fix: signal search viability -- emission_rate range + mass floor

The actual root cause of 1-cell archives: CREATURE_MASS_FLOOR = 0.2 was
killing ~98% of randomly sampled signal creatures before they could enter
the archive. final_signal ≈ 0 in the diagnostic confirmed the mass wasn't
going into the signal field -- it was burning through the emission→decay
cycle. The hard floor was doing nothing useful; signal_retention in the
quality metric already penalizes mass bleed.

quality.py
  CREATURE_MASS_FLOOR: 0.2 → 0.05. Only truly collapsed creatures
  (essentially zero mass) are now rejected by the floor. The retention
  term at weight 0.3 in the quality metric provides the actual selection
  pressure against mass bleed, as intended.

search/params.py
  emission_rate range: [0.001, 0.05] → [0.0001, 0.01]. At 0.05 over
  500 steps with any decent growth activity a creature bleeds to death.
  0.01 is still high enough for meaningful signal dynamics.

search/descriptors.py
  emission_activity normalizer: 0.001 → 0.0002, recalibrated for the
  new emission_rate ceiling of 0.01.

search/result.py
  emission_rate docstring updated to reflect new range.

tests/test_signal.py
  Alive filter tests updated: floor is now 0.05 not 0.2. Test values
  adjusted to sit below (3%, 4%) vs above (15%) the new threshold.

tests/search/test_descriptors.py
  Scaling test values adjusted for new normalizer.

422 passed, 0 errors, 0 warnings

Author: rkv0id

src/biota/search/descriptors.py

@@ -698,9 +698,9 @@ def compute_emission_activity(trace: RolloutTrace) -> float:
         return 0.0
     mean_activity = float(trace.signal_emission_history.mean())
     # Normalizer calibrated to mean(G_pos * rate): G_pos averaged over grid
-    # is typically 0.0-0.1; rate in [0.001, 0.05]. So mean(G_pos * rate)
-    # spans ~0.00005-0.003 in practice. 0.001 gives good spread.
-    return float(np.clip(mean_activity / 0.001, 0.0, 1.0))
+    # is typically 0.0-0.1; rate in [0.0001, 0.01]. So mean(G_pos * rate)
+    # spans ~0.00001-0.0005 in practice. 0.0002 gives good spread.
+    return float(np.clip(mean_activity / 0.0002, 0.0, 1.0))
 
 
 def compute_receptor_sensitivity(trace: RolloutTrace) -> float:

src/biota/search/params.py

@@ -68,7 +68,7 @@ def width(self) -> float:
 SIGNAL_PARAMETER_SPECS: tuple[ParameterSpec, ...] = (
     ParameterSpec("emission_vector", "c", 0.0, 1.0, 0.1),
     ParameterSpec("receptor_profile", "c_sym", -1.0, 1.0, 0.2),
-    ParameterSpec("emission_rate", "scalar", 0.001, 0.05, 0.005),
+    ParameterSpec("emission_rate", "scalar", 0.0001, 0.01, 0.001),
     ParameterSpec("decay_rates", "c", 0.0, 0.9, 0.05),
     ParameterSpec("alpha_coupling", "scalar", -1.0, 1.0, 0.15),
     ParameterSpec("beta_modulation", "scalar", -1.0, 1.0, 0.15),

src/biota/search/quality.py

@@ -57,8 +57,10 @@
 LOCALIZED_THRESHOLD = 0.6
 PERSISTENT_DESCRIPTOR_DRIFT = 0.2
 # Signal creatures: mass field must not drop below this fraction of initial_mass
-# even when total (mass + signal) is conserved.
-CREATURE_MASS_FLOOR = 0.2
+# even when total (mass + signal) is conserved. Set low (0.05) so that
+# signal_retention in the quality metric does the selection work rather
+# than this hard floor killing viable emitters.
+CREATURE_MASS_FLOOR = 0.05
 
 # Quality component weights
 _W_COMPACT_BASE = 0.6

src/biota/search/result.py

@@ -27,8 +27,9 @@ class _SignalParams(TypedDict, total=False):
     receptor_profile: (C,) floats in [-1, 1]. Dot product with the convolved
                       signal field produces a scalar growth boost. Negative
                       values produce an inhibitory (aversive) response.
-    emission_rate:    Scalar in [0.001, 0.05]. Fraction of positive growth
-                      activity converted to signal per step.
+    emission_rate:    Scalar in [0.0001, 0.01]. Fraction of positive growth
+                      activity converted to signal per step. Kept low to
+                      prevent catastrophic mass bleed over 500+ steps.
     decay_rates:      (C,) floats in [0, 0.9]. Per-channel decay rate.
     alpha_coupling:   Scalar in [-1, 1]. Reception-to-growth coupling strength.
                       Positive = chemotaxis (grow into favorable signal gradients,

tests/search/test_descriptors.py

@@ -580,8 +580,8 @@ def test_emission_activity_zero_for_empty_history() -> None:
 
 
 def test_emission_activity_scales_with_emission() -> None:
-    low = _make_signal_trace(emission_history=np.full(TRACE_LEN, 0.0002, dtype=np.float32))
-    high = _make_signal_trace(emission_history=np.full(TRACE_LEN, 0.0008, dtype=np.float32))
+    low = _make_signal_trace(emission_history=np.full(TRACE_LEN, 0.00005, dtype=np.float32))
+    high = _make_signal_trace(emission_history=np.full(TRACE_LEN, 0.00015, dtype=np.float32))
     assert compute_emission_activity(low) < compute_emission_activity(high)
 
 

tests/test_signal.py

@@ -108,7 +108,7 @@ def test_signal_vector_lengths() -> None:
 
 def test_emission_rate_in_range() -> None:
     params = _make_signal_params()
-    assert 0.001 <= params["emission_rate"] <= 0.05  # type: ignore[typeddict-item]
+    assert 0.0001 <= params["emission_rate"] <= 0.01  # type: ignore[typeddict-item]
 
 
 def test_decay_rates_in_range() -> None:
@@ -150,7 +150,7 @@ def test_mutate_preserves_signal_keys() -> None:
     assert len(child["emission_vector"]) == SIGNAL_CHANNELS  # type: ignore[typeddict-item]
     assert len(child["receptor_profile"]) == SIGNAL_CHANNELS  # type: ignore[typeddict-item]
     assert len(child["decay_rates"]) == SIGNAL_CHANNELS  # type: ignore[typeddict-item]
-    assert 0.001 <= child["emission_rate"] <= 0.05  # type: ignore[typeddict-item]
+    assert 0.0001 <= child["emission_rate"] <= 0.01  # type: ignore[typeddict-item]
     assert -1.0 <= child["alpha_coupling"] <= 1.0  # type: ignore[typeddict-item]
     assert -1.0 <= child["beta_modulation"] <= 1.0  # type: ignore[typeddict-item]
 
@@ -213,13 +213,13 @@ def test_alive_filter_fails_when_creature_mass_collapses() -> None:
     """Creature converted almost all mass to signal -- below creature floor."""
     trace = _minimal_trace()
     initial_mass = 100.0
-    # Creature mass dropped to 5% of initial -- below CREATURE_MASS_FLOOR (20%)
+    # Creature mass dropped to 3% of initial -- below CREATURE_MASS_FLOOR (5%)
     eval_input = RolloutEvaluation(
         initial_mass=initial_mass,
-        final_mass=5.0,
+        final_mass=3.0,
         trace=trace,
         initial_total=initial_mass,
-        final_signal_mass=95.0,
+        final_signal_mass=97.0,
     )
     result = evaluate(eval_input)
     assert result.rejection_reason == "dead"
@@ -240,15 +240,15 @@ def test_alive_filter_fails_when_total_mass_lost() -> None:
 
 
 def test_alive_filter_signal_creature_mass_floor_is_stricter() -> None:
-    """Signal creature mass floor is 0.2 (vs no effective floor for non-signal)."""
+    """Signal creature mass floor is 0.05 -- only truly collapsed creatures fail."""
     trace = _minimal_trace()
-    # 15% of initial_mass remaining -- above old 10% floor, below new 20% floor
+    # 4% of initial_mass remaining -- below CREATURE_MASS_FLOOR (5%)
     eval_input = RolloutEvaluation(
         initial_mass=100.0,
-        final_mass=15.0,
+        final_mass=4.0,
         trace=trace,
         initial_total=100.0,
-        final_signal_mass=85.0,  # total conserved
+        final_signal_mass=96.0,  # total conserved
     )
     result = evaluate(eval_input)
     assert result.rejection_reason == "dead"