diff --git a/tensorrt_llm/_torch/auto_deploy/custom_ops/cuda_backend_causal_conv.py b/tensorrt_llm/_torch/auto_deploy/custom_ops/cuda_backend_causal_conv.py
index fbfc2fad614..b4e43b949af 100644
--- a/tensorrt_llm/_torch/auto_deploy/custom_ops/cuda_backend_causal_conv.py
+++ b/tensorrt_llm/_torch/auto_deploy/custom_ops/cuda_backend_causal_conv.py
@@ -186,7 +186,7 @@ def _cuda_cached_causal_conv1d(
         # Use true start offsets for decode tokens (tail after prefills)
         decode_idx = seq_start[num_prefill:].to(torch.long)
         x_decode = inp_flat.index_select(0, decode_idx)  # [num_decode, C_in]
-
+        slot_idx_decode = slot_idx[num_prefill:].to(torch.int32)
         y_dec = causal_conv1d_update(
             x_decode,  # [batch, dim]
             conv_state_cache,
@@ -194,7 +194,7 @@ def _cuda_cached_causal_conv1d(
             bias,
             activation=None,
             cache_seqlens=None,
-            conv_state_indices=slot_idx[num_prefill:].to(torch.int32),
+            conv_state_indices=slot_idx_decode,
             pad_slot_id=PAD_SLOT_ID,
         )
 
diff --git a/tensorrt_llm/_torch/auto_deploy/custom_ops/triton_backend_mamba.py b/tensorrt_llm/_torch/auto_deploy/custom_ops/triton_backend_mamba.py
index 9cf141ce24d..4a98a600617 100644
--- a/tensorrt_llm/_torch/auto_deploy/custom_ops/triton_backend_mamba.py
+++ b/tensorrt_llm/_torch/auto_deploy/custom_ops/triton_backend_mamba.py
@@ -124,7 +124,12 @@ def _triton_cached_ssm_transform(
 
     # Decode: batch single-token updates via selective_state_update
     if num_decode > 0:
-        decode_idx = seq_start[num_prefill:].to(torch.long)
+        # In generate-only (s == 1), each batch element has one token and seq_start entries
+        # are typically zeros. Use arange over the flattened batch to index tokens correctly.
+        if s == 1:
+            decode_idx = torch.arange(bs, device=device, dtype=torch.long)
+        else:
+            decode_idx = seq_start[num_prefill:].to(torch.long)
         slot_idx_decode = slot_idx[num_prefill:].to(torch.long)
 
         x_decode = hs_flat.index_select(0, decode_idx)  # [nd, H, D]
@@ -237,7 +242,8 @@ def get_cache_initializers(
             ssm_state_size = max(1, B_fake.shape[-1])
 
         def _get_ssm_cache(si: SequenceInfo):
-            return torch.empty(
+            # Initialize to zeros so brand-new sequences start from a clean state.
+            return torch.zeros(
                 si.max_batch_size,
                 num_heads,
                 head_dim,
diff --git a/tests/unittest/_torch/auto_deploy/unit/singlegpu/custom_ops/test_triton_mamba_cached_op.py b/tests/unittest/_torch/auto_deploy/unit/singlegpu/custom_ops/test_triton_mamba_cached_op.py
index 6cce60f1684..97a23572206 100644
--- a/tests/unittest/_torch/auto_deploy/unit/singlegpu/custom_ops/test_triton_mamba_cached_op.py
+++ b/tests/unittest/_torch/auto_deploy/unit/singlegpu/custom_ops/test_triton_mamba_cached_op.py
@@ -28,7 +28,6 @@ def mamba_env():
     return {"device": device, "dtype": dtype, "atol": atol, "rtol": rtol}
 
 
-@pytest.mark.skip(reason="https://nvbugspro.nvidia.com/bug/5548861")
 def test_triton_generate_only_with_slot_mapping(mamba_env):
     device = mamba_env["device"]
     dtype = mamba_env["dtype"]