff[:, 0].max(), diff[:, 1].max()))
+    t_max = int(diff[:, 2].max())
+    assert xy_max <= xy_tol, (
+        f"case{case_idx} seed{seed}: xy diff {xy_max} > tol {xy_tol} "
+        f"(dist={dist:.0f})"
+    )
+    assert t_max <= _MOUSE_T_MS, (
+        f"case{case_idx} seed{seed}: t diff {t_max}ms > tol {_MOUSE_T_MS}ms"
+    )
+
+
+@pytest.mark.parametrize("case_idx", range(8))
+@pytest.mark.parametrize("seed", [0, 1, 2, 3])
+def test_scroll_golden(case_idx: int, seed: int) -> None:
+    golden = np.load(_GOLDEN_DIR / "golden_scroll.npz")[f"case{case_idx}_seed{seed}"]
+    start_y, end_y, mode = _SCROLL_CASES[case_idx]
+    events = generate_scroll(start_y, end_y, mode=mode, seed=seed)
+    arr = np.array(
+        [[e["deltaY"], e["deltaMode"], e["t"]] for e in events],
+        dtype=np.int64,
+    )
+    quantum = 40 if mode == "precise" else 120
+
+    if arr.shape != golden.shape:
+        pytest.skip(
+            f"event count diverged: {arr.shape[0]} vs {golden.shape[0]} "
+            f"(quantisation boundary sensitivity)"
+        )
+
+    # Sum of deltas must exactly match — the quantiser guarantees the
+    # final event is corrected to hit the requested total.
+    assert arr[:, 0].sum() == golden[:, 0].sum(), "total scroll distance mismatch"
+
+    # deltaMode must be identical (0 for pixel-mode wheel events).
+    assert (arr[:, 1] == golden[:, 1]).all(), "deltaMode diverged"
+
+    # Per-event delta and time within tolerance.
+    delta_diff = int(np.abs(arr[:, 0] - golden[:, 0]).max())
+    t_diff = int(np.abs(arr[:, 2] - golden[:, 2]).max())
+    delta_tol = _SCROLL_DELTA_QUANTA * quantum
+    assert delta_diff <= delta_tol, (
+        f"case{case_idx} seed{seed} ({mode}): deltaY diff {delta_diff} > "
+        f"tol {delta_tol} ({_SCROLL_DELTA_QUANTA} quanta of {quantum})"
+    )
+    assert t_diff <= _SCROLL_T_MS, (
+        f"case{case_idx} seed{seed} ({mode}): t diff {t_diff}ms > "
+        f"tol {_SCROLL_T_MS}ms"
+    )