refactor(generator): remove deterministic speed_profile and hard log_dt clip

These post-processing hacks were added to compensate for small-data
training. With Balabit pretraining they suppress the multimodal
timing distribution and cause the template-y Δt curves seen in the
verify UI.

ai_mouse/generator.py

@@ -12,8 +12,8 @@ Pipeline:
    c. Enforce forward monotonicity (prevent x-axis jitter).
 6. Temporal post-processing:
    a. Clip log_dt to [0, 5] to prevent exponential explosion.
-   b. Remove outliers beyond 2σ from median.
-   c. Apply bell-curve speed profile (slow→fast→slow).
+   (speed profile and median±1.1 hard clip removed in 2026-05 refactor —
+    let the model's learned timing distribution come through naturally.)
 7. Decode to pixels via decode_trajectory.
 8. Resample to n_points if n_points != model seq_len.
 9. Convert log_dt → ms timestamps, scale to total_duration, clip [2, 150].
@@ -257,34 +257,6 @@ def generate(
     # First point has no interval (padding from training)
     log_dt[0] = 0.0
 
-    # The model tends to produce exaggerated deceleration at the tail
-    # (last 10 points log_dt ~3-5 vs middle ~1.5).
-    # Cap the max-to-median ratio to ~3× (i.e., tail Δt ≤ 3× median Δt)
-    median_ldt = float(np.median(log_dt[1:]))
-    # Allow max log_dt = median + 1.1 (exp(1.1) ≈ 3× ratio)
-    max_allowed = median_ldt + 1.1
-    min_allowed = max(median_ldt - 1.1, 0.0)
-    for i in range(1, len(log_dt)):
-        if log_dt[i] > max_allowed:
-            log_dt[i] = max_allowed
-        elif log_dt[i] < min_allowed:
-            log_dt[i] = min_allowed
-
-    # Apply asymmetric speed profile: start slow, fast in middle, gentle end
-    # Mimics natural mouse movement (accelerate → cruise → decelerate)
-    t_frac = np.linspace(0, 1, len(log_dt))
-    speed_profile = np.zeros_like(log_dt, dtype=float)
-    for i in range(1, len(log_dt)):
-        t = t_frac[i]
-        if t < 0.15:
-            # Acceleration phase: start slow (+0.3 at t=0, → 0 at t=0.15)
-            speed_profile[i] = 0.3 * (1.0 - t / 0.15)
-        elif t > 0.85:
-            # Deceleration phase: end slightly slow (+0.2 at t=1)
-            speed_profile[i] = 0.2 * ((t - 0.85) / 0.15)
-        # Middle: speed_profile = 0 (fastest, no penalty)
-    log_dt[1:] = log_dt[1:] + speed_profile[1:]
-
     # Decode spatial coordinates to pixels
     normalised = np.stack([forward, lateral], axis=1)  # (seq_len, 2)
     pixels = decode_trajectory(normalised, start, end)  # (seq_len, 2)

tests/test_generator.py

@@ -104,3 +104,23 @@ class TestGenerate:
         )
         # 32 move points + 2 click events = 34
         assert len(result) == 34
+
+
+class TestPostProcessing:
+    def test_dt_diversity_preserved(self, model_dir):
+        """After removing speed_profile + median clip, multiple generations
+        should differ in their Δt sequences (not all identical)."""
+        results = [generate(start=(100, 200), end=(500, 400), model_dir=str(model_dir))
+                   for _ in range(5)]
+        # Extract Δt sequences (only move events, not click events)
+        dts = []
+        for r in results:
+            moves = r[:-2]
+            dt_seq = [moves[i+1][2] - moves[i][2] for i in range(len(moves)-1)]
+            dts.append(dt_seq)
+        # At least 2 of the 5 sequences should differ at any given index
+        for i in range(min(len(d) for d in dts)):
+            values = {tuple([d[i]]) for d in dts}
+            if len(values) > 1:
+                return  # at least one position has variation — pass
+        pytest.fail("All 5 Δt sequences are identical at every position — diversity collapsed")