diff --git a/comfy/ldm/sam3/detector.py b/comfy/ldm/sam3/detector.py
index 6ae919a795ab..12d3a01abf92 100644
--- a/comfy/ldm/sam3/detector.py
+++ b/comfy/ldm/sam3/detector.py
@@ -54,7 +54,7 @@ def forward(self, q_input, k_input=None, v_input=None, mask=None):
         if mask is not None and mask.ndim == 2:
             mask = mask[:, None, None, :]  # [B, T] -> [B, 1, 1, T] for SDPA broadcast
         dtype = q.dtype  # manual_cast may produce mixed dtypes
-        out = optimized_attention(q, k.to(dtype), v.to(dtype), self.num_heads, mask=mask)
+        out = optimized_attention(q, k.to(dtype), v.to(dtype), self.num_heads, mask=mask, low_precision_attention=False)
         return self.out_proj(out)
 
 
diff --git a/comfy/ldm/sam3/sam.py b/comfy/ldm/sam3/sam.py
index 272781d45774..75cb457cff53 100644
--- a/comfy/ldm/sam3/sam.py
+++ b/comfy/ldm/sam3/sam.py
@@ -40,7 +40,7 @@ def forward(self, q, k, v):
         q = self.q_proj(q)
         k = self.k_proj(k)
         v = self.v_proj(v)
-        return self.out_proj(optimized_attention(q, k, v, self.num_heads))
+        return self.out_proj(optimized_attention(q, k, v, self.num_heads, low_precision_attention=False))
 
 
 class TwoWayAttentionBlock(nn.Module):
@@ -179,7 +179,7 @@ def forward(self, x, freqs_cis=None):
         q, k, v = qkv.permute(2, 0, 3, 1, 4).unbind(dim=0)
         if self.use_rope and freqs_cis is not None:
             q, k = apply_rope(q, k, freqs_cis)
-        return self.proj(optimized_attention(q, k, v, self.num_heads, skip_reshape=True))
+        return self.proj(optimized_attention(q, k, v, self.num_heads, skip_reshape=True, low_precision_attention=False))
 
 
 class Block(nn.Module):
diff --git a/comfy/ldm/sam3/tracker.py b/comfy/ldm/sam3/tracker.py
index 6ff6369d1339..8f7481003cf0 100644
--- a/comfy/ldm/sam3/tracker.py
+++ b/comfy/ldm/sam3/tracker.py
@@ -364,7 +364,7 @@ def forward(self, q, k=None, v=None, rope=None, num_k_exclude_rope=0):
         v = self.v_proj(v)
         if rope is not None:
             q, k = apply_rope_memory(q, k, rope, self.num_heads, num_k_exclude_rope)
-        out = optimized_attention(q, k, v, self.num_heads)
+        out = optimized_attention(q, k, v, self.num_heads, low_precision_attention=False)
         return self.out_proj(out)
 
 
@@ -657,7 +657,7 @@ def forward(self, image, x, memory_image, memory, memory_image_pos=None,
         v = self.self_attn_v_proj(normed)
         if rope is not None:
             q, k = apply_rope_memory(q, k, rope, self.num_heads, 0)
-        x = x + self.self_attn_out_proj(optimized_attention(q, k, v, self.num_heads))
+        x = x + self.self_attn_out_proj(optimized_attention(q, k, v, self.num_heads, low_precision_attention=False))
 
         # Decoupled cross-attention: fuse image and memory projections
         normed = self.norm2(x)
@@ -668,7 +668,7 @@ def forward(self, image, x, memory_image, memory, memory_image_pos=None,
         v = self.cross_attn_v_proj(memory)
         if rope is not None:
             q, k = apply_rope_memory(q, k, rope, self.num_heads, num_k_exclude_rope)
-        x = x + self.cross_attn_out_proj(optimized_attention(q, k, v, self.num_heads))
+        x = x + self.cross_attn_out_proj(optimized_attention(q, k, v, self.num_heads, low_precision_attention=False))
 
         # FFN
         x = x + self.linear2(F.gelu(self.linear1(self.norm3(x))))
diff --git a/comfy_extras/nodes_lt.py b/comfy_extras/nodes_lt.py
index d7c2e874470f..19d8a387f07b 100644
--- a/comfy_extras/nodes_lt.py
+++ b/comfy_extras/nodes_lt.py
@@ -1,6 +1,7 @@
 import nodes
 import node_helpers
 import torch
+import torchaudio
 import comfy.model_management
 import comfy.model_sampling
 import comfy.samplers
@@ -711,7 +712,14 @@ def define_schema(cls) -> io.Schema:
     @classmethod
     def execute(cls, model, positive, negative, reference_audio, audio_vae, identity_guidance_scale, start_percent, end_percent) -> io.NodeOutput:
         # Encode reference audio to latents and patchify
-        audio_latents = audio_vae.encode(reference_audio)
+        sample_rate = reference_audio["sample_rate"]
+        vae_sample_rate = getattr(audio_vae, "audio_sample_rate", 44100)
+        if vae_sample_rate != sample_rate:
+            waveform = torchaudio.functional.resample(reference_audio["waveform"], sample_rate, vae_sample_rate)
+        else:
+            waveform = reference_audio["waveform"]
+
+        audio_latents = audio_vae.encode(waveform.movedim(1, -1))
         b, c, t, f = audio_latents.shape
         ref_tokens = audio_latents.permute(0, 2, 1, 3).reshape(b, t, c * f)
         ref_audio = {"tokens": ref_tokens}
diff --git a/execution.py b/execution.py
index 5e02dffb204f..e15eb4bda008 100644
--- a/execution.py
+++ b/execution.py
@@ -811,11 +811,30 @@ async def execute_async(self, prompt, prompt_id, extra_data={}, execute_outputs=
             self._notify_prompt_lifecycle("end", prompt_id)
 
 
-async def validate_inputs(prompt_id, prompt, item, validated):
+async def validate_inputs(prompt_id, prompt, item, validated, visiting=None):
+    if visiting is None:
+        visiting = []
+
     unique_id = item
     if unique_id in validated:
         return validated[unique_id]
 
+    if unique_id in visiting:
+        cycle_path_nodes = visiting[visiting.index(unique_id):] + [unique_id]
+        cycle_nodes = list(dict.fromkeys(cycle_path_nodes))
+        cycle_path = " -> ".join(f"{node_id} ({prompt[node_id]['class_type']})" for node_id in cycle_path_nodes)
+        for node_id in cycle_nodes:
+            validated[node_id] = (False, [{
+                "type": "dependency_cycle",
+                "message": "Dependency cycle detected",
+                "details": cycle_path,
+                "extra_info": {
+                    "node_id": node_id,
+                    "cycle_nodes": cycle_nodes,
+                }
+            }], node_id)
+        return validated[unique_id]
+
     inputs = prompt[unique_id]['inputs']
     class_type = prompt[unique_id]['class_type']
     obj_class = nodes.NODE_CLASS_MAPPINGS[class_type]
@@ -899,7 +918,11 @@ async def validate_inputs(prompt_id, prompt, item, validated):
                 errors.append(error)
                 continue
             try:
-                r = await validate_inputs(prompt_id, prompt, o_id, validated)
+                visiting.append(unique_id)
+                try:
+                    r = await validate_inputs(prompt_id, prompt, o_id, validated, visiting)
+                finally:
+                    visiting.pop()
                 if r[0] is False:
                     # `r` will be set in `validated[o_id]` already
                     valid = False
@@ -1048,10 +1071,13 @@ async def validate_inputs(prompt_id, prompt, item, validated):
                     errors.append(error)
                     continue
 
-    if len(errors) > 0 or valid is not True:
-        ret = (False, errors, unique_id)
-    else:
-        ret = (True, [], unique_id)
+    ret = validated.get(unique_id, (True, [], unique_id))
+    # Recursive cycle detection may have already populated an error on us. Join it.
+    ret = (
+        ret[0] and valid is True and not errors,
+        ret[1] + [error for error in errors if error not in ret[1]],
+        unique_id,
+    )
 
     validated[unique_id] = ret
     return ret
diff --git a/requirements.txt b/requirements.txt
index 419124f482ab..7a2e4e0a2d7d 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -23,7 +23,7 @@ SQLAlchemy>=2.0
 filelock
 av>=14.2.0
 comfy-kitchen>=0.2.8
-comfy-aimdo>=0.2.12
+comfy-aimdo==0.2.14
 requests
 simpleeval>=1.0.0
 blake3