diff --git a/README.md b/README.md
index 8d8ed385..4003eb7b 100644
--- a/README.md
+++ b/README.md
@@ -65,6 +65,8 @@ We've also added optimized Post-Training kernels that deliver **up to 80% memory
 
 You can view the documentation site for additional installation, usage examples, and API references:https://linkedin.github.io/Liger-Kernel/
 
+You can view the Liger Kernel Technical Report: https://openreview.net/forum?id=36SjAIT42G
+
 ## Supercharge Your Model with Liger Kernel
 
 ![Banner](https://raw.githubusercontent.com/linkedin/Liger-Kernel/main/docs/images/banner.GIF)
@@ -394,3 +396,4 @@ url={https://openreview.net/forum?id=36SjAIT42G}
         ↑ Back to Top ↑
     </a>
 </p>
+
diff --git a/src/liger_kernel/transformers/monkey_patch.py b/src/liger_kernel/transformers/monkey_patch.py
index 8aa4ce31..2622cea3 100755
--- a/src/liger_kernel/transformers/monkey_patch.py
+++ b/src/liger_kernel/transformers/monkey_patch.py
@@ -35,8 +35,7 @@ from liger_kernel.transformers.model.smollm3 import lce_forward as smollm3_lce_f
 from liger_kernel.transformers.qwen2vl_mrope import liger_multimodal_rotary_pos_emb
 from liger_kernel.transformers.rms_norm import LigerRMSNorm
 from liger_kernel.transformers.rope import liger_rotary_pos_emb
-from liger_kernel.transformers.rope import liger_rotary_pos_emb_with_cast
-from liger_kernel.transformers.rope import liger_rotary_pos_emb_with_cast_and_leading_batch
+from liger_kernel.transformers.rope import liger_rotary_pos_emb_vision
 from liger_kernel.transformers.swiglu import LigerBlockSparseTop2MLP
 from liger_kernel.transformers.swiglu import LigerPhi3SwiGLUMLP
 from liger_kernel.transformers.swiglu import LigerSwiGLUMLP
@@ -1754,8 +1753,8 @@ def apply_liger_kernel_to_qwen3_vl(
     from liger_kernel.transformers.model.qwen3_vl import lce_forward as qwen3_vl_lce_forward
 
     if rope:
-        modeling_qwen3_vl.apply_rotary_pos_emb = liger_rotary_pos_emb_with_cast
-        modeling_qwen3_vl.apply_rotary_pos_emb_vision = liger_rotary_pos_emb_with_cast_and_leading_batch
+        modeling_qwen3_vl.apply_rotary_pos_emb = liger_rotary_pos_emb
+        modeling_qwen3_vl.apply_rotary_pos_emb_vision = liger_rotary_pos_emb_vision
 
     if rms_norm:
         modeling_qwen3_vl.Qwen3VLTextRMSNorm = LigerRMSNorm
@@ -1829,8 +1828,8 @@ def apply_liger_kernel_to_qwen3_vl_moe(
     from liger_kernel.transformers.model.qwen3_vl_moe import lce_forward as qwen3_vl_moe_lce_forward
 
     if rope:
-        modeling_qwen3_vl_moe.apply_rotary_pos_emb = liger_rotary_pos_emb_with_cast
-        modeling_qwen3_vl_moe.apply_rotary_pos_emb_vision = liger_rotary_pos_emb_with_cast_and_leading_batch
+        modeling_qwen3_vl_moe.apply_rotary_pos_emb = liger_rotary_pos_emb
+        modeling_qwen3_vl_moe.apply_rotary_pos_emb_vision = liger_rotary_pos_emb_vision
 
     if rms_norm:
         modeling_qwen3_vl_moe.Qwen3VLMoeTextRMSNorm = LigerRMSNorm
diff --git a/src/liger_kernel/transformers/rope.py b/src/liger_kernel/transformers/rope.py
index ced53d5d..01de6630 100644
--- a/src/liger_kernel/transformers/rope.py
+++ b/src/liger_kernel/transformers/rope.py
@@ -1,4 +1,3 @@
-from typing import Optional
 from typing import Tuple
 
 import torch
@@ -25,39 +24,41 @@ def liger_rotary_pos_emb(q, k, cos, sin, position_ids=None, unsqueeze_dim=1):
     return LigerRopeFunction.apply(q, k, cos, sin, position_ids, unsqueeze_dim)
 
 
-def liger_rotary_pos_emb_with_cast(
+def liger_rotary_pos_emb_vision(
     q: torch.Tensor,
     k: torch.Tensor,
     cos: torch.Tensor,
     sin: torch.Tensor,
-    position_ids: Optional[torch.Tensor] = None,
-    unsqueeze_dim: int = 1,
 ) -> Tuple[torch.Tensor, torch.Tensor]:
+    """
+    Modified version of liger_rotary_pos_emb for qwen3_vl's apply_rotary_pos_emb_vision function.
+    Manually tranposed the input and output to match the expected shape for liger_rotary_pos_emb.
+    Reference: https://https://github.com/huggingface/transformers/blob/v5.0.0rc0/src/transformers/models/qwen3_vl/modeling_qwen3_vl.py#L116
+
+    Args:
+        q (torch.Tensor): The query tensor of shape (seq_length, num_heads, head_dim),
+        with stride (num_heads * head_dim, head_dim, 1).
+        k (torch.Tensor): The query tensor of shape (seq_length, num_heads, head_dim),
+        with stride (num_heads * head_dim, head_dim, 1). Same as q.
+        cos (torch.Tensor): The cosine tensor of shape (seq_length, head_dim).
+        sin (torch.Tensor): The sine tensor of shape (seq_length, head_dim).
+
+    Returns:
+        Tuple[torch.Tensor, torch.Tensor]: The query and key tensors with the same shape and stride as inputs.
+    """
     orig_q_dtype, orig_k_dtype = q.dtype, k.dtype
 
-    q32 = q.to(torch.float32)
-    k32 = k.to(torch.float32)
+    # tranpose to (1, num_heads, seq_length, head_dim) and cast to float32 to match liger_rotary_pos_emb input shape
+    # also unsqueeze for batch dim
+    q32 = q.to(torch.float32).unsqueeze(0).transpose(1, 2)
+    k32 = k.to(torch.float32).unsqueeze(0).transpose(1, 2)
     cos32 = cos.to(torch.float32)
     sin32 = sin.to(torch.float32)
 
-    q_out, k_out = liger_rotary_pos_emb(q32, k32, cos32, sin32, position_ids=position_ids, unsqueeze_dim=unsqueeze_dim)
-    return q_out.to(orig_q_dtype), k_out.to(orig_k_dtype)
+    q_out, k_out = liger_rotary_pos_emb(q32, k32, cos32, sin32)
 
-
-def liger_rotary_pos_emb_with_cast_and_leading_batch(
-    q: torch.Tensor,
-    k: torch.Tensor,
-    cos: torch.Tensor,
-    sin: torch.Tensor,
-    position_ids: Optional[torch.Tensor] = None,
-    unsqueeze_dim: int = 1,
-) -> Tuple[torch.Tensor, torch.Tensor]:
-    orig_q_dtype, orig_k_dtype = q.dtype, k.dtype
-
-    q32 = q.to(torch.float32).unsqueeze(0)
-    k32 = k.to(torch.float32).unsqueeze(0)
-    cos32 = cos.to(torch.float32).unsqueeze(0)
-    sin32 = sin.to(torch.float32).unsqueeze(0)
-
-    q_out, k_out = liger_rotary_pos_emb(q32, k32, cos32, sin32, position_ids=position_ids, unsqueeze_dim=unsqueeze_dim)
-    return q_out.to(orig_q_dtype).squeeze(0), k_out.to(orig_k_dtype).squeeze(0)
+    # transpose back to (seq_length, num_heads, head_dim) and cast back to original dtype
+    # also squeeze out batch dim
+    q_out = q_out.transpose(1, 2).squeeze(0).to(orig_q_dtype)
+    k_out = k_out.transpose(1, 2).squeeze(0).to(orig_k_dtype)
+    return q_out, k_out
diff --git a/test/transformers/test_monkey_patch.py b/test/transformers/test_monkey_patch.py
index 51fa0266..ce06f767 100755
--- a/test/transformers/test_monkey_patch.py
+++ b/test/transformers/test_monkey_patch.py
@@ -1019,8 +1019,8 @@ def test_apply_liger_kernel_to_instance_for_qwen3_vl_moe_text():
 def test_qwen3_vl_rope_hooks_applied():
     # Ensure any monkey patching is cleaned up for subsequent tests
     with patch("transformers.models.qwen3_vl.modeling_qwen3_vl") as modeling_mod:
-        from liger_kernel.transformers.monkey_patch import liger_rotary_pos_emb_with_cast
-        from liger_kernel.transformers.monkey_patch import liger_rotary_pos_emb_with_cast_and_leading_batch
+        from liger_kernel.transformers.monkey_patch import liger_rotary_pos_emb
+        from liger_kernel.transformers.monkey_patch import liger_rotary_pos_emb_vision
 
         # Before applying, make sure attributes exist but are not the liger implementations
         setattr(modeling_mod, "apply_rotary_pos_emb", object())
@@ -1028,16 +1028,16 @@ def test_qwen3_vl_rope_hooks_applied():
 
         _apply_liger_kernel("qwen3_vl")
 
-        assert modeling_mod.apply_rotary_pos_emb is liger_rotary_pos_emb_with_cast
-        assert modeling_mod.apply_rotary_pos_emb_vision is liger_rotary_pos_emb_with_cast_and_leading_batch
+        assert modeling_mod.apply_rotary_pos_emb is liger_rotary_pos_emb
+        assert modeling_mod.apply_rotary_pos_emb_vision is liger_rotary_pos_emb_vision
 
 
 @pytest.mark.skipif(not is_qwen3_vl_moe_available(), reason="qwen3_vl_moe module not available")
 def test_qwen3_vl_moe_rope_hooks_applied():
     # Ensure any monkey patching is cleaned up for subsequent tests
     with patch("transformers.models.qwen3_vl_moe.modeling_qwen3_vl_moe") as modeling_mod:
-        from liger_kernel.transformers.monkey_patch import liger_rotary_pos_emb_with_cast
-        from liger_kernel.transformers.monkey_patch import liger_rotary_pos_emb_with_cast_and_leading_batch
+        from liger_kernel.transformers.monkey_patch import liger_rotary_pos_emb
+        from liger_kernel.transformers.monkey_patch import liger_rotary_pos_emb_vision
 
         # Before applying, make sure attributes exist but are not the liger implementations
         setattr(modeling_mod, "apply_rotary_pos_emb", object())
@@ -1045,8 +1045,8 @@ def test_qwen3_vl_moe_rope_hooks_applied():
 
         _apply_liger_kernel("qwen3_vl_moe")
 
-        assert modeling_mod.apply_rotary_pos_emb is liger_rotary_pos_emb_with_cast
-        assert modeling_mod.apply_rotary_pos_emb_vision is liger_rotary_pos_emb_with_cast_and_leading_batch
+        assert modeling_mod.apply_rotary_pos_emb is liger_rotary_pos_emb
+        assert modeling_mod.apply_rotary_pos_emb_vision is liger_rotary_pos_emb_vision
 
 
 @pytest.mark.skipif(not is_falcon_h1_available(), reason="falcon_h1 module not available")