diff --git a/libs/Ditto/core/atomic_components/audio2motion.py b/libs/Ditto/core/atomic_components/audio2motion.py
index af3bdee..e3355c0 100644
--- a/libs/Ditto/core/atomic_components/audio2motion.py
+++ b/libs/Ditto/core/atomic_components/audio2motion.py
@@ -166,6 +166,18 @@ class Audio2Motion:
 
         return res_kp_seq
     
+    def _generate_gaussian_pitch(self, target_deg, sigma=1.5):
+        # Ditto Pitch: 66 bins, 3 deg each, center at 32.5 (0 deg)
+        # target_deg = (idx * 3) - 97.5 => idx = (target_deg + 97.5) / 3
+        mu = (target_deg + 97.5) / 3.0
+        x = np.arange(66)
+        # We generate raw coefficients that will simulate the target degree
+        gaussian = np.exp(-((x - mu)**2) / (2 * sigma**2))
+        # Add tiny epsilon to avoid log(0) issues in model
+        gaussian = np.clip(gaussian, 1e-6, 1.0)
+        # Log-space coefficients for the categorical model
+        return np.log(gaussian).reshape(1, 66)
+
     def _update_kp_cond(self, res_kp_seq, idx, step_len=0):
         # [v1.9.144] Unconditional State Tracking
         # We process these outside the fix_kp_cond branches to ensure monitoring never stops.
@@ -205,39 +217,45 @@ class Audio2Motion:
         if self.is_recovering:
              self.look_up_timer += step_len
         
-        # [v1.9.162] PREDICTIVE POSTURE STATE MACHINE
-        # We scan the VAD timeline to decide the character's "Readiness"
+        # [v1.9.165] FLUID POSTURE STATE MACHINE
+        # Decisions are based on Silence Frames (Universal) + VAD Lookahead (Optional)
         target_bias_deg = 0.0 # Standard flat
         
+        is_currently_talking = self.silence_frames < 25
+        has_upcoming_speech = False
+        
         if self.vad_timeline is not None:
-             # Lookahead window: 2 seconds (50 frames)
              lookahead = self.vad_timeline[idx : idx + 50]
-             
-             is_currently_talking = self.silence_frames < 25
              has_upcoming_speech = len(lookahead) > 0 and np.max(lookahead) > 0.1
-             
-             if is_currently_talking or has_upcoming_speech:
-                  # COMBAT STANCE: Tucked chin (-5 deg downward = -0.0020 impulse push)
-                  target_bias_deg = +5.0 
-                  self.target_push = -0.0025 # Persistent negative impulse to lower head
-             else:
-                  # IDLE SWAY: Natural breathing motion
-                  cycle = np.sin(self.global_time * 0.05)
-                  target_bias_deg = -3.0 + cycle * 5.0 
-                  self.target_push = 0.0 # No forced push, just natural sway
+        
+        if is_currently_talking or has_upcoming_speech:
+             # COMBAT STANCE: Tucked chin for headroom
+             target_bias_deg = -5.0 
         else:
-             self.target_push = 0.0
-             target_bias_deg = 0.0
-
-        # Smooth Push Transition
-        self.current_push = self.current_push * 0.90 + self.target_push * 0.10
+             # IDLE SWAY: Natural breathing
+             cycle = np.sin(self.global_time * 0.05)
+             target_bias_deg = +2.0 + cycle * 5.0 # Slightly down/neutral sway
+        
+        # [v1.9.165] Synthetic Anchor Generation
+        # Instead of photo bias, we anchor to a mathematical IDEAL distribution.
         self.target_bias_deg = target_bias_deg
+        synthetic_pitch = self._generate_gaussian_pitch(target_bias_deg)
+        
+        # Apply to anchor target
+        self.target_neutralizer = self.photo_base_neutralizer.copy()
+        # Offset 1:67 is Pitch. We overwrite with synthetic distribution.
+        # Since photo_base_neutralizer[1:67] is -s_kp[1:67], 
+        # photo_base + s_kp + synthetic = synthetic.
+        self.target_neutralizer[0, 1:67] = -self.s_kp_cond[0, 1:67] + synthetic_pitch[0]
+        
+        # Smooth Transition (0.05 EMA = ~1s glide)
+        self.current_neutralizer = self.current_neutralizer * 0.95 + self.target_neutralizer * 0.05
         
         tag = "[HEARTBEAT]" if not self.is_recovering else "[RECOVERY]"
-        if self.vad_timeline is not None and has_upcoming_speech and not is_currently_talking:
+        if has_upcoming_speech and not is_currently_talking:
              tag = "[ANTICIPATION]"
              
-        print(f"{tag} Frame {idx:04d} | Delta={delta_p:+.2f}° | Bias={self.target_bias_deg:+.1f}° | Silence={self.silence_frames:03d}")
+        print(f"{tag} Frame {idx:04d} | Delta={delta_p:+.2f}° | Goal={self.target_bias_deg:+.1f}° | Silence={self.silence_frames:03d}")
 
         # 5. Postural Auto-Correction Logic [REPLACED by v1.9.160 STATIC + v1.9.162 PREDICTIVE]
 
@@ -381,25 +399,26 @@ class Audio2Motion:
 
         pred_kp_seq = self.lmdm(self.kp_cond, aud_cond, self.sampling_timesteps)
         
-        # [v1.9.160/162/164] STATIC/PREDICTIVE POSTURE MODE
-        # We now enforce high Pitch pressure ALWAYS to anchor to our target baseline.
+        # [v1.9.165] FLUID POSTURE MODE
+        # We now use "Mathematical Gravity" to pull the head into the safe zone.
         if True:
-            # Constant 95% tension for Pitch (Vertical)
-            pressure = 0.95
-            soft_p = 0.40 # Maintain soft-lock for Yaw/Roll to allow mouth projection
+            # [v1.9.165] Fluid Pressure: 75% Pitch / 30% Yaw-Roll 
+            # This restores expressiveness while maintaining vertical stability.
+            pressure = 0.75 
+            soft_p = 0.30 
             
-            # [v1.9.164] Stable Anchor = Photo + Drift-Adjusted Offset
-            anchor_p = (self.s_kp_cond + self.brownian_pos)[0, 1:202]
+            # Stable Anchor = Photo + Synthetic Offset + Brownian Drift
+            anchor_p = (self.s_kp_cond + self.current_neutralizer + self.brownian_pos)[0, 1:202]
             
             for f in range(pred_kp_seq.shape[1]):
                 # Absolute Pitch Locking (Halt baring distortion)
                 pred_kp_seq[0, f, 1:67] = pred_kp_seq[0, f, 1:67] * (1.0 - pressure) + anchor_p[0:66] * pressure
-                # Soft Rotational Locking (Organic freedom)
+                # Soft Rotational Locking (Organic movement)
                 pred_kp_seq[0, f, 67:202] = pred_kp_seq[0, f, 67:202] * (1.0 - soft_p) + anchor_p[66:201] * soft_p
             
             if self.clip_idx % 20 == 0:
-                 mode_s = "SPEECH" if self.target_bias_deg > 2.0 else "IDLE"
-                 print(f"[v1.9.162 {mode_s}] Anchor: {self.target_bias_deg:+.1f}° goal.")
+                 mode_s = "SPEECH" if self.target_bias_deg < -2.0 else "IDLE"
+                 print(f"[v1.9.165 {mode_s}] Fluid Gravity: {self.target_bias_deg:+.1f}° goal.")
         
         # [v1.9.156] Virtual Last Frame for Startup Stabilization
         # If this is the VERY first chunk, we treat the source photo as the "prev frame"
diff --git a/pyproject.toml b/pyproject.toml
index d01b5c6..65f618f 100755
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -1,7 +1,7 @@
 [project]
 name = "aiia"
 description = "The Ultimate AI Audio/Video toolkit for ComfyUI. Features an enhanced Ditto (with optimizations that outperform official demos and other SOTA talking head models in lip-sync accuracy and natural motion), EchoMimic V3 & FLOAT, VibeVoice & CosyVoice 3.0 (Zero-Shot Voice Cloning), Multi-Role Podcast Generation, and a powerful Media Browser."
-version = "1.9.164"
+version = "1.9.165"
 license = {file = "LICENSE"}
 readme = "README.md"
 authors = [