diff --git a/nodes/selva_activation_steering_extractor.py b/nodes/selva_activation_steering_extractor.py
index a5307be..cefb482 100644
--- a/nodes/selva_activation_steering_extractor.py
+++ b/nodes/selva_activation_steering_extractor.py
@@ -27,7 +27,7 @@ from .selva_lora_trainer import _prepare_dataset
 def _collect_activations(generator, conditions, latent, t_tensor):
     """Run one predict_flow call, collecting latent hidden states per block.
 
-    Returns a list of [hidden_dim] float32 CPU tensors,
+    Returns a list of [seq, hidden_dim] float32 CPU tensors,
     one per block (joint_blocks first, then fused_blocks).
     """
     activations = []
@@ -35,9 +35,7 @@ def _collect_activations(generator, conditions, latent, t_tensor):
     def make_hook(is_joint):
         def hook(module, input, output):
             h = output[0] if is_joint else output
-            # Mean over batch then seq → [hidden]: makes vectors length-agnostic so
-            # they broadcast to any inference duration without shape mismatches.
-            activations.append(h.detach().float().mean(0).mean(0).cpu())  # [hidden]
+            activations.append(h.detach().float().mean(0).cpu())  # [seq, hidden]
         return hook
 
     handles = []
@@ -186,7 +184,7 @@ class SelvaActivationSteeringExtractor:
 
         n_joint = len(generator.joint_blocks)
         payload = {
-            "steering_vectors": steering_vectors,   # list of [hidden] tensors
+            "steering_vectors": steering_vectors,   # list of [seq, hidden] tensors
             "n_blocks":         n_blocks,
             "n_joint":          n_joint,
             "n_fused":          len(generator.fused_blocks),
diff --git a/nodes/selva_sampler.py b/nodes/selva_sampler.py
index 1e3f056..95233e7 100644
--- a/nodes/selva_sampler.py
+++ b/nodes/selva_sampler.py
@@ -159,21 +159,50 @@ class SelvaSampler:
                     device=gen_device, dtype=dtype, generator=rng,
                 ).to(device)
 
-                # Activation steering hooks
+                # Activation steering: apply only during the conditional predict_flow pass
+                # so steering gets amplified by cfg_strength rather than canceling out.
                 steering_handles = []
+                _orig_predict_flow = None
                 if steering_vectors is not None and steering_strength > 0.0:
                     vecs = steering_vectors["steering_vectors"]
                     n_joint = steering_vectors["n_joint"]
 
+                    # Patch predict_flow to flag which pass is conditional.
+                    # ode_wrapper calls predict_flow(conditions) and predict_flow(empty_conditions);
+                    # identity check tells us which is which.
+                    _is_cond_pass = [False]
+                    _orig_predict_flow = net_generator.predict_flow
+
+                    def _tracked_predict_flow(latent, t, cond):
+                        _is_cond_pass[0] = (cond is conditions)
+                        return _orig_predict_flow(latent, t, cond)
+
+                    net_generator.predict_flow = _tracked_predict_flow
+
                     def _make_steering_hook(vec_cpu, is_joint, strength, dev, dt):
-                        vec = vec_cpu.to(dev, dt)  # [hidden] — broadcasts over [B, T, H]
+                        vec = vec_cpu.to(dev, dt)  # [seq, hidden]
                         def hook(module, input, output):
+                            if not _is_cond_pass[0]:
+                                return  # skip unconditional pass; steering amplified by cfg_strength
+                            # Interpolate steering vec to match actual output seq length
+                            # (handles generation at different duration than extraction)
                             if is_joint:
-                                # JointBlock returns (latent, clip, text) tuple
-                                latent_out = output[0] + strength * vec
+                                out_seq = output[0].shape[1]
+                            else:
+                                out_seq = output.shape[1]
+                            v = vec
+                            if v.shape[0] != out_seq:
+                                v = torch.nn.functional.interpolate(
+                                    v.T.unsqueeze(0),   # [1, hidden, seq_orig]
+                                    size=out_seq,
+                                    mode="linear",
+                                    align_corners=False,
+                                ).squeeze(0).T          # [seq_new, hidden]
+                            if is_joint:
+                                latent_out = output[0] + strength * v
                                 return (latent_out,) + output[1:]
                             else:
-                                return output + strength * vec
+                                return output + strength * v
                         return hook
 
                     blocks = list(net_generator.joint_blocks) + list(net_generator.fused_blocks)
@@ -184,7 +213,8 @@ class SelvaSampler:
                                 _make_steering_hook(vecs[i], is_joint, steering_strength, device, dtype)
                             )
                             steering_handles.append(h)
-                    print(f"[SelVA] Activation steering: {len(steering_handles)} blocks  strength={steering_strength}", flush=True)
+                    print(f"[SelVA] Activation steering: {len(steering_handles)} blocks  "
+                          f"strength={steering_strength}  (conditional pass only)", flush=True)
 
                 # Flow matching ODE (Euler)
                 fm = FlowMatching(min_sigma=0, inference_mode="euler", num_steps=steps)
@@ -203,6 +233,8 @@ class SelvaSampler:
                         "to 'offload_to_cpu', using a smaller variant, or reducing duration."
                     )
                 finally:
+                    if _orig_predict_flow is not None:
+                        net_generator.predict_flow = _orig_predict_flow
                     for h in steering_handles:
                         h.remove()