Also clip the positive tail of raw predicted log_mass in rollout

The previous commit clipped log_mass's negative tail (undershooting
_EPS made mass go slightly negative). The mirror case also crashes
rollout: a sufficiently large raw predicted log_mass overflows
exp() in float32, giving mass = inf, which then fails the same
downstream log_transform finiteness check when that mass is fed back
in as conditioning for a further step. Bound the upper tail too, at a
value comfortably below float32's overflow point.

Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>

tests/test_phase2.py

@@ -566,3 +566,27 @@ def test_decode_secondaries_extreme_negative_log_mass_stays_nonnegative():
     # stay well above -eps so log_transform(mass) stays finite.
     assert sec_mass[0, 0] > -1e-8
     log_transform(sec_mass[0, 0])
+
+
+def test_decode_secondaries_extreme_positive_log_mass_stays_finite():
+    from giant.data.transforms import decode_secondaries, log_transform
+
+    N = 1
+    e_sec = np.array([5.0], dtype=np.float32)
+    n_sec = np.array([1])
+    pre_dir = np.array([[0.0, 0.0, 1.0]], dtype=np.float32)
+
+    sec_cont = np.zeros((N, K_MAX, 6), dtype=np.float32)
+    sec_cont[0, 0, 0] = 10.0  # stick logit -> ~all of e_sec
+    sec_cont[0, 0, 1:4] = [0, 0, 1]
+    sec_cont[0, 0, 4] = 200.0  # raw model prediction: extremely positive log_mass
+    sec_cont[0, 0, 5] = 1.0
+
+    _, _, sec_mass, _, _ = decode_secondaries(sec_cont, n_sec, e_sec, pre_dir)
+
+    # Mirror image of the extreme-negative case above: exp(log_mass)
+    # overflows float32 to inf for an unclipped raw prediction this large,
+    # which then crashes the next log_transform call the same way a
+    # negative mass would.
+    assert np.isfinite(sec_mass[0, 0])
+    log_transform(sec_mass[0, 0])