diff --git a/.github/workflows/publish.yaml b/.github/workflows/publish.yaml
index 04440f407..b5d26817d 100644
--- a/.github/workflows/publish.yaml
+++ b/.github/workflows/publish.yaml
@@ -41,7 +41,9 @@ jobs:
         # manylinux docker image, but I haven't figured out how to install CUDA on manylinux.
         os: [ubuntu-22.04, ubuntu-22.04-arm]
         python-version: ["3.10", "3.11", "3.12", "3.13"]
-        torch-version: ["2.6.0", "2.7.1", "2.8.0", "2.9.1", "2.10.0"]
+        # Keep in sync with setup.py get_wheel_url() torch major.minor tags.
+        # New torch minors require a tagged release to publish matching wheels (#999).
+        torch-version: ["2.6.0", "2.7.1", "2.8.0", "2.9.1", "2.10.0", "2.11.0", "2.12.1", "2.13.0"]
         cuda-version: ["11.8.0", "12.9.1", "13.0.1"]
         # We need separate wheels that either uses C++11 ABI (-D_GLIBCXX_USE_CXX11_ABI) or not.
         # Pytorch wheels currently don't use it, but nvcr images have Pytorch compiled with C++11 ABI.
@@ -56,6 +58,12 @@ jobs:
             cuda-version: "11.8.0"
           - torch-version: "2.10.0"
             cuda-version: "11.8.0"
+          - torch-version: "2.11.0"
+            cuda-version: "11.8.0"
+          - torch-version: "2.12.1"
+            cuda-version: "11.8.0"
+          - torch-version: "2.13.0"
+            cuda-version: "11.8.0"
           # CUDA 13.0 is only supported by PyTorch 2.9+
           - torch-version: "2.6.0"
             cuda-version: "13.0.1"
@@ -78,6 +86,12 @@ jobs:
             cxx11_abi: "FALSE"
           - torch-version: "2.10.0"
             cxx11_abi: "FALSE"
+          - torch-version: "2.11.0"
+            cxx11_abi: "FALSE"
+          - torch-version: "2.12.1"
+            cxx11_abi: "FALSE"
+          - torch-version: "2.13.0"
+            cxx11_abi: "FALSE"
     uses: ./.github/workflows/_build.yml
     with:
       runs-on: ${{ matrix.os }}
diff --git a/README.md b/README.md
index 2aa92d789..885ee517d 100755
--- a/README.md
+++ b/README.md
@@ -36,6 +36,13 @@ Install PyTorch first. By default, `mamba-ssm` installs the core package without
 | CUDA selective scan opt-in | `MAMBA_KEEP_CUDA_BUILD=TRUE pip install mamba-ssm --no-build-isolation` | Installs `selective_scan_cuda`; pip first tries a matching prebuilt CUDA/HIP wheel, then compiles locally if no wheel is available. |
 | Force local CUDA selective scan build | `MAMBA_FORCE_BUILD=TRUE MAMBA_KEEP_CUDA_BUILD=TRUE pip install mamba-ssm --no-build-isolation` | Skips cached wheels and compiles `selective_scan_cuda` locally. |
 
+Prebuilt CUDA wheels (when using `MAMBA_KEEP_CUDA_BUILD=TRUE`) are published on
+[GitHub releases](https://github.com/state-spaces/mamba/releases) for select
+torch/CUDA/Python/platform combinations. The release CI matrix in
+`.github/workflows/publish.yaml` defines which torch versions are built; if your
+stack is newer than the latest release (e.g. torch 2.11+ on Colab), pip falls
+back to a local `selective_scan_cuda` compile with no warning beyond setup logs.
+
 `--no-build-isolation` is required for CUDA builds so that pip uses your existing CUDA-enabled
 PyTorch instead of installing torch-cpu in an isolated build environment.