diff --git a/.gitattributes b/.gitattributes
index a8dc694..91d7e6c 100644
--- a/.gitattributes
+++ b/.gitattributes
@@ -2,4 +2,3 @@
 
 # Auto detect text files and perform LF normalization
 * text=auto
-
diff --git a/.github/CODE_OF_CONDUCT.md b/.github/CODE_OF_CONDUCT.md
new file mode 100644
index 0000000..6e8dfb8
--- /dev/null
+++ b/.github/CODE_OF_CONDUCT.md
@@ -0,0 +1,58 @@
+# Contributor Covenant Code of Conduct
+
+## Our Pledge
+
+We pledge to make our community welcoming, safe, and equitable for all.
+
+We are committed to fostering an environment that respects and promotes the dignity, rights, and contributions of all individuals, regardless of characteristics including race, ethnicity, caste, color, age, physical characteristics, neurodiversity, disability, sex or gender, gender identity or expression, sexual orientation, language, philosophy or religion, national or social origin, socio-economic position, level of education, or other status. The same privileges of participation are extended to everyone who participates in good faith and in accordance with this Covenant.
+
+## Encouraged Behaviors
+
+While acknowledging differences in social norms, we all strive to meet our community’s expectations for positive behavior. We also understand that our words and actions may be interpreted differently than we intend based on culture, background, or native language.
+
+With these considerations in mind, we agree to behave mindfully toward each other and act in ways that center our shared values, including:
+
+1. Respecting the **purpose of our community**, our activities, and our ways of gathering.
+2. Engaging **kindly and honestly** with others.
+3. Respecting **different viewpoints** and experiences.
+4. **Taking responsibility** for our actions and contributions.
+5. Gracefully giving and accepting **constructive feedback**.
+6. Committing to **repairing harm** when it occurs.
+7. Behaving in other ways that promote and sustain the **well-being of our community**.
+
+## Restricted Behaviors
+
+We agree to restrict the following behaviors in our community. Instances, threats, and promotion of these behaviors are violations of this Code of Conduct.
+
+1. **Harassment**. Violating explicitly expressed boundaries or engaging in unnecessary personal attention after any clear request to stop.
+2. **Character attacks**. Making insulting, demeaning, or pejorative comments directed at a community member or group of people.
+3. **Stereotyping or discrimination**. Characterizing anyone’s personality or behavior on the basis of immutable identities or traits.
+4. **Sexualization**. Behaving in a way that would generally be considered inappropriately intimate in the context or purpose of the community.
+5. **Violating confidentiality**. Sharing or acting on someone’s personal or private information without their permission.
+6. **Endangerment**. Causing, encouraging, or threatening violence or other harm toward any person or group.
+7. Behaving in other ways that **threaten the well-being** of our community.
+
+### Other Restrictions
+
+1. **Misleading identity**. Impersonating someone else for any reason, or pretending to be someone else to evade enforcement actions.
+2. **Failing to credit sources**. Not properly crediting the sources of content you contribute.
+3. **Promotional materials**. Sharing marketing or other commercial content in a way that is outside the norms of the community.
+4. **Irresponsible communication**. Failing to responsibly present content which includes, links or describes any other restricted behaviors.
+
+
+## Our Responsibilities
+
+Project maintainers are responsible for clarifying the standards of acceptable behavior and are expected to take appropriate and fair corrective action in response to any instances of unacceptable behavior.
+
+Project maintainers have the right and responsibility to remove, edit, or reject comments, commits, code, wiki edits, issues, and other contributions that are not aligned to this Code of Conduct, or to ban temporarily or permanently any contributor for other behaviors that they deem inappropriate, threatening, offensive, or harmful.
+
+
+## Scope
+
+This Code of Conduct applies both within project spaces and in public spaces when an individual is representing the project or its community. Examples of representing a project or community include using an official project e-mail address, posting via an official social media account, or acting as an appointed representative at an online or offline event. Representation of a project may be further defined and clarified by project maintainers.
+
+
+
+## Attribution
+
+This Code of Conduct is adapted from the [Contributor Covenant](http://contributor-covenant.org), version 3.0, available at [https://www.contributor-covenant.org/version/3/0/code_of_conduct/](https://www.contributor-covenant.org/version/3/0/code_of_conduct/)
diff --git a/.github/CONTRIBUTING.md b/.github/CONTRIBUTING.md
new file mode 100644
index 0000000..f7f87c8
--- /dev/null
+++ b/.github/CONTRIBUTING.md
@@ -0,0 +1,42 @@
+# Introduction
+
+Thank you for your interest in contributing to our project! We welcome contributions from the community and appreciate your efforts to help improve our codebase. This document provides guidelines and instructions for contributing to our project.
+
+
+## Installation
+
+To set up the development environment, please follow these steps:
+
+1. Fork the repository:
+   ```bash
+    gh repo fork MatchCake/TorchPfaffian
+   ```
+2. Clone your forked repository:
+   ```bash
+    git clone <your-fork-url>
+    ```
+3. Navigate to the project directory:
+4. Initialize the Virtual Environment using [uv](https://docs.astral.sh/uv/):
+    ```bash
+     uv sync --dev --extra <extra-option>
+    ```
+    - Extra options: See the [README.md](../README.md) for details on extra options for installation.
+5. Set up Pre-commit hooks:
+    ```bash
+     uv run pre-commit install
+    ```
+
+# Guidelines
+
+1. When you whish to contribute, please create a new fork of the repository and create a new branch for your changes.
+2. Make your changes and commit them with clear and descriptive commit messages.
+3. Push your changes to your forked repository.
+4. Create a pull request to the main repository on the `dev` branch, describing the changes you have made and why they are necessary.
+5. Our team will review your pull request and provide feedback. We may ask for changes or improvements before merging your contribution.
+6. Once your contribution is approved, it will be merged into the main repository on the `dev` branch and will eventually be included in the next release.
+7. Please ensure that your code follows the project's coding standards and guidelines, and that it includes appropriate tests and documentation.
+
+
+## Code of Conduct
+
+We expect all contributors to adhere to our code of conduct, which can be found in the [CODE_OF_CONDUCT.md](CODE_OF_CONDUCT.md) file. This code of conduct outlines our expectations for behavior and communication within the community, and we take it seriously to ensure a welcoming and inclusive environment for all contributors.
diff --git a/.github/ISSUE_TEMPLATE/bug_report.yml b/.github/ISSUE_TEMPLATE/bug_report.yml
new file mode 100644
index 0000000..4bad241
--- /dev/null
+++ b/.github/ISSUE_TEMPLATE/bug_report.yml
@@ -0,0 +1,64 @@
+name: Bug report
+description: File a bug report
+title: '[BUG]'
+labels: "bug :bug:"
+
+body:
+  - type: markdown
+    id: description
+    attributes:
+      value: |
+          ## Before posting a bug report
+          Search existing GitHub issues to make sure the issue does not already exist.
+
+          # Issue description
+          Description of the issue - include code snippets in the Source code section below and screenshots if relevant.
+  - type: textarea
+    id: expected
+    attributes:
+      label: Expected behavior
+      description: |
+        What do you expect to happen?
+    validations:
+      required: true
+  - type: textarea
+    id: actual
+    attributes:
+      label: Actual behavior
+      description: |
+        What actually happens?
+    validations:
+      required: true
+  - type: textarea
+    id: info
+    attributes:
+      label: Additional information
+      description: |
+        Please provide any additional information here that might be necessary to reproduce the issue.
+  - type: textarea
+    id: sourcecode
+    attributes:
+      label: Source code
+      description: Please include a minimal non-working example, or any other code related to the issue.
+      render: shell
+  - type: textarea
+    id: tracebacks
+    attributes:
+      label:  Tracebacks
+      description: Please include the error tracebacks related to the issue here.
+      render: shell
+  - type: textarea
+    id: system
+    attributes:
+      label:  System information
+      description: Please provide informations about you python version and os. The command `python -c "import sys; print(sys.version, sys.platform)"` can be used to get this information. Provide also the output of `pip list` command.
+      render: shell
+    validations:
+      required: true
+  - type: checkboxes
+    id: terms
+    attributes:
+      label: Existing GitHub issues
+      options:
+        - label: I have searched existing GitHub issues to make sure the issue does not already exist.
+          required: true
diff --git a/.github/ISSUE_TEMPLATE/feature_request.yml b/.github/ISSUE_TEMPLATE/feature_request.yml
new file mode 100644
index 0000000..9ca5a64
--- /dev/null
+++ b/.github/ISSUE_TEMPLATE/feature_request.yml
@@ -0,0 +1,44 @@
+name: Feature request
+description: Suggest a new feature
+labels: "enhancement :sparkles:"
+
+body:
+  - type: markdown
+    id: description
+    attributes:
+      value: |
+          ## Before posting a feature request
+          Search existing GitHub issues to make sure the issue does not already exist.
+
+          # Feature description
+          Description of the feature request - include code snippets and screenshots here if relevant.
+  - type: textarea
+    id: details
+    attributes:
+      label: Feature details
+      description: |
+        What feature would you like to have?
+    validations:
+      required: true
+  - type: textarea
+    id: implementation
+    attributes:
+      label: Implementation
+      description: |
+        Do you have an idea for how this could be implemented? Please include those details here.
+  - type: dropdown
+    id: urgency
+    attributes:
+      label: How important would you say this feature is?
+      options:
+        - "1: Not important. Would be nice to have."
+        - "2: Somewhat important. Needed this quarter."
+        - "3: Very important! Blocking work."
+    validations:
+      required: true
+  - type: textarea
+    id: info
+    attributes:
+      label: Additional information
+      description: |
+        Please provide any additional information here. Include potential alternative solutions, or workarounds, as well as references, if any.
diff --git a/.github/pull_request_template.md b/.github/pull_request_template.md
new file mode 100644
index 0000000..38248d1
--- /dev/null
+++ b/.github/pull_request_template.md
@@ -0,0 +1,19 @@
+# Description
+
+Please provide a brief description of the changes you are making in this pull request.
+
+------------------------------------------------------------------------------------------------------------
+
+# Checklist
+
+Please complete the following checklist when submitting a PR. The PR will not be reviewed until all items are checked.
+
+- [ ] All new features include a unit test.
+      Make sure that the tests passed and the coverage is
+      sufficient by running
+      `uv run pytest --session-timeout=600`.
+- [ ] All new functions and code are clearly documented.
+- [ ] The code passes all pre-commit hooks.
+      You can do this by running `uvx pre-commit run --all-files`.
+- [ ] The code is type-checked using Mypy.
+      You can do this by running `uv run mypy src tests`.
diff --git a/.github/workflows/build_dist.yml b/.github/workflows/build_dist.yml
index 7ce5d56..95fc29b 100644
--- a/.github/workflows/build_dist.yml
+++ b/.github/workflows/build_dist.yml
@@ -4,6 +4,15 @@ on:
   push:
     branches: ["main"]
 
+permissions:
+  contents: write
+  pull-requests: write
+  actions: write
+  checks: write
+  statuses: write
+  issues: write
+  discussions: write
+  id-token: write
 
 jobs:
   Build-Dist:
@@ -12,35 +21,83 @@ jobs:
 
     steps:
     - uses: actions/checkout@v3
-    - name: Set up Python 3.10
+    - name: Set up Python 3.11
       uses: actions/setup-python@v3
       with:
-        python-version: "3.10"
+        python-version: "3.11"
 
+    - name: Install uv
+      uses: astral-sh/setup-uv@v6
+      with:
+        version: "0.9.2"
+        python-version: "3.11"
+        enable-cache: true
     - name: Install dependencies
       run: |
-        python -m pip install --upgrade pip
-        pip install flake8
-        if [ -f requirements.txt ]; then pip install -r requirements.txt; fi
-        pip install wheel
-        python -m pip install --upgrade build
+        uv venv .venv
+        uv sync --locked --dev --extra cpu
+
+    - name: Gather new package version
+      id: version
+      uses: anothrNick/github-tag-action@1.61.0
+      env:
+        GITHUB_TOKEN: ${{secrets.GITHUB_TOKEN}}
+        WITH_V: false
+        DEFAULT_BUMP: patch
+        DRY_RUN: true
+
+    - name: Bump package version in pyproject.toml
+      run: |
+        uv version ${{steps.version.outputs.new_tag}}
+        uv lock
 
     - name: Build dist
       run: |
-        python -m build
+        uv run python -m build --sdist --wheel --no-isolation --outdir dist/ .
+        uv run twine check dist/*
 
-    - name: Commit
+    - name: Commit updated pyproject.toml
       run: |
-        git add -f dist
         git config --local user.email "action@github.com"
         git config --local user.name "GitHub Action"
-        git commit -m "Update dist"
+        git add pyproject.toml
+        git add uv.lock
+        git diff-index --quiet HEAD || git commit -m "Updating version of pyproject.toml"
 
-    - name: Push
+    - name: Push the new pyproject.toml
       uses: ad-m/github-push-action@master
       with:
         github_token: ${{secrets.GITHUB_TOKEN}}
         branch: main
         force: true
-        directory: dist
 
+    - name: Create Tag
+      id: tag
+      uses: anothrNick/github-tag-action@1.61.0
+      env:
+        GITHUB_TOKEN: ${{secrets.GITHUB_TOKEN}}
+        WITH_V: false
+        DEFAULT_BUMP: patch
+        DRY_RUN: false
+
+    - name: Create Release
+      uses: softprops/action-gh-release@v1
+      with:
+        tag_name: ${{ steps.tag.outputs.new_tag }}
+        generate_release_notes: true
+
+    - name: Merge 'main' branch -> 'dev' branch
+      uses: devmasx/merge-branch@master
+      with:
+        type: now
+        from_branch: main
+        target_branch: dev
+        message: "Automatic merge from 'main' into 'dev' [skip actions]"
+        label_name: "gh-actions"
+        github_token: ${{ secrets.GITHUB_TOKEN }}
+
+    - name: Publish distribution to PyPI
+      uses: pypa/gh-action-pypi-publish@release/v1
+      with:
+        skip_existing: true
+        verbose: true
diff --git a/.github/workflows/coverage_badge.yml b/.github/workflows/coverage_badge.yml
new file mode 100644
index 0000000..158c43b
--- /dev/null
+++ b/.github/workflows/coverage_badge.yml
@@ -0,0 +1,51 @@
+name: Update-Coverage-Badge
+
+on:
+  push:
+    branches: ["main"]
+
+permissions:
+  contents: write
+  pull-requests: write
+  actions: write
+  checks: write
+  statuses: write
+  issues: write
+  discussions: write
+  id-token: write
+
+jobs:
+  Update-Coverage-Badge:
+    name: Update-Coverage-Badge
+    runs-on: ubuntu-latest
+
+    steps:
+    - uses: actions/checkout@v3
+    - name: Set up Python 3.11
+      uses: actions/setup-python@v3
+      with:
+        python-version: "3.11"
+
+    - name: Install uv
+      uses: astral-sh/setup-uv@v6
+      with:
+        version: "0.9.2"
+        python-version: "3.11"
+        enable-cache: true
+
+    - name: Install dependencies
+      run: |
+        uv venv .venv
+        uv sync --locked --dev --extra cpu
+
+    - name: Test Unittests with pytest
+      run: |
+        uv run pytest tests --cov=src --cov-branch --cov-report="xml:tests/.tmp/coverage.xml" --cov-report=term-missing
+
+    - name: Upload Coverage to Codecov
+      uses: codecov/codecov-action@v5
+      with:
+        fail_ci_if_error: true
+        token: ${{ secrets.CODECOV_TOKEN }}
+        files: tests/.tmp/coverage.xml
+        verbose: true
diff --git a/.github/workflows/docs.yml b/.github/workflows/docs.yml
index 7ddad9b..6dfd348 100644
--- a/.github/workflows/docs.yml
+++ b/.github/workflows/docs.yml
@@ -2,7 +2,7 @@ name: Docs
 
 on:
   push:
-    branches: ["main"]
+    branches: ["main", "dev"]
 
 
 jobs:
@@ -12,28 +12,32 @@ jobs:
 
     steps:
     - uses: actions/checkout@v3
-    - name: Set up Python 3.10
+      with:
+        fetch-depth: 0
+
+    - name: Set up Python 3.11
       uses: actions/setup-python@v3
       with:
-        python-version: "3.10"
+        python-version: "3.11"
+
+    - name: Install uv
+      uses: astral-sh/setup-uv@v6
+      with:
+        version: "0.9.2"
+        python-version: "3.11"
+        enable-cache: true
 
     - name: Install dependencies
       run: |
-        python -m pip install --upgrade pip
-        pip install flake8
-        if [ -f requirements.txt ]; then pip install -r requirements.txt; fi
-        pip install -U sphinx==6.2.1 docutils==0.18.1 sphinx_rtd_theme==1.2.2 sphinxcontrib-bibtex==2.5.0
-        pip install sphinx-theme
-        pip install groundwork-sphinx-theme
-        pip install karma_sphinx_theme
-        pip install sphinx-mdinclude
+        uv venv .venv
+        uv sync --locked --dev --group docs --extra cpu
 
     - name: Build the sphinx docs
       run: |
-        sphinx-apidoc -f -o ./sphinx/source ./src/torch_pfaffian
-        make -C sphinx clean
-        python sphinx/clean_html_files.py
-        make -C sphinx html
+        uv run sphinx-apidoc -f -o ./sphinx/source ./src/torch_pfaffian
+        uv run make -C sphinx clean
+        uv run python sphinx/clean_html_files.py
+        uv run make -C sphinx html
         touch sphinx/build/html/.nojekyll
 
     - name: Checkout gh-pages branch
@@ -44,7 +48,7 @@ jobs:
     - name: Copy build files to docs folder
       run: |
         cp -a sphinx/build/html/. docs/
-        python sphinx/make_html_files_list.py
+        uv run python sphinx/make_html_files_list.py
         rm -rf sphinx/build
 
     - name: Commit to gh-pages branch
@@ -60,4 +64,4 @@ jobs:
         github_token: ${{secrets.GITHUB_TOKEN}}
         branch: gh-pages
         force: true
-        directory: docs
\ No newline at end of file
+        directory: docs
diff --git a/.github/workflows/publish.yml b/.github/workflows/publish.yml
deleted file mode 100644
index bdbd65e..0000000
--- a/.github/workflows/publish.yml
+++ /dev/null
@@ -1,40 +0,0 @@
-name: Publish to PyPI.org
-on:
-  release:
-    types: [published]
-jobs:
-  pypi:
-    runs-on: ubuntu-latest
-    environment:
-      name: pypi
-      url: https://pypi.org/p/TorchPfaffian
-    permissions:
-      id-token: write
-    steps:
-      - name: Checkout
-        uses: actions/checkout@v3
-        with:
-          fetch-depth: 0
-      
-      - name: Set up Python 3.10
-        uses: actions/setup-python@v3
-        with:
-          python-version: "3.10"
-
-      - name: Install dependencies
-        run: |
-          python -m pip install --upgrade pip
-          pip install flake8
-          if [ -f requirements.txt ]; then pip install -r requirements.txt; fi
-          pip install wheel
-
-      - name: Build dist
-        run: |
-          python -m pip install --upgrade build
-          python -m build
-      - name: Publish distribution to PyPI
-        if: startsWith(github.ref, 'refs/tags')
-        uses: pypa/gh-action-pypi-publish@release/v1
-        with:
-          skip_existing: true
-          verbose: true
diff --git a/.github/workflows/tests.yml b/.github/workflows/tests.yml
index b0d6784..cb599e5 100644
--- a/.github/workflows/tests.yml
+++ b/.github/workflows/tests.yml
@@ -1,67 +1,137 @@
-# This workflow will install Python dependencies, run tests and lint with a single version of Python
-# For more information see: https://help.github.com/actions/language-and-framework-guides/using-python-with-github-actions
-
 name: Tests
 
 on:
-  push:
-    branches: [ "main" ]
   pull_request:
-    branches: [ "main" ]
+    branches: [ "*" ]
 
 permissions:
-  contents: read
+  contents: write
+  pull-requests: write
+  actions: write
+  checks: write
+  statuses: write
+  issues: write
+  discussions: write
 
 jobs:
   Run-tests-on-Ubuntu:
     name: Run tests on Ubuntu-latest
     runs-on: ubuntu-latest
+    strategy:
+      matrix:
+        python-version: [ "3.10", "3.11", "3.12", "3.13", "3.14", ]
 
     steps:
-    - uses: actions/checkout@v3
-    - name: Set up Python 3.10
-      uses: actions/setup-python@v3
-      with:
-        python-version: "3.10"
-    - name: Install dependencies
-      run: |
-        python -m pip install --upgrade pip
-        pip install flake8 pytest
-        pip install -U pytest-cov coverage-lcov
-        if [ -f requirements.txt ]; then pip install -r requirements.txt; fi
-    - name: Lint with flake8
-      run: |
-        # stop the build if there are Python syntax errors or undefined names
-        flake8 . --count --select=E9,F63,F7,F82 --show-source --statistics
-        # exit-zero treats all errors as warnings. The GitHub editor is 127 chars wide
-        flake8 . --count --exit-zero --max-complexity=10 --max-line-length=127 --statistics
-    - name: Test with pytest
-      run: |
-        python run_pytests.py tests --N_RANDOM_TESTS_PER_CASE=10 --run_slow=False
-        coverage-lcov
+      - uses: actions/checkout@v3
+      - name: Set up Python ${{ matrix.python-version }}
+        uses: actions/setup-python@v3
+        with:
+          python-version: ${{ matrix.python-version }}
+      - name: Install uv
+        uses: astral-sh/setup-uv@v6
+        with:
+          version: "0.9.2"
+          python-version: ${{ matrix.python-version }}
+          enable-cache: true
+      - name: Install dependencies
+        run: |
+          uv venv .venv
+          uv sync --locked --dev --extra cpu
+      - name: Test Linting
+        run: |
+          uv run ruff format --check src tests
+          uv run ruff check src tests
+      - name: Test Typing
+        continue-on-error: true
+        run: |
+          uv run mypy src tests
+      - name: Test Unittests with pytest
+        run: |
+          uv run pytest tests \
+            ${{ github.event.pull_request.head.repo.full_name != github.repository && '--cov-fail-under=98' || '' }} \
+            --cov=src --cov-branch --cov-report="xml:tests/.tmp/coverage.xml" --cov-report=term-missing --durations=30 --session-timeout=600
+
+      - name: Debug coverage before upload
+        if: matrix.python-version == '3.11'
+        run: |
+          echo "=== Coverage summary ==="
+          head -5 tests/.tmp/coverage.xml
+
+      - name: Upload coverage artifact
+        if: |
+          matrix.python-version == '3.11' &&
+          github.event.pull_request.head.repo.full_name == github.repository
+        uses: actions/upload-artifact@v4
+        with:
+          name: coverage
+          path: tests/.tmp/coverage.xml
+          if-no-files-found: error
+          retention-days: 1
+
+      - name: Test Build
+        if: ${{ matrix.python-version == '3.11' }}
+        run: |
+          uv run python -m build --sdist --wheel --no-isolation --outdir dist/ .
+          uv run twine check dist/*
 
   Run-tests-on-Windows:
     name: Run tests on Windows-latest
+    needs: [ Run-tests-on-Ubuntu ]
     runs-on: windows-latest
+    if: success()
+    strategy:
+      matrix:
+        python-version: [ "3.10", "3.11", "3.12", "3.13", "3.14", ]
 
     steps:
-    - uses: actions/checkout@v3
-    - name: Set up Python 3.10
-      uses: actions/setup-python@v3
-      with:
-        python-version: "3.10"
-    - name: Install dependencies
-      run: |
-        python -m pip install --upgrade pip
-        pip install flake8 pytest
-        pip install -r requirements.txt
-        pip install git+https://github.com/JeremieGince/PythonBasicTools
-    - name: Lint with flake8
-      run: |
-        # stop the build if there are Python syntax errors or undefined names
-        flake8 . --count --select=E9,F63,F7,F82 --show-source --statistics
-        # exit-zero treats all errors as warnings. The GitHub editor is 127 chars wide
-        flake8 . --count --exit-zero --max-complexity=10 --max-line-length=127 --statistics
-    - name: Test with pytest
-      run: |
-        python run_pytests.py tests --N_RANDOM_TESTS_PER_CASE=10 --run_slow=False
+      - uses: actions/checkout@v3
+      - name: Set up Python ${{ matrix.python-version }}
+        uses: actions/setup-python@v3
+        with:
+          python-version: ${{ matrix.python-version }}
+      - name: Install uv
+        uses: astral-sh/setup-uv@v6
+        with:
+          version: "0.9.2"
+          python-version: ${{ matrix.python-version }}
+          enable-cache: true
+      - name: Install dependencies
+        run: |
+          uv venv .venv
+          uv sync --locked --dev --extra cpu
+      - name: Test Unittests with pytest
+        run: |
+          uv run pytest tests --session-timeout=600
+
+
+  Coverage:
+    name: Post Coverage Report
+    needs: [ Run-tests-on-Ubuntu ]
+    runs-on: ubuntu-latest
+    if: success() && github.event.pull_request.head.repo.full_name == github.repository
+    permissions:
+      contents: write
+      pull-requests: write
+      actions: write
+      checks: write
+      statuses: write
+      issues: write
+      discussions: write
+    steps:
+      - uses: actions/checkout@v4
+      - name: Download coverage artifact
+        uses: actions/download-artifact@v4
+        with:
+          path: tests/.tmp
+          name: coverage
+      - run: ls -R tests/.tmp
+      - name: Debug coverage file
+        run: cat tests/.tmp/coverage.xml | head -50
+      - name: Post coverage
+        uses: orgoro/coverage@v3.2
+        with:
+          token: ${{ secrets.GITHUB_TOKEN }}
+          coverageFile: tests/.tmp/coverage.xml
+          thresholdAll: 0.98
+          thresholdNew: 0.98
+          thresholdModified: 0.98
diff --git a/.gitignore b/.gitignore
index a368195..840287c 100644
--- a/.gitignore
+++ b/.gitignore
@@ -178,6 +178,7 @@ instance/
 # Sphinx documentation
 docs/_build/
 doc/_build/
+sphinx/source/notebooks/
 
 # PyBuilder
 target/
@@ -294,6 +295,7 @@ pip-selfcheck.json
 
 user/
 tests/tmp
+tests/.tmp
 .report.json
 tests/master_src
 report_dir
@@ -301,4 +303,6 @@ auxil
 out
 tmp_report_dir
 .tmp_report.json
-coverage.json
\ No newline at end of file
+coverage.json
+dist/*.whl
+dist/*.tar.gz
diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
new file mode 100644
index 0000000..c90d531
--- /dev/null
+++ b/.pre-commit-config.yaml
@@ -0,0 +1,42 @@
+repos:
+  - repo: https://github.com/pre-commit/pre-commit-hooks
+    rev: v6.0.0
+    hooks:
+      - id: check-added-large-files
+        # uv.lock is a generated lockfile that legitimately exceeds the 500 KB default.
+        args: ["--maxkb=1024"]
+      - id: check-docstring-first
+      - id: check-merge-conflict
+      - id: check-toml
+      - id: check-yaml
+      - id: detect-private-key
+      - id: trailing-whitespace
+      - id: end-of-file-fixer
+
+  - repo: https://github.com/astral-sh/uv-pre-commit
+    rev: 0.9.28
+    hooks:
+      - id: uv-sync
+      - id: uv-lock
+      - id: uv-export
+        args:
+          [
+            "--frozen",
+            "--no-hashes",
+            "--output-file=requirements.txt",
+            "--quiet",
+          ]
+
+  - repo: https://github.com/tsvikas/sync-with-uv
+    rev: v0.5.0
+    hooks:
+      - id: sync-with-uv
+
+  - repo: https://github.com/astral-sh/ruff-pre-commit
+    rev: v0.15.16
+    hooks:
+      # Run the linter.
+      - id: ruff-check
+        args: [--fix, --exit-non-zero-on-fix]
+      # Run the formatter.
+      - id: ruff-format
diff --git a/AGENTS.md b/AGENTS.md
new file mode 100644
index 0000000..2dce1fc
--- /dev/null
+++ b/AGENTS.md
@@ -0,0 +1,44 @@
+# Code style
+
+## Files
+- No file-level docstrings. Move any documentation into the class docstring instead.
+- For files with only module-level functions, omit the module docstring entirely.
+
+## Comments
+- No separator comments between methods or functions (e.g. no `# --- Public interface ---`).
+- Inline comments inside function bodies are fine when they clarify non-obvious things (e.g. tensor shape annotations).
+- Use em dashes (—) sparingly in docstrings and comments. Prefer plain sentence structure over dashes.
+
+## Naming conventions
+- Variable names must be lowercase with underscores (`snake_case`). No single-letter or abbreviated names unless the meaning is unambiguous in context (e.g. `n` for the matrix dimension, `k` for a loop index).
+- Choose descriptive names that reflect the role of the variable (e.g. `block_size` instead of `m`, `n_terms` instead of `K`).
+- Module-level constants must be `UPPER_SNAKE_CASE`.
+
+## Type hints and docstrings
+- All public functions and methods must have full type annotations on every parameter and the return type.
+- Docstrings use Sphinx style: `:param name: description`, `:return: description`, `:rtype: type`.
+
+## Tensors and backends
+- Any function that accepts a tensor argument must return a tensor with the same backend, dtype, and device as its primary input.
+- Tensor functions must support arbitrary leading batch dimensions (expressed as `...` in shape comments, e.g. `(..., 2n, 2n)`).
+- Hardware-specific branches (CUDA, platform-only code) that cannot run in CI must be marked `# pragma: no cover`.
+
+## Testing
+- Every new class or module-level function must have an associated test class added in the same PR/commit.
+- Tests must achieve >98% code coverage for the new code. Numerical comparisons use tolerance constants from `tests/configs.py` (`ATOL_SCALAR_COMPARISON`, `ATOL_MATRIX_COMPARISON`, etc.) — never hardcode tolerance values.
+- The `tests/` folder must mirror the package structure (replacing `src/torch_pfaffian/` with `tests/` and prefixing each path segment with `test_`). For example, `src/torch_pfaffian/utils.py` → `tests/test_utils.py`, and `src/torch_pfaffian/strategies/pfaffian_block_det.py` → `tests/test_strategies/test_pfaffian_block_det.py`.
+- One test class per file. Each test file contains exactly one `Test*` class.
+- All tests must be methods of a `Test*` class. No module-level `test_*` functions.
+- Tests must be compatible with pytest-xdist parallel execution: no shared mutable state between tests, no dependency on execution order, and no hardcoded resources (ports, file paths) that would conflict across workers.
+- Prefer `np.testing` and `torch.testing` assertion helpers over bare `assert` statements for numerical comparisons. Use `torch.autograd.gradcheck` when testing gradient correctness.
+
+## Class method ordering
+Methods within a class must appear in this order:
+1. `@staticmethod`
+2. `@classmethod`
+3. Constructor (`__init__`)
+4. Operator / dunder methods (`__call__`, `__repr__`, …)
+5. Public methods
+6. Protected methods (`_method`)
+7. Private methods (`__method`)
+8. Properties (`@property`)
diff --git a/CLAUDE.md b/CLAUDE.md
new file mode 100644
index 0000000..c317064
--- /dev/null
+++ b/CLAUDE.md
@@ -0,0 +1 @@
+AGENTS.md
diff --git a/README.md b/README.md
index 7c20897..e2c9feb 100644
--- a/README.md
+++ b/README.md
@@ -10,21 +10,21 @@
 ![Tests Workflow](https://github.com/MatchCake/TorchPfaffian/actions/workflows/tests.yml/badge.svg)
 ![Dist Workflow](https://github.com/MatchCake/TorchPfaffian/actions/workflows/build_dist.yml/badge.svg)
 ![Doc Workflow](https://github.com/MatchCake/TorchPfaffian/actions/workflows/docs.yml/badge.svg)
-![Publish Workflow](https://github.com/MatchCake/TorchPfaffian/actions/workflows/publish.yml/badge.svg)
+![Coverage Badge Workflow](https://github.com/MatchCake/TorchPfaffian/actions/workflows/coverage_badge.yml/badge.svg)
 
 
 # Description
 
-TorchPfaffian is a Python package for efficiently computing the Pfaffian of skew-symmetric matrices using PyTorch. 
-Designed as a PyTorch-based alternative to [pfapack](https://github.com/basnijholt/pfapack), it enables GPU 
-acceleration and supports automatic differentiation, making it particularly useful in physics, quantum computing, 
-and machine learning applications.  
+TorchPfaffian is a Python package for efficiently computing the Pfaffian of skew-symmetric matrices using PyTorch.
+Designed as a PyTorch-based alternative to [pfapack](https://github.com/basnijholt/pfapack), it enables GPU
+acceleration and supports automatic differentiation, making it particularly useful in physics, quantum computing,
+and machine learning applications.
 
 ## Features:
-- Efficient Pfaffian computation for skew-symmetric matrices  
-- GPU acceleration via PyTorch  
-- Support for automatic differentiation  
-- Seamless integration with PyTorch tensors  
+- Efficient Pfaffian computation for skew-symmetric matrices
+- GPU acceleration via PyTorch
+- Support for automatic differentiation
+- Seamless integration with PyTorch tensors
 
 
 
@@ -36,10 +36,13 @@ With `python` and `pip` installed, run the following commands to install TorchPf
 pip install torchpfaffian
 ```
 
-With `poetry` installed, run the following commands to install TorchPfaffian:
+For development, this project uses [uv](https://docs.astral.sh/uv/). Clone the repository and set up the
+environment with:
 ```bash
-poetry add torchpfaffian
+uv sync --dev --extra cpu
 ```
+Use the `cu128` or `cu130` extra instead of `cpu` to install a CUDA-enabled build of PyTorch. See
+[.github/CONTRIBUTING.md](.github/CONTRIBUTING.md) for the full contribution workflow.
 
 
 # Important Links
diff --git a/archives/_pfaffian.py b/archives/_pfaffian.py
index 45b7101..13b4aed 100644
--- a/archives/_pfaffian.py
+++ b/archives/_pfaffian.py
@@ -5,13 +5,12 @@
 It is used as a reference to implement the `pfaffian` functions.
 """
 
-
-from typing import Union, Literal, Optional
-import numpy as np
 import warnings
+from typing import Literal, Optional, Union
 
-import tqdm
+import numpy as np
 import pennylane as qml
+import tqdm
 
 from torch_pfaffian import Pfaffian
 
@@ -51,26 +50,24 @@ def _compute_gauss_vector(__matrix, k):
     zero_like = convert_and_cast_like(0, __matrix)
     tau_norm = __matrix[..., k, k + 1][..., None]
     zero_mask = qml.math.isclose(tau_norm, zero_like)
-    tau = qml.math.where(zero_mask, zero_like, __matrix[..., k, k + 2:] / tau_norm)
+    tau = qml.math.where(zero_mask, zero_like, __matrix[..., k, k + 2 :] / tau_norm)
     return tau
 
 
 def _update_matrix_block_kp2_kp2(__matrix, k, tau):
     add_matrix = qml.math.zeros_like(__matrix)
-    add_matrix[..., k + 2:, k + 2:] += (
-        qml.math.einsum("...i,...j->...ij", tau, __matrix[..., k + 2:, k + 1])
-        -
-        qml.math.einsum("...i,...j->...ij", __matrix[..., k + 2:, k + 1], tau)
-    )
+    add_matrix[..., k + 2 :, k + 2 :] += qml.math.einsum(
+        "...i,...j->...ij", tau, __matrix[..., k + 2 :, k + 1]
+    ) - qml.math.einsum("...i,...j->...ij", __matrix[..., k + 2 :, k + 1], tau)
     return __matrix + add_matrix
 
 
 def batch_pfaffian_ltl(
-        __matrix: TensorLike,
-        overwrite_input: bool = False,
-        test_input: bool = False,
-        p_bar: Optional[tqdm.tqdm] = None,
-        show_progress: bool = False
+    __matrix: TensorLike,
+    overwrite_input: bool = False,
+    test_input: bool = False,
+    p_bar: Optional[tqdm.tqdm] = None,
+    show_progress: bool = False,
 ) -> Union[float, complex, TensorLike]:
     r"""
     Compute the Pfaffian of a real or complex skew-symmetric
@@ -117,7 +114,7 @@ def batch_pfaffian_ltl(
 
     # Quick return if possible
     if n % 2 == 1:
-        p_bar.n = n//2
+        p_bar.n = n // 2
         p_bar.set_description("Odd-sized matrix")
         p_bar.close()
         return pfaffian_val * zero_like * matrix[..., 0, 0]  # 0.0 but with require grad if needed
@@ -125,7 +122,7 @@ def batch_pfaffian_ltl(
     p_bar.set_description(f"Computing Pfaffian of {shape} matrix")
     for k in p_bar:
         # First, find the largest entry in A[k+1:,k] and permute it to A[k+1,k]
-        kp = k + 1 + qml.math.abs(matrix[..., k + 1:, k]).argmax(-1)
+        kp = k + 1 + qml.math.abs(matrix[..., k + 1 :, k]).argmax(-1)
         kp1 = qml.math.convert_like(k + 1, kp)
 
         # Check if we need to pivot
@@ -139,7 +136,7 @@ def batch_pfaffian_ltl(
         zero_ss_condition = qml.math.isclose(matrix[..., k + 1, k], zero_like)
         pfaffian_val *= qml.math.where(zero_ss_condition, zero_like, matrix[..., k, k + 1])
         if qml.math.all(zero_ss_condition):
-            p_bar.n = n//2
+            p_bar.n = n // 2
             p_bar.set_description("Pfaffian is zero")
             p_bar.close()
             return pfaffian_val
@@ -176,7 +173,7 @@ def batch_householder_complex(x: TensorLike):
     v_frobenius_norm = qml.math.einsum("...i,...i->...", qml.math.conjugate(v), v) ** 0.5
     v = v / v_frobenius_norm[..., None]
 
-    tau = convert_and_cast_like(2.0+0.0j, x)
+    tau = convert_and_cast_like(2.0 + 0.0j, x)
     return v, tau, -phase * norm_x
 
 
@@ -200,14 +197,14 @@ def _eliminate_ith_column_householder(matrix, i, alpha):
     where_mask = qml.math.cast(qml.math.zeros_like(matrix), dtype=bool)
     where_mask[..., i + 1, i] = True
     where_mask[..., i, i + 1] = True
-    where_mask[..., i + 2:, i] = True
-    where_mask[..., i, i + 2:] = True
+    where_mask[..., i + 2 :, i] = True
+    where_mask[..., i, i + 2 :] = True
 
     values = qml.math.zeros_like(matrix)
     values[..., i + 1, i] = alpha
     values[..., i, i + 1] = -alpha
-    values[..., i + 2:, i] = zero_like
-    values[..., i, i + 2:] = zero_like
+    values[..., i + 2 :, i] = zero_like
+    values[..., i, i + 2 :] = zero_like
 
     matrix = qml.math.where(where_mask, values, matrix)
     return matrix
@@ -227,22 +224,20 @@ def _update_matrix_block_householder(matrix, i, v, tau):
     :param tau:
     :return:
     """
-    w = tau * qml.math.einsum("...ij,...j->...i", matrix[..., i + 1:, i + 1:], qml.math.conjugate(v))
+    w = tau * qml.math.einsum("...ij,...j->...i", matrix[..., i + 1 :, i + 1 :], qml.math.conjugate(v))
     values = qml.math.zeros_like(matrix)
-    values[..., i + 1:, i + 1:] += (
-        qml.math.einsum("...i,...j->...ij", v, w)
-        -
-        qml.math.einsum("...i,...j->...ij", w, v)
+    values[..., i + 1 :, i + 1 :] += qml.math.einsum("...i,...j->...ij", v, w) - qml.math.einsum(
+        "...i,...j->...ij", w, v
     )
     return matrix + values
 
 
 def batch_pfaffian_householder(
-        __matrix: TensorLike,
-        overwrite_input: bool = False,
-        test_input: bool = False,
-        p_bar: Optional[tqdm.tqdm] = None,
-        show_progress: bool = False
+    __matrix: TensorLike,
+    overwrite_input: bool = False,
+    test_input: bool = False,
+    p_bar: Optional[tqdm.tqdm] = None,
+    show_progress: bool = False,
 ):
     """pfaffian(A, overwrite_a=False)
 
@@ -284,7 +279,7 @@ def batch_pfaffian_householder(
     p_bar.set_description(f"Computing Pfaffian of {shape} matrix")
     for i in p_bar:
         # Find a Householder vector to eliminate the i-th column
-        v, tau, alpha = batch_householder_complex(matrix[..., i + 1:, i])
+        v, tau, alpha = batch_householder_complex(matrix[..., i + 1 :, i])
         matrix = _eliminate_ith_column_householder(matrix, i, alpha)
         matrix = _update_matrix_block_householder(matrix, i, v, tau)
 
@@ -299,10 +294,7 @@ def batch_pfaffian_householder(
 
 
 def pfaffian_by_det(
-        __matrix: TensorLike,
-        p_bar: Optional[tqdm.tqdm] = None,
-        show_progress: bool = False,
-        epsilon: float = 1e-12
+    __matrix: TensorLike, p_bar: Optional[tqdm.tqdm] = None, show_progress: bool = False, epsilon: float = 1e-12
 ):
     shape = qml.math.shape(__matrix)
     p_bar = p_bar or tqdm.tqdm(total=1, disable=not show_progress)
@@ -323,12 +315,12 @@ def pfaffian_by_det(
 
 
 def pfaffian(
-        __matrix: TensorLike,
-        overwrite_input: bool = False,
-        method: Literal["P", "H", "det", "bLTL", "bH"] = "bLTL",
-        epsilon: float = 1e-12,
-        p_bar: Optional[tqdm.tqdm] = None,
-        show_progress: bool = False
+    __matrix: TensorLike,
+    overwrite_input: bool = False,
+    method: Literal["P", "H", "det", "bLTL", "bH"] = "bLTL",
+    epsilon: float = 1e-12,
+    p_bar: Optional[tqdm.tqdm] = None,
+    show_progress: bool = False,
 ) -> Union[float, complex, TensorLike]:
     """pfaffian(A, overwrite_a=False, method='P')
 
@@ -362,17 +354,17 @@ def pfaffian(
 
     if method == "P":
         from pfapack.pfaffian import pfaffian
+
         warnings.warn(
-            "The method 'P' is not implemented yet. "
-            "It is recommended to use the method 'det' instead.",
+            "The method 'P' is not implemented yet. It is recommended to use the method 'det' instead.",
             UserWarning,
         )
         return pfaffian(__matrix, overwrite_input, method="P")
     elif method == "H":
         from pfapack.pfaffian import pfaffian_householder
+
         warnings.warn(
-            "The method 'H' is not implemented yet. "
-            "It is recommended to use the method 'det' instead.",
+            "The method 'H' is not implemented yet. It is recommended to use the method 'det' instead.",
             UserWarning,
         )
         return pfaffian_householder(__matrix, overwrite_input)
diff --git a/archives/_test_pfaffian.py b/archives/_test_pfaffian.py
index 897dcc3..a320154 100644
--- a/archives/_test_pfaffian.py
+++ b/archives/_test_pfaffian.py
@@ -2,12 +2,12 @@
 import pytest
 
 from tests.configs import (
-    N_RANDOM_TESTS_PER_CASE,
-    TEST_SEED,
-    ATOL_SCALAR_COMPARISON,
-    RTOL_SCALAR_COMPARISON,
     ATOL_MATRIX_COMPARISON,
+    ATOL_SCALAR_COMPARISON,
+    N_RANDOM_TESTS_PER_CASE,
     RTOL_MATRIX_COMPARISON,
+    RTOL_SCALAR_COMPARISON,
+    TEST_SEED,
     set_seed,
 )
 from torch_pfaffian import utils
@@ -34,7 +34,7 @@ def gen_skew_symmetric_matrix_and_det(n, batch_size=None):
         gen_skew_symmetric_matrix_and_det(i, batch_size=None)
         for i in range(MIN_MATRIX_SIZE, MAX_MATRIX_SIZE + 1)
         for _ in range(N_RANDOM_TESTS_PER_CASE)
-    ]
+    ],
 )
 def test_get_skew_symmetric_matrix_and_det(matrix, det):
     try:
@@ -42,7 +42,7 @@ def test_get_skew_symmetric_matrix_and_det(matrix, det):
     except ImportError:
         pytest.skip("pfapack is not installed.")
     pf = pfaffian(matrix)
-    np.testing.assert_allclose(pf ** 2, det, atol=ATOL_SCALAR_COMPARISON, rtol=RTOL_SCALAR_COMPARISON)
+    np.testing.assert_allclose(pf**2, det, atol=ATOL_SCALAR_COMPARISON, rtol=RTOL_SCALAR_COMPARISON)
 
 
 @pytest.mark.parametrize(
@@ -53,11 +53,11 @@ def test_get_skew_symmetric_matrix_and_det(matrix, det):
         for _ in range(N_RANDOM_TESTS_PER_CASE)
         for mth in RECOMMENDED_METHODS
         for batch_size in [None, BATCH_SIZE]
-    ]
+    ],
 )
 def test_pfaffian_methods(matrix, det, mth):
     pf = utils.pfaffian(matrix, method=mth)
-    np.testing.assert_allclose(pf ** 2, det, atol=10 * ATOL_SCALAR_COMPARISON, rtol=10 * RTOL_SCALAR_COMPARISON)
+    np.testing.assert_allclose(pf**2, det, atol=10 * ATOL_SCALAR_COMPARISON, rtol=10 * RTOL_SCALAR_COMPARISON)
 
 
 @pytest.mark.parametrize(
@@ -66,11 +66,11 @@ def test_pfaffian_methods(matrix, det, mth):
         gen_skew_symmetric_matrix_and_det(i, batch_size=BATCH_SIZE)
         for i in range(MIN_MATRIX_SIZE, MAX_MATRIX_SIZE + 1)
         for _ in range(N_RANDOM_TESTS_PER_CASE)
-    ]
+    ],
 )
 def test_batch_pfaffian_ltl(matrix, det):
     pf = utils._pfaffian.batch_pfaffian_ltl(matrix)
-    np.testing.assert_allclose(pf ** 2, det, atol=ATOL_MATRIX_COMPARISON, rtol=RTOL_MATRIX_COMPARISON)
+    np.testing.assert_allclose(pf**2, det, atol=ATOL_MATRIX_COMPARISON, rtol=RTOL_MATRIX_COMPARISON)
 
 
 @pytest.mark.parametrize(
@@ -79,11 +79,11 @@ def test_batch_pfaffian_ltl(matrix, det):
         gen_skew_symmetric_matrix_and_det(i, batch_size=BATCH_SIZE)
         for i in range(MIN_MATRIX_SIZE, MAX_MATRIX_SIZE + 1)
         for _ in range(N_RANDOM_TESTS_PER_CASE)
-    ]
+    ],
 )
 def test_pfaffian_bltl(matrix, det):
     pf = utils.pfaffian(matrix, method="bLTL")
-    np.testing.assert_allclose(pf ** 2, det, atol=ATOL_MATRIX_COMPARISON, rtol=RTOL_MATRIX_COMPARISON)
+    np.testing.assert_allclose(pf**2, det, atol=ATOL_MATRIX_COMPARISON, rtol=RTOL_MATRIX_COMPARISON)
 
 
 @pytest.mark.parametrize(
@@ -93,11 +93,11 @@ def test_pfaffian_bltl(matrix, det):
         for batch_size in [None, 1]
         for i in range(MIN_MATRIX_SIZE, MAX_MATRIX_SIZE + 1)
         for _ in range(N_RANDOM_TESTS_PER_CASE)
-    ]
+    ],
 )
 def test_pfaffian_bltl_single_item(matrix, det):
     pf = utils.pfaffian(matrix, method="bLTL")
-    np.testing.assert_allclose(pf ** 2, det, atol=ATOL_MATRIX_COMPARISON, rtol=RTOL_MATRIX_COMPARISON)
+    np.testing.assert_allclose(pf**2, det, atol=ATOL_MATRIX_COMPARISON, rtol=RTOL_MATRIX_COMPARISON)
 
 
 @pytest.mark.parametrize(
@@ -108,7 +108,7 @@ def test_pfaffian_bltl_single_item(matrix, det):
         for _ in range(N_RANDOM_TESTS_PER_CASE)
         for mth in RECOMMENDED_METHODS
         for batch_size in [None, BATCH_SIZE]
-    ]
+    ],
 )
 def test_pfaffian_methods_grads(n, batch_size, mth):
     try:
@@ -130,7 +130,7 @@ def test_pfaffian_methods_grads(n, batch_size, mth):
 
     matrix = torch.from_numpy(np_matrix).requires_grad_()
     pf = utils.pfaffian(matrix, method=mth)
-    pred_det = torch.real(pf ** 2)
+    pred_det = torch.real(pf**2)
     with torch.no_grad():
         np.testing.assert_allclose(pred_det, det, atol=10 * ATOL_SCALAR_COMPARISON, rtol=10 * RTOL_SCALAR_COMPARISON)
 
@@ -140,9 +140,7 @@ def test_pfaffian_methods_grads(n, batch_size, mth):
     assert pred_grad is not None
     if mth == "det":
         np.testing.assert_allclose(
-            torch.abs(pred_grad), torch.abs(true_grad),
-            atol=ATOL_MATRIX_COMPARISON,
-            rtol=RTOL_MATRIX_COMPARISON
+            torch.abs(pred_grad), torch.abs(true_grad), atol=ATOL_MATRIX_COMPARISON, rtol=RTOL_MATRIX_COMPARISON
         )
 
 
@@ -152,11 +150,11 @@ def test_pfaffian_methods_grads(n, batch_size, mth):
         gen_skew_symmetric_matrix_and_det(i, batch_size=BATCH_SIZE)
         for i in range(MIN_MATRIX_SIZE, MAX_MATRIX_SIZE + 1)
         for _ in range(N_RANDOM_TESTS_PER_CASE)
-    ]
+    ],
 )
 def test_pfaffian_bh(matrix, det):
     pf = utils.pfaffian(matrix, method="bH")
-    np.testing.assert_allclose(pf ** 2, det, atol=ATOL_MATRIX_COMPARISON, rtol=RTOL_MATRIX_COMPARISON)
+    np.testing.assert_allclose(pf**2, det, atol=ATOL_MATRIX_COMPARISON, rtol=RTOL_MATRIX_COMPARISON)
 
 
 @pytest.mark.parametrize(
@@ -166,8 +164,8 @@ def test_pfaffian_bh(matrix, det):
         for batch_size in [None, 1]
         for i in range(MIN_MATRIX_SIZE, MAX_MATRIX_SIZE + 1)
         for _ in range(N_RANDOM_TESTS_PER_CASE)
-    ]
+    ],
 )
 def test_pfaffian_bltl_single_item(matrix, det):
     pf = utils.pfaffian(matrix, method="bH")
-    np.testing.assert_allclose(pf ** 2, det, atol=ATOL_MATRIX_COMPARISON, rtol=RTOL_MATRIX_COMPARISON)
+    np.testing.assert_allclose(pf**2, det, atol=ATOL_MATRIX_COMPARISON, rtol=RTOL_MATRIX_COMPARISON)
diff --git a/archives/pfaffian_perm.py b/archives/pfaffian_perm.py
index 90859f0..fd4ce3c 100644
--- a/archives/pfaffian_perm.py
+++ b/archives/pfaffian_perm.py
@@ -1,7 +1,6 @@
-import itertools
-from typing import Any
-import torch
 import numpy as np
+import torch
+
 from .strategy import PfaffianStrategy
 
 
@@ -10,6 +9,7 @@ class PfaffianPerm(PfaffianStrategy):
     This class implements the Pfaffian using the determinant of the matrix for the forward pass and the
     derivative of the Pfaffian with respect to the input matrix for the backward pass.
     """
+
     NAME = "PfaffianPerm"
 
     @staticmethod
@@ -34,7 +34,7 @@ def forward(matrix: torch.Tensor):
                 for i in range(_i_matrix.shape[0])
                 for j in range(i, _j_matrix.shape[0])
             ],
-            axis=0
+            axis=0,
         )
 
         pf_matrix = torch.prod(matrix[..., ij_matrix[..., 0], ij_matrix[..., 1]], dim=-1)
@@ -57,7 +57,5 @@ def backward(ctx: torch.autograd.function.FunctionCtx, grad_output):
         matrix, pf = ctx.saved_tensors
         grad_matrix = None
         if ctx.needs_input_grad[0]:
-            grad_matrix = torch.einsum('...,...ij->...ji', 0.5 * grad_output * pf, torch.linalg.pinv(matrix))
+            grad_matrix = torch.einsum("...,...ij->...ji", 0.5 * grad_output * pf, torch.linalg.pinv(matrix))
         return grad_matrix
-
-
diff --git a/notebooks/_strategies_benchmark_helpers.py b/notebooks/_strategies_benchmark_helpers.py
new file mode 100644
index 0000000..e7557d0
--- /dev/null
+++ b/notebooks/_strategies_benchmark_helpers.py
@@ -0,0 +1,185 @@
+import time
+from collections.abc import Callable
+
+import matplotlib.pyplot as plt
+import numpy as np
+import torch
+from torch.profiler import ProfilerActivity, profile
+
+from torch_pfaffian.strategies.strategy import PfaffianStrategy
+
+Strategy = type[PfaffianStrategy]
+BenchmarkResults = dict[str, dict[str, list[float]]]
+
+METRICS = ("forward_time", "backward_time", "forward_memory", "forward_backward_memory")
+
+
+def make_block_antidiagonal(
+    n: int, batch_size: int, dtype: torch.dtype = torch.float64, device: str = "cpu"
+) -> torch.Tensor:
+    """
+    Build a batch of block-antidiagonal skew matrices ``[[0, B], [-B^T, 0]]``.
+
+    :param n: Half the matrix dimension; the result has shape ``(batch_size, 2n, 2n)``.
+    :param batch_size: Number of matrices in the batch.
+    :param dtype: Floating dtype of the matrices.
+    :param device: Device on which to allocate the matrices.
+    :return: A tensor of shape ``(batch_size, 2n, 2n)``.
+    :rtype: torch.Tensor
+    """
+    block = torch.randn(batch_size, n, n, dtype=dtype, device=device)
+    zero = torch.zeros_like(block)
+    top = torch.cat([zero, block], dim=-1)
+    bottom = torch.cat([-block.transpose(-1, -2), zero], dim=-1)
+    return torch.cat([top, bottom], dim=-2)
+
+
+def _synchronize(is_cuda: bool) -> None:
+    if is_cuda:
+        torch.cuda.synchronize()
+
+
+def _median_time(fn: Callable[[], object], is_cuda: bool, n_repeats: int = 3) -> float:
+    fn()  # warm-up
+    _synchronize(is_cuda)
+    timings = []
+    for _ in range(n_repeats):
+        _synchronize(is_cuda)
+        start = time.perf_counter()
+        fn()
+        _synchronize(is_cuda)
+        timings.append(time.perf_counter() - start)
+    return float(np.median(timings))
+
+
+def _peak_memory(fn: Callable[[], object], is_cuda: bool) -> float:
+    # On CUDA this is the true peak; on CPU it is the profiler-reported allocated bytes.
+    if is_cuda:
+        torch.cuda.reset_peak_memory_stats()
+        fn()
+        torch.cuda.synchronize()
+        return float(torch.cuda.max_memory_allocated())
+    with profile(activities=[ProfilerActivity.CPU], profile_memory=True) as prof:
+        fn()
+    return float(sum(e.cpu_memory_usage for e in prof.key_averages() if e.cpu_memory_usage > 0))
+
+
+def _forward_call(strategy: Strategy, matrix: torch.Tensor) -> torch.Tensor:
+    return strategy.apply(matrix)
+
+
+def _forward_backward_call(strategy: Strategy, matrix: torch.Tensor) -> torch.Tensor:
+    grad_matrix = matrix.detach().clone().requires_grad_(True)
+    strategy.apply(grad_matrix).sum().backward()
+    return grad_matrix
+
+
+def _confidence_interval(values: np.ndarray, confidence_z: float = 1.96) -> float:
+    # Half-width of the normal-approximation confidence interval for the mean.
+    if values.size < 2:
+        return 0.0
+    return float(confidence_z * values.std(ddof=1) / np.sqrt(values.size))
+
+
+def benchmark_strategies(
+    strategies: list[Strategy],
+    sizes_n: list[int],
+    batch_size: int,
+    device: str = "cpu",
+    n_seeds: int = 20,
+    n_repeats: int = 3,
+) -> BenchmarkResults:
+    """
+    Benchmark forward/backward time and memory of each strategy over several random seeds.
+
+    For each matrix dimension and strategy the metric is measured once per seed; the returned
+    values are the across-seed mean and the half-width of the 95% confidence interval.
+
+    :param strategies: The strategy classes to benchmark (each exposes ``NAME`` and ``apply``).
+    :param sizes_n: Half-dimensions to sweep; each yields matrices of dimension ``2n``.
+    :param batch_size: Number of matrices per benchmarked batch.
+    :param device: Device on which to run the benchmark.
+    :param n_seeds: Number of random seeds used to build the confidence intervals.
+    :param n_repeats: Number of timed repeats per seed (the median is kept).
+    :return: A mapping ``strategy_name -> {"dimension", "<metric>_mean", "<metric>_ci"}``.
+    :rtype: dict
+    """
+    is_cuda = device == "cuda"
+    results: BenchmarkResults = {
+        strategy.NAME: {
+            "dimension": [],
+            **{f"{metric}_mean": [] for metric in METRICS},
+            **{f"{metric}_ci": [] for metric in METRICS},
+        }
+        for strategy in strategies
+    }
+
+    for n in sizes_n:
+        per_seed: dict[str, dict[str, list[float]]] = {
+            strategy.NAME: {metric: [] for metric in METRICS} for strategy in strategies
+        }
+        for seed in range(n_seeds):
+            torch.manual_seed(seed)
+            matrix = make_block_antidiagonal(n, batch_size, dtype=torch.float64, device=device)
+            for strategy in strategies:
+                forward_time = _median_time(lambda: _forward_call(strategy, matrix), is_cuda, n_repeats)
+                full_time = _median_time(lambda: _forward_backward_call(strategy, matrix), is_cuda, n_repeats)
+                record = per_seed[strategy.NAME]
+                record["forward_time"].append(forward_time)
+                record["backward_time"].append(max(full_time - forward_time, 0.0))
+                record["forward_memory"].append(_peak_memory(lambda: _forward_call(strategy, matrix), is_cuda))
+                record["forward_backward_memory"].append(
+                    _peak_memory(lambda: _forward_backward_call(strategy, matrix), is_cuda)
+                )
+
+        for strategy in strategies:
+            results[strategy.NAME]["dimension"].append(2 * n)
+            for metric in METRICS:
+                values = np.asarray(per_seed[strategy.NAME][metric], dtype=float)
+                results[strategy.NAME][f"{metric}_mean"].append(float(values.mean()))
+                results[strategy.NAME][f"{metric}_ci"].append(_confidence_interval(values))
+    return results
+
+
+def plot_results(
+    results: BenchmarkResults, strategies: list[Strategy], batch_size: int, device: str = "cpu"
+) -> plt.Figure:
+    """
+    Plot the benchmark results as a 2x2 grid of time and memory panels with 95% CI bands.
+
+    :param results: The mapping returned by :func:`benchmark_strategies`.
+    :param strategies: The strategy classes that were benchmarked.
+    :param batch_size: Batch size used for the benchmark (shown in the title).
+    :param device: Device used for the benchmark (shown in the title and memory label).
+    :return: The matplotlib figure.
+    :rtype: matplotlib.figure.Figure
+    """
+    memory_label = "peak memory (bytes, CUDA)" if device == "cuda" else "allocated bytes (CPU)"
+    panels = [
+        ("forward_time", "Forward time", "time [s]"),
+        ("backward_time", "Backward time", "time [s]"),
+        ("forward_memory", "Forward memory", memory_label),
+        ("forward_backward_memory", "Forward + backward memory", memory_label),
+    ]
+
+    fig, axes = plt.subplots(2, 2, figsize=(12, 9))
+    for ax, (key, title, ylabel) in zip(axes.flat, panels):
+        for strategy in strategies:
+            record = results[strategy.NAME]
+            dimension = np.asarray(record["dimension"], dtype=float)
+            mean = np.asarray(record[f"{key}_mean"], dtype=float)
+            ci = np.asarray(record[f"{key}_ci"], dtype=float)
+            line = ax.plot(dimension, mean, marker="o", label=strategy.NAME)[0]
+            lower = np.clip(mean - ci, np.finfo(float).tiny, None)
+            ax.fill_between(dimension, lower, mean + ci, alpha=0.2, color=line.get_color())
+        ax.set_title(title)
+        ax.set_xlabel("matrix dimension (2n)")
+        ax.set_ylabel(ylabel)
+        ax.set_xscale("log", base=2)
+        ax.set_yscale("log")
+        ax.grid(True, which="both", linestyle=":", alpha=0.5)
+        ax.legend()
+
+    fig.suptitle(f"Pfaffian strategies benchmark (batch_size={batch_size}, device={device}, 95% CI over seeds)")
+    fig.tight_layout()
+    return fig
diff --git a/notebooks/strategies_benchmark.ipynb b/notebooks/strategies_benchmark.ipynb
new file mode 100644
index 0000000..3742965
--- /dev/null
+++ b/notebooks/strategies_benchmark.ipynb
@@ -0,0 +1,326 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "56c973ab",
+   "metadata": {},
+   "source": [
+    "# Benchmarking the Pfaffian strategies\n",
+    "\n",
+    "This notebook benchmarks the available Pfaffian strategies in terms of **time** and **memory** for both the **forward** and **backward** passes.\n",
+    "\n",
+    "Three strategies are compared:\n",
+    "\n",
+    "- `PfaffianFDBPf`: computes the Pfaffian from `sqrt(|det(A)|)` with a custom **analytic** backward. Valid for any skew-symmetric matrix.\n",
+    "- `PfaffianBlockDet`: computes the Pfaffian from the determinant of the upper-right block, with a custom analytic backward.\n",
+    "- `PfaffianDet`: a reference baseline that also computes `pf = sqrt(|det(A)|)` but differentiates straight through `det`/`sqrt` with **plain autograd** (no custom backward), to expose the cost of naive autodiff against the analytic backward of `PfaffianFDBPf`.\n",
+    "\n",
+    "Each strategy is invoked through `Strategy.apply(matrix)`.\n",
+    "\n",
+    "For each strategy and matrix size we report, **averaged over 20 random seeds with 95% confidence intervals**:\n",
+    "\n",
+    "- the median forward time,\n",
+    "- the median backward time,\n",
+    "- the forward peak memory,\n",
+    "- the forward + backward peak memory."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "55b95a09",
+   "metadata": {},
+   "source": [
+    "## Validity note\n",
+    "\n",
+    "`PfaffianBlockDet`'s forward only returns the *true* Pfaffian for **block-antidiagonal** skew matrices of the form\n",
+    "\n",
+    "$$ A = \\begin{pmatrix} 0 & B \\\\ -B^{T} & 0 \\end{pmatrix}. $$\n",
+    "\n",
+    "To keep the comparison both **fair** and **numerically valid**, we benchmark *all* strategies on block-antidiagonal inputs. `PfaffianFDBPf` and `PfaffianDet` are valid on any skew matrix (including this form), so this input shape is a common ground on which all strategies compute the same quantity."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "2305932f",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-06-10T14:25:10.025490742Z",
+     "start_time": "2026-06-10T14:25:08.874486863Z"
+    },
+    "execution": {
+     "iopub.execute_input": "2026-06-10T14:16:52.667639Z",
+     "iopub.status.busy": "2026-06-10T14:16:52.667294Z",
+     "iopub.status.idle": "2026-06-10T14:16:54.207116Z",
+     "shell.execute_reply": "2026-06-10T14:16:54.205473Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Running benchmarks on device: cpu\n"
+     ]
+    }
+   ],
+   "source": [
+    "import os\n",
+    "import sys\n",
+    "\n",
+    "# The helper module lives next to this notebook; ensure it is importable both when\n",
+    "# running the notebook directly and when it is executed during the Sphinx docs build.\n",
+    "sys.path.insert(0, os.getcwd())\n",
+    "\n",
+    "import torch\n",
+    "from _strategies_benchmark_helpers import benchmark_strategies, plot_results\n",
+    "\n",
+    "from torch_pfaffian.strategies.pfaffian_block_det import PfaffianBlockDet\n",
+    "from torch_pfaffian.strategies.pfaffian_det import PfaffianDet\n",
+    "from torch_pfaffian.strategies.pfaffian_fdbpf import PfaffianFDBPf\n",
+    "\n",
+    "strategies = [PfaffianFDBPf, PfaffianBlockDet, PfaffianDet]\n",
+    "\n",
+    "device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n",
+    "print(f\"Running benchmarks on device: {device}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8838f08f",
+   "metadata": {},
+   "source": [
+    "## Running the benchmark\n",
+    "\n",
+    "The benchmarking and plotting logic lives in `_strategies_benchmark_helpers.py` to keep this notebook focused on the narrative. `benchmark_strategies` sweeps the matrix dimension `2n` and, for every strategy, measures each metric once per seed across `n_seeds` seeds, returning the across-seed mean and the half-width of the 95% confidence interval."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "b6a3c6b4",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-06-10T14:25:22.671153790Z",
+     "start_time": "2026-06-10T14:25:10.033292322Z"
+    },
+    "execution": {
+     "iopub.execute_input": "2026-06-10T14:16:54.210625Z",
+     "iopub.status.busy": "2026-06-10T14:16:54.210309Z",
+     "iopub.status.idle": "2026-06-10T14:17:21.107161Z",
+     "shell.execute_reply": "2026-06-10T14:17:21.105779Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/local/USHERBROOKE/ginj2102/github/TorchPfaffian/.venv/lib/python3.14/site-packages/torch/profiler/profiler.py:217: UserWarning: Warning: Profiler clears events at the end of each cycle.Only events from the current cycle will be reported.To keep events across cycles, set acc_events=True.\n",
+      "  _warn_once(\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "{'PfaffianFDBPf': {'dimension': [4, 8, 16, 32, 64],\n",
+       "  'forward_time_mean': [8.39531006931793e-05,\n",
+       "   8.420960039074998e-05,\n",
+       "   0.0048694635002902945,\n",
+       "   0.0038942812007007888,\n",
+       "   0.005135932300981949],\n",
+       "  'backward_time_mean': [0.00023082459956640377,\n",
+       "   0.005614171149136382,\n",
+       "   0.0037422726509248605,\n",
+       "   0.0035597407983004813,\n",
+       "   0.007134951999250916],\n",
+       "  'forward_memory_mean': [5578.0, 13622.0, 42042.4, 147885.2, 556360.8],\n",
+       "  'forward_backward_memory_mean': [51952.75,\n",
+       "   191664.4,\n",
+       "   741525.9,\n",
+       "   2922178.35,\n",
+       "   11609078.25],\n",
+       "  'forward_time_ci': [9.135929690982144e-06,\n",
+       "   3.2582327167016847e-06,\n",
+       "   0.0009277367240195104,\n",
+       "   0.0012259096343144602,\n",
+       "   0.0024523398701700883],\n",
+       "  'backward_time_ci': [1.66414058654275e-05,\n",
+       "   0.0011459601539736321,\n",
+       "   0.0012184828913370757,\n",
+       "   0.0010552649404508374,\n",
+       "   0.002653286676583563],\n",
+       "  'forward_memory_ci': [29.3318367720307,\n",
+       "   24.815081322708405,\n",
+       "   102.10139647068085,\n",
+       "   30.656304800154892,\n",
+       "   40.23282740303128],\n",
+       "  'forward_backward_memory_ci': [4.743538874367067,\n",
+       "   21.21989739545609,\n",
+       "   162.69658520403138,\n",
+       "   34.345881944220274,\n",
+       "   52.56864599840158]},\n",
+       " 'PfaffianBlockDet': {'dimension': [4, 8, 16, 32, 64],\n",
+       "  'forward_time_mean': [7.726129915681667e-05,\n",
+       "   7.922490076452959e-05,\n",
+       "   8.57123999594478e-05,\n",
+       "   0.0034056200998747953,\n",
+       "   0.0026923404508124803],\n",
+       "  'backward_time_mean': [0.00013435915170703083,\n",
+       "   0.0001344478991086362,\n",
+       "   0.00016380800007027575,\n",
+       "   0.0020619140501366927,\n",
+       "   0.00030570419985451733],\n",
+       "  'forward_memory_mean': [2748.0, 5211.6, 13258.0, 41630.8, 147519.2],\n",
+       "  'forward_backward_memory_mean': [19599.05,\n",
+       "   71313.3,\n",
+       "   274652.7,\n",
+       "   1091927.6,\n",
+       "   4345071.7],\n",
+       "  'forward_time_ci': [3.169456596676188e-06,\n",
+       "   2.4604736536371967e-06,\n",
+       "   2.3125298299448165e-06,\n",
+       "   0.0013217441709651864,\n",
+       "   0.0012497851096257763],\n",
+       "  'backward_time_ci': [3.1405466727071304e-06,\n",
+       "   4.261377559919364e-06,\n",
+       "   5.229660731091481e-06,\n",
+       "   0.0011858865161943775,\n",
+       "   0.00018241185806708765],\n",
+       "  'forward_memory_ci': [25.777422026179995,\n",
+       "   22.071778484609798,\n",
+       "   22.287644535647285,\n",
+       "   25.872,\n",
+       "   30.415286129112605],\n",
+       "  'forward_backward_memory_ci': [84.88650624773861,\n",
+       "   66.22060255651084,\n",
+       "   3213.0211251070227,\n",
+       "   29.615975830977654,\n",
+       "   39.7277365762723]},\n",
+       " 'PfaffianDet': {'dimension': [4, 8, 16, 32, 64],\n",
+       "  'forward_time_mean': [2.814660001604352e-05,\n",
+       "   3.418759915803093e-05,\n",
+       "   0.004134788549345103,\n",
+       "   0.002652941150518018,\n",
+       "   0.00029987425114086363],\n",
+       "  'backward_time_mean': [0.0001211243503348669,\n",
+       "   0.0001411523018759908,\n",
+       "   5.7608549832366406e-05,\n",
+       "   0.00020152409961156082,\n",
+       "   0.0013275362496642628],\n",
+       "  'forward_memory_mean': [5464.4, 13483.2, 41928.4, 147770.4, 556153.2],\n",
+       "  'forward_backward_memory_mean': [39437.6,\n",
+       "   143664.2,\n",
+       "   554873.8,\n",
+       "   2188320.6,\n",
+       "   8699278.2],\n",
+       "  'forward_time_ci': [1.2732541170077155e-06,\n",
+       "   1.207109827186109e-06,\n",
+       "   0.0013741837643709278,\n",
+       "   0.0012392837411897722,\n",
+       "   0.00019687483453382923],\n",
+       "  'backward_time_ci': [2.5277217525177566e-06,\n",
+       "   5.454397998524737e-06,\n",
+       "   3.565945106097614e-05,\n",
+       "   0.00010193383971478204,\n",
+       "   0.00023040019338968858],\n",
+       "  'forward_memory_ci': [45.91907264788897,\n",
+       "   16.69521850237682,\n",
+       "   96.97527751795408,\n",
+       "   35.6852606931327,\n",
+       "   52.39233383857443],\n",
+       "  'forward_backward_memory_ci': [34.74130008778419,\n",
+       "   29.6061432600878,\n",
+       "   34.246935795314904,\n",
+       "   22.767103951003282,\n",
+       "   46.1319567830325]}}"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sizes_n = [2, 4, 8, 16, 32]\n",
+    "batch_size = 16\n",
+    "n_seeds = 20\n",
+    "\n",
+    "results = benchmark_strategies(strategies, sizes_n, batch_size, device=device, n_seeds=n_seeds)\n",
+    "results"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0ad995ad",
+   "metadata": {},
+   "source": [
+    "## Results"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "e414240b2fc6064e",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-06-10T14:25:48.810201185Z",
+     "start_time": "2026-06-10T14:25:47.642776418Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x900 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig = plot_results(results, strategies, batch_size, device=device)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8e444db9",
+   "metadata": {},
+   "source": [
+    "## Conclusions and caveats\n",
+    "\n",
+    "The figure above reports, as a function of the matrix dimension $2n$, the median forward time, the median backward time, the forward peak memory, and the combined forward + backward peak memory for every strategy, evaluated on block-antidiagonal skew matrices. Shaded bands are 95% confidence intervals over the 20 seeds (they are essentially invisible for memory, which is seed-independent for a fixed shape).\n",
+    "\n",
+    "**Memory-metric caveat.** The memory numbers depend on the device:\n",
+    "\n",
+    "- On **CUDA**, the reported value is a true peak obtained from `torch.cuda.reset_peak_memory_stats()` / `torch.cuda.max_memory_allocated()`.\n",
+    "- On **CPU** (the continuous-integration case), no equivalent peak counter exists, so we report the **allocated bytes** measured by `torch.profiler` instead. This is an aggregate of allocations rather than a strict instantaneous peak, so CPU and CUDA memory values are not directly comparable and the absolute CPU numbers should be read as a relative indicator across strategies and sizes.\n",
+    "\n",
+    "`PfaffianFDBPf` and `PfaffianDet` share the same forward (`sqrt(|det(A)|)`); their difference is the backward, so the backward panels highlight the analytic custom backward against plain autograd."
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.14.0"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/poetry.lock b/poetry.lock
deleted file mode 100644
index a593822..0000000
--- a/poetry.lock
+++ /dev/null
@@ -1,563 +0,0 @@
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-
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-dev = ["pre-commit", "ruff"]
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-dropbox = ["dropbox", "dropboxdrivefs", "requests"]
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-test-full = ["adlfs", "aiohttp (!=4.0.0a0,!=4.0.0a1)", "cloudpickle", "dask", "distributed", "dropbox", "dropboxdrivefs", "fastparquet", "fusepy", "gcsfs", "jinja2", "kerchunk", "libarchive-c", "lz4", "notebook", "numpy", "ocifs", "pandas", "panel", "paramiko", "pyarrow", "pyarrow (>=1)", "pyftpdlib", "pygit2", "pytest", "pytest-asyncio (!=0.22.0)", "pytest-benchmark", "pytest-cov", "pytest-mock", "pytest-recording", "pytest-rerunfailures", "python-snappy", "requests", "smbprotocol", "tqdm", "urllib3", "zarr", "zstandard"]
-tqdm = ["tqdm"]
-
-[[package]]
-name = "jinja2"
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-    {file = "torch-2.5.1-cp39-cp39-win_amd64.whl", hash = "sha256:46c817d3ea33696ad3b9df5e774dba2257e9a4cd3c4a3afbf92f6bb13ac5ce2d"},
-    {file = "torch-2.5.1-cp39-none-macosx_11_0_arm64.whl", hash = "sha256:8046768b7f6d35b85d101b4b38cba8aa2f3cd51952bc4c06a49580f2ce682291"},
-]
-
-[package.dependencies]
-filelock = "*"
-fsspec = "*"
-jinja2 = "*"
-networkx = "*"
-nvidia-cublas-cu12 = {version = "12.4.5.8", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""}
-nvidia-cuda-cupti-cu12 = {version = "12.4.127", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""}
-nvidia-cuda-nvrtc-cu12 = {version = "12.4.127", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""}
-nvidia-cuda-runtime-cu12 = {version = "12.4.127", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""}
-nvidia-cudnn-cu12 = {version = "9.1.0.70", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""}
-nvidia-cufft-cu12 = {version = "11.2.1.3", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""}
-nvidia-curand-cu12 = {version = "10.3.5.147", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""}
-nvidia-cusolver-cu12 = {version = "11.6.1.9", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""}
-nvidia-cusparse-cu12 = {version = "12.3.1.170", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""}
-nvidia-nccl-cu12 = {version = "2.21.5", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""}
-nvidia-nvjitlink-cu12 = {version = "12.4.127", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""}
-nvidia-nvtx-cu12 = {version = "12.4.127", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""}
-setuptools = {version = "*", markers = "python_version >= \"3.12\""}
-sympy = [
-    {version = "1.12.1", markers = "python_version == \"3.8\""},
-    {version = "1.13.1", markers = "python_version >= \"3.9\""},
-]
-triton = {version = "3.1.0", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\" and python_version < \"3.13\""}
-typing-extensions = ">=4.8.0"
-
-[package.extras]
-opt-einsum = ["opt-einsum (>=3.3)"]
-optree = ["optree (>=0.12.0)"]
-
-[[package]]
-name = "triton"
-version = "3.1.0"
-description = "A language and compiler for custom Deep Learning operations"
-optional = false
-python-versions = "*"
-groups = ["main"]
-markers = "python_version == \"3.8\" and platform_system == \"Linux\" and platform_machine == \"x86_64\" or python_version >= \"3.9\" and platform_system == \"Linux\" and platform_machine == \"x86_64\" and python_version < \"3.13\""
-files = [
-    {file = "triton-3.1.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:6b0dd10a925263abbe9fa37dcde67a5e9b2383fc269fdf59f5657cac38c5d1d8"},
-    {file = "triton-3.1.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:0f34f6e7885d1bf0eaaf7ba875a5f0ce6f3c13ba98f9503651c1e6dc6757ed5c"},
-    {file = "triton-3.1.0-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:c8182f42fd8080a7d39d666814fa36c5e30cc00ea7eeeb1a2983dbb4c99a0fdc"},
-    {file = "triton-3.1.0-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:6dadaca7fc24de34e180271b5cf864c16755702e9f63a16f62df714a8099126a"},
-    {file = "triton-3.1.0-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:aafa9a20cd0d9fee523cd4504aa7131807a864cd77dcf6efe7e981f18b8c6c11"},
-]
-
-[package.dependencies]
-filelock = "*"
-
-[package.extras]
-build = ["cmake (>=3.20)", "lit"]
-tests = ["autopep8", "flake8", "isort", "llnl-hatchet", "numpy", "pytest", "scipy (>=1.7.1)"]
-tutorials = ["matplotlib", "pandas", "tabulate"]
-
-[[package]]
-name = "typing-extensions"
-version = "4.12.2"
-description = "Backported and Experimental Type Hints for Python 3.8+"
-optional = false
-python-versions = ">=3.8"
-groups = ["main"]
-markers = "python_version == \"3.8\" or python_version >= \"3.9\""
-files = [
-    {file = "typing_extensions-4.12.2-py3-none-any.whl", hash = "sha256:04e5ca0351e0f3f85c6853954072df659d0d13fac324d0072316b67d7794700d"},
-    {file = "typing_extensions-4.12.2.tar.gz", hash = "sha256:1a7ead55c7e559dd4dee8856e3a88b41225abfe1ce8df57b7c13915fe121ffb8"},
-]
-
-[metadata]
-lock-version = "2.1"
-python-versions = ">=3.8"
-content-hash = "86cbdcc9942ce0fe264ccaf656b9a1faaf82a1281d609dd55483eb95e5db46ae"
diff --git a/pyproject.toml b/pyproject.toml
index 5593271..c789a1b 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -1,60 +1,179 @@
-[tool.pytest.ini_options]
-pythonpath = [
-  ".", "src",
-]
-addopts = [
-    "--cov=src",
-#    "--no-cov",
-    "--durations=10",
-]
-
-
-[build-system]
-requires = [
-    "numpy>=1.23.0",
-    "setuptools>=57.0.0",
-    "pytest>=7.4.2",
-    "pytest-cov>=4.1.0",
-    "wheel",
-    "torch",
-]
-build-backend = "setuptools.build_meta"
-
-
-
 [project]
 name = "TorchPfaffian"
-dynamic = ["version", "readme"]
-description = ""
-authors = [
-  { name="Jérémie Gince", email="gincejeremie@gmail.com" },
-]
+version = "0.0.1"
+description = "PyTorch-based Pfaffian computation for skew-symmetric matrices."
+authors = [{ name = "Jérémie Gince", email = "gincejeremie@gmail.com" }]
+requires-python = ">=3.10,<3.15"
+license = { file = "LICENSE" }
 classifiers = [
     "Development Status :: 3 - Alpha",
     "Intended Audience :: Science/Research",
     "License :: OSI Approved :: Apache Software License",
     "Programming Language :: Python :: 3",
     "Programming Language :: Python :: 3.10",
+    "Programming Language :: Python :: 3.11",
     "Topic :: Scientific/Engineering",
+    "Topic :: Scientific/Engineering :: Artificial Intelligence",
     "Topic :: Software Development :: Libraries :: Python Modules",
     "Operating System :: OS Independent",
 ]
-requires-python = ">=3.8"
 dependencies = [
-    "numpy>=1.23.0",
+    "numpy (>=1.23,<3.0.0)",
+    "setuptools (>=80.10)",
+    "torch (>=2.1,<3.0.0)",
+]
+dynamic = ["readme"]
+
+[project.urls]
+homepage = "https://github.com/MatchCake/TorchPfaffian"
+Documentation = "https://MatchCake.github.io/TorchPfaffian"
+Source = "https://github.com/MatchCake/TorchPfaffian"
+
+[tool.pytest.ini_options]
+pythonpath = [
+    ".", "src",
+]
+testpaths = ["tests"]
+addopts = [
+    "-n=auto",
+    "--cov-branch",
+    "--cov=src",
+    '--cov-report=xml:tests/.tmp/coverage.xml',
+    "--cov-report=term-missing",
+    "--durations=10",
+]
+
+[dependency-groups]
+dev = [
+    "pytest-cov>=6.1,<7",
+    "pytest-json-report>=1.5,<2",
+    "pytest>=8.4,<9",
+    "mypy>=1.15,<2",
+    "ruff>=0.9,<1",
+    "nbmake>=1.5,<2",
+    "wheel>=0.45,<0.46",
+    "build>=1.4,<2",
+    "twine>=6.1,<7",
+    "pytest-xdist>=3.7,<4",
+    "pytest-timeout>=2.4",
+    "pip>=25",
+    "pre-commit>=4.5.1",
+]
+docs = [
+    "wheel>=0.45,<0.46",
+    "build>=1.4,<2",
+    "twine>=6.1,<7",
+    "sphinx>=6.2,<6.3",
+    "docutils>=0.18,<0.20",
+    "sphinx-rtd-theme>=3.0,<4",
+    "sphinxcontrib-bibtex>=2.5,<2.6",
+    "sphinx-theme~=1.0",
+    "groundwork-sphinx-theme>=1.1,<2",
+    "karma-sphinx-theme>=0.0.8,<0.0.9",
+    "sphinx-mdinclude>=0.6,<0.7",
+    "myst-nb>=1.1,<2",
+    "ipykernel>=6.29,<7",
+    "matplotlib>=3.8,<4",
+    "nbconvert>=7.16,<8",
+]
+
+[tool.uv]
+default-groups = [
+    "dev",
+    "docs",
+]
+conflicts = [
+    [
+        { extra = "cpu" },
+        { extra = "cu128" },
+        { extra = "cu130" },
+    ],
+]
+
+[project.optional-dependencies]
+cpu = [
+    "torch (>=2.1.2,<3.0.0)",
+]
+cu128 = [
+    "torch (>=2.1.2,<3.0.0)",
+]
+cu130 = [
+    "torch (>=2.1.2,<3.0.0)",
+]
+
+[tool.uv.sources]
+torch = [
+    { index = "pytorch-cpu", extra = "cpu" },
+    { index = "pytorch-cu128", extra = "cu128" },
+    { index = "pytorch-cu130", extra = "cu130" },
+]
+
+[[tool.uv.index]]
+name = "pytorch-cpu"
+url = "https://download.pytorch.org/whl/cpu"
+explicit = true
+
+[[tool.uv.index]]
+name = "pytorch-cu128"
+url = "https://download.pytorch.org/whl/cu128"
+explicit = true
+
+[[tool.uv.index]]
+name = "pytorch-cu130"
+url = "https://download.pytorch.org/whl/cu130"
+explicit = true
+
+[build-system]
+requires = [
     "setuptools>=57.0.0",
-    "torch",
-    "numpy"
+    "wheel>=0.45.1,<0.46",
+    "build>=1.2.2.post1,<2",
+    "twine>=6.1.0,<7",
 ]
+build-backend = "setuptools.build_meta"
 
+[tool.setuptools.dynamic]
+readme = { file = "README.md", content-type = "text/markdown" }
 
 [tool.setuptools]
-package-dir = {"" = "src"}
+package-dir = { "" = "src" }
 
 [tool.setuptools.packages.find]
 where = ["src"]
 
+[tool.setuptools.package-data]
+torch_pfaffian = ["py.typed"]
 
-[tool.poetry]
-packages = [{include = "torch_pfaffian", from = "src"}]
-version = "0.0.1"
+[tool.coverage.report]
+exclude_also = [
+    'def __repr__',
+    'if self.debug:',
+    'if settings.DEBUG',
+    'raise AssertionError',
+    'raise NotImplementedError',
+    'if __name__ == .__main__.:',
+    'if TYPE_CHECKING:',
+    '@(abc\.)?abstractmethod',
+]
+
+[tool.coverage.run]
+relative_files = true
+
+[[tool.mypy.overrides]]
+module = []
+ignore_missing_imports = true
+
+[tool.ruff]
+line-length = 120
+extend-exclude = ["archives"]
+force-exclude = true
+
+[tool.ruff.lint]
+select = ["E", "F", "I"]
+
+[tool.ruff.lint.per-file-ignores]
+"**/__init__.py" = ["F401", "F403", "F405"]
+"sphinx/source/conf.py" = ["E402"]
+
+[tool.ruff.format]
+quote-style = "double"
diff --git a/requirements.txt b/requirements.txt
index 234cad5..7141f32 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,9 +1,462 @@
-pytest>=7.4.2
-pytest_cov>=4.1.0
-pytest_json_report>=1.5.0
-pythonbasictools>=0.0.1a10
-setuptools>=65.5.1
-tqdm>=4.66.1
-psutil>=5.9.6
-numpy>=1.23.0
-torch
\ No newline at end of file
+# This file was autogenerated by uv via the following command:
+#    uv export --frozen --no-hashes --output-file=requirements.txt
+-e .
+alabaster==0.7.16
+    # via sphinx
+appnope==0.1.4 ; sys_platform == 'darwin'
+    # via ipykernel
+asttokens==3.0.1
+    # via stack-data
+attrs==26.1.0
+    # via
+    #   jsonschema
+    #   jupyter-cache
+    #   referencing
+babel==2.18.0
+    # via sphinx
+backports-tarfile==1.2.0 ; python_full_version < '3.12' and platform_machine != 'ppc64le' and platform_machine != 's390x'
+    # via jaraco-context
+beautifulsoup4==4.15.0
+    # via nbconvert
+bleach==6.4.0
+    # via nbconvert
+build==1.5.0
+certifi==2026.5.20
+    # via requests
+cffi==2.0.0 ; (implementation_name == 'pypy' and platform_machine == 'ppc64le') or (implementation_name == 'pypy' and platform_machine == 's390x') or (implementation_name == 'pypy' and platform_machine != 'ppc64le' and platform_machine != 's390x' and platform_python_implementation == 'PyPy') or (implementation_name == 'pypy' and sys_platform != 'linux') or (platform_machine != 'ppc64le' and platform_machine != 's390x' and platform_python_implementation != 'PyPy' and sys_platform == 'linux')
+    # via
+    #   cryptography
+    #   pyzmq
+cfgv==3.5.0
+    # via pre-commit
+charset-normalizer==3.4.7
+    # via requests
+click==8.4.1
+    # via jupyter-cache
+colorama==0.4.6 ; os_name == 'nt' or sys_platform == 'win32'
+    # via
+    #   build
+    #   click
+    #   ipython
+    #   pytest
+    #   sphinx
+comm==0.2.3
+    # via ipykernel
+contourpy==1.3.2 ; python_full_version < '3.11'
+    # via matplotlib
+contourpy==1.3.3 ; python_full_version >= '3.11'
+    # via matplotlib
+coverage==7.14.1
+    # via pytest-cov
+cryptography==48.0.1 ; platform_machine != 'ppc64le' and platform_machine != 's390x' and sys_platform == 'linux'
+    # via secretstorage
+cycler==0.12.1
+    # via matplotlib
+debugpy==1.8.21
+    # via ipykernel
+decorator==5.3.1
+    # via ipython
+defusedxml==0.7.1
+    # via nbconvert
+distlib==0.4.2
+    # via virtualenv
+docutils==0.19
+    # via
+    #   myst-parser
+    #   pybtex-docutils
+    #   readme-renderer
+    #   sphinx
+    #   sphinx-mdinclude
+    #   sphinx-rtd-theme
+    #   sphinxcontrib-bibtex
+exceptiongroup==1.3.1 ; python_full_version < '3.11'
+    # via
+    #   ipython
+    #   pytest
+execnet==2.1.2
+    # via pytest-xdist
+executing==2.2.1
+    # via stack-data
+fastjsonschema==2.21.2
+    # via nbformat
+filelock==3.29.1
+    # via
+    #   python-discovery
+    #   torch
+    #   virtualenv
+fonttools==4.63.0
+    # via matplotlib
+fsspec==2026.4.0
+    # via torch
+greenlet==3.5.1 ; platform_machine == 'AMD64' or platform_machine == 'WIN32' or platform_machine == 'aarch64' or platform_machine == 'amd64' or platform_machine == 'ppc64le' or platform_machine == 'win32' or platform_machine == 'x86_64'
+    # via sqlalchemy
+groundwork-sphinx-theme==1.1.1
+id==1.6.1
+    # via twine
+identify==2.6.19
+    # via pre-commit
+idna==3.18
+    # via requests
+imagesize==2.0.0
+    # via sphinx
+importlib-metadata==9.0.0
+    # via
+    #   build
+    #   jupyter-cache
+    #   keyring
+    #   myst-nb
+iniconfig==2.3.0
+    # via pytest
+ipykernel==6.31.0
+    # via
+    #   myst-nb
+    #   nbmake
+ipython==8.39.0 ; python_full_version < '3.11'
+    # via
+    #   ipykernel
+    #   myst-nb
+ipython==9.14.1 ; python_full_version >= '3.11'
+    # via
+    #   ipykernel
+    #   myst-nb
+ipython-pygments-lexers==1.1.1 ; python_full_version >= '3.11'
+    # via ipython
+jaraco-classes==3.4.0 ; platform_machine != 'ppc64le' and platform_machine != 's390x'
+    # via keyring
+jaraco-context==6.1.2 ; platform_machine != 'ppc64le' and platform_machine != 's390x'
+    # via keyring
+jaraco-functools==4.5.0 ; platform_machine != 'ppc64le' and platform_machine != 's390x'
+    # via keyring
+jedi==0.20.0
+    # via ipython
+jeepney==0.9.0 ; platform_machine != 'ppc64le' and platform_machine != 's390x' and sys_platform == 'linux'
+    # via
+    #   keyring
+    #   secretstorage
+jinja2==3.1.6
+    # via
+    #   myst-parser
+    #   nbconvert
+    #   sphinx
+    #   torch
+jsonschema==4.26.0
+    # via nbformat
+jsonschema-specifications==2025.9.1
+    # via jsonschema
+jupyter-cache==1.0.1
+    # via myst-nb
+jupyter-client==8.9.1
+    # via
+    #   ipykernel
+    #   nbclient
+jupyter-core==5.9.1
+    # via
+    #   ipykernel
+    #   jupyter-client
+    #   nbclient
+    #   nbconvert
+    #   nbformat
+jupyterlab-pygments==0.3.0
+    # via nbconvert
+karma-sphinx-theme==0.0.8
+keyring==25.7.0 ; platform_machine != 'ppc64le' and platform_machine != 's390x'
+    # via twine
+kiwisolver==1.5.0
+    # via matplotlib
+latexcodec==3.0.1
+    # via pybtex
+librt==0.11.0 ; platform_python_implementation != 'PyPy'
+    # via mypy
+markdown-it-py==3.0.0
+    # via
+    #   mdit-py-plugins
+    #   myst-parser
+    #   rich
+markupsafe==3.0.3
+    # via
+    #   jinja2
+    #   nbconvert
+matplotlib==3.10.9
+matplotlib-inline==0.2.2
+    # via
+    #   ipykernel
+    #   ipython
+mdit-py-plugins==0.6.1
+    # via myst-parser
+mdurl==0.1.2
+    # via markdown-it-py
+mistune==3.2.1
+    # via
+    #   nbconvert
+    #   sphinx-mdinclude
+more-itertools==11.1.0 ; platform_machine != 'ppc64le' and platform_machine != 's390x'
+    # via
+    #   jaraco-classes
+    #   jaraco-functools
+mpmath==1.3.0
+    # via sympy
+mypy==1.20.2
+mypy-extensions==1.1.0
+    # via mypy
+myst-nb==1.4.0
+myst-parser==3.0.1
+    # via myst-nb
+nbclient==0.11.0
+    # via
+    #   jupyter-cache
+    #   myst-nb
+    #   nbconvert
+    #   nbmake
+nbconvert==7.17.1
+nbformat==5.10.4
+    # via
+    #   jupyter-cache
+    #   myst-nb
+    #   nbclient
+    #   nbconvert
+    #   nbmake
+nbmake==1.5.5
+nest-asyncio==1.6.0
+    # via ipykernel
+networkx==3.4.2 ; python_full_version < '3.11'
+    # via torch
+networkx==3.6.1 ; python_full_version >= '3.11'
+    # via torch
+nh3==0.3.5
+    # via readme-renderer
+nodeenv==1.10.0
+    # via pre-commit
+numpy==2.2.6 ; python_full_version < '3.11'
+    # via
+    #   contourpy
+    #   matplotlib
+    #   torchpfaffian
+numpy==2.4.6 ; python_full_version >= '3.11'
+    # via
+    #   contourpy
+    #   matplotlib
+    #   torchpfaffian
+packaging==26.2
+    # via
+    #   build
+    #   ipykernel
+    #   matplotlib
+    #   nbconvert
+    #   pytest
+    #   sphinx
+    #   twine
+pandocfilters==1.5.1
+    # via nbconvert
+parso==0.8.7
+    # via jedi
+pathspec==1.1.1
+    # via mypy
+pexpect==4.9.0 ; sys_platform != 'emscripten' and sys_platform != 'win32'
+    # via ipython
+pillow==12.2.0
+    # via matplotlib
+pip==26.1.2
+platformdirs==4.10.0
+    # via
+    #   jupyter-core
+    #   python-discovery
+    #   virtualenv
+pluggy==1.6.0
+    # via
+    #   pytest
+    #   pytest-cov
+pre-commit==4.6.0
+prompt-toolkit==3.0.52
+    # via ipython
+psutil==7.2.2
+    # via
+    #   ipykernel
+    #   ipython
+ptyprocess==0.7.0 ; sys_platform != 'emscripten' and sys_platform != 'win32'
+    # via pexpect
+pure-eval==0.2.3
+    # via stack-data
+pybtex==0.26.1
+    # via
+    #   pybtex-docutils
+    #   sphinxcontrib-bibtex
+pybtex-docutils==1.0.3
+    # via sphinxcontrib-bibtex
+pycparser==3.0 ; (implementation_name == 'pypy' and platform_machine == 'ppc64le') or (implementation_name == 'pypy' and platform_machine == 's390x') or (implementation_name == 'pypy' and platform_machine != 'ppc64le' and platform_machine != 's390x' and platform_python_implementation == 'PyPy') or (implementation_name != 'PyPy' and platform_machine != 'ppc64le' and platform_machine != 's390x' and platform_python_implementation != 'PyPy' and sys_platform == 'linux') or (implementation_name == 'pypy' and sys_platform != 'linux')
+    # via cffi
+pygments==2.20.0
+    # via
+    #   ipython
+    #   ipython-pygments-lexers
+    #   nbconvert
+    #   nbmake
+    #   pytest
+    #   readme-renderer
+    #   rich
+    #   sphinx
+    #   sphinx-mdinclude
+pyparsing==3.3.2
+    # via matplotlib
+pyproject-hooks==1.2.0
+    # via build
+pytest==8.4.2
+    # via
+    #   nbmake
+    #   pytest-cov
+    #   pytest-json-report
+    #   pytest-metadata
+    #   pytest-timeout
+    #   pytest-xdist
+pytest-cov==6.3.0
+pytest-json-report==1.5.0
+pytest-metadata==3.1.1
+    # via pytest-json-report
+pytest-timeout==2.4.0
+pytest-xdist==3.8.0
+python-dateutil==2.9.0.post0
+    # via
+    #   jupyter-client
+    #   matplotlib
+python-discovery==1.4.0
+    # via virtualenv
+pywin32-ctypes==0.2.3 ; platform_machine != 'ppc64le' and platform_machine != 's390x' and sys_platform == 'win32'
+    # via keyring
+pyyaml==6.0.3
+    # via
+    #   jupyter-cache
+    #   myst-nb
+    #   myst-parser
+    #   pre-commit
+    #   pybtex
+pyzmq==27.1.0
+    # via
+    #   ipykernel
+    #   jupyter-client
+readme-renderer==43.0
+    # via twine
+referencing==0.37.0
+    # via
+    #   jsonschema
+    #   jsonschema-specifications
+requests==2.34.2
+    # via
+    #   requests-toolbelt
+    #   sphinx
+    #   twine
+requests-toolbelt==1.0.0
+    # via twine
+rfc3986==2.0.0
+    # via twine
+rich==15.0.0
+    # via twine
+rpds-py==0.30.0 ; python_full_version < '3.11'
+    # via
+    #   jsonschema
+    #   referencing
+rpds-py==2026.5.1 ; python_full_version >= '3.11'
+    # via
+    #   jsonschema
+    #   referencing
+ruff==0.15.16
+secretstorage==3.5.0 ; platform_machine != 'ppc64le' and platform_machine != 's390x' and sys_platform == 'linux'
+    # via keyring
+setuptools==82.0.1
+    # via
+    #   torch
+    #   torchpfaffian
+six==1.17.0
+    # via python-dateutil
+snowballstemmer==3.1.1
+    # via sphinx
+soupsieve==2.8.4
+    # via beautifulsoup4
+sphinx==6.2.1
+    # via
+    #   groundwork-sphinx-theme
+    #   myst-nb
+    #   myst-parser
+    #   sphinx-mdinclude
+    #   sphinx-rtd-theme
+    #   sphinxcontrib-bibtex
+    #   sphinxcontrib-jquery
+sphinx-mdinclude==0.6.2
+sphinx-rtd-theme==3.1.0
+sphinx-theme==1.0
+sphinxcontrib-applehelp==2.0.0
+    # via sphinx
+sphinxcontrib-bibtex==2.5.0
+sphinxcontrib-devhelp==2.0.0
+    # via sphinx
+sphinxcontrib-htmlhelp==2.1.0
+    # via sphinx
+sphinxcontrib-jquery==4.1
+    # via sphinx-rtd-theme
+sphinxcontrib-jsmath==1.0.1
+    # via sphinx
+sphinxcontrib-qthelp==2.0.0
+    # via sphinx
+sphinxcontrib-serializinghtml==2.0.0
+    # via sphinx
+sqlalchemy==2.0.50
+    # via jupyter-cache
+stack-data==0.6.3
+    # via ipython
+sympy==1.14.0
+    # via torch
+tabulate==0.10.0
+    # via jupyter-cache
+tinycss2==1.5.1
+    # via bleach
+tomli==2.4.1 ; python_full_version <= '3.11'
+    # via
+    #   build
+    #   coverage
+    #   mypy
+    #   pytest
+torch==2.10.0
+    # via torchpfaffian
+tornado==6.5.7
+    # via
+    #   ipykernel
+    #   jupyter-client
+traitlets==5.15.1
+    # via
+    #   ipykernel
+    #   ipython
+    #   jupyter-client
+    #   jupyter-core
+    #   matplotlib-inline
+    #   nbclient
+    #   nbconvert
+    #   nbformat
+twine==6.2.0
+typing-extensions==4.15.0
+    # via
+    #   beautifulsoup4
+    #   cryptography
+    #   exceptiongroup
+    #   ipython
+    #   jupyter-client
+    #   mistune
+    #   mypy
+    #   myst-nb
+    #   referencing
+    #   sqlalchemy
+    #   torch
+    #   virtualenv
+urllib3==2.7.0
+    # via
+    #   id
+    #   requests
+    #   twine
+virtualenv==21.4.2
+    # via pre-commit
+wcwidth==0.8.1
+    # via prompt-toolkit
+webencodings==0.5.1
+    # via
+    #   bleach
+    #   tinycss2
+wheel==0.45.1
+zipp==4.1.0
+    # via importlib-metadata
diff --git a/run_pytests.py b/run_pytests.py
deleted file mode 100644
index a2706a8..0000000
--- a/run_pytests.py
+++ /dev/null
@@ -1,37 +0,0 @@
-import json
-import os
-
-import pytest
-import pythonbasictools as pbt
-from pytest_jsonreport.plugin import JSONReport
-from pytest_cov.plugin import CovPlugin
-
-from tests import configs
-from tests.conftest import RUN_SLOW_ARG_NAME
-
-if __name__ == '__main__':
-    sys_args_dict = pbt.cmds.get_cmd_kwargs(
-        {
-            1                        : os.path.join(os.getcwd(), "tests"),
-            "N_RANDOM_TESTS_PER_CASE": configs.N_RANDOM_TESTS_PER_CASE,
-            "save_report"            : "True",
-            "cov"                    : "src",
-            "cov-report"             : "xml",
-            RUN_SLOW_ARG_NAME        : "True",
-            "durations"              : 10,
-        }
-    )
-    sys_args_dict["cov-report"] = sys_args_dict["cov_report"]  # fix
-    configs.N_RANDOM_TESTS_PER_CASE = sys_args_dict["N_RANDOM_TESTS_PER_CASE"]
-    configs.RUN_SLOW_TESTS = 't' in str(sys_args_dict[RUN_SLOW_ARG_NAME]).lower()
-    json_plugin = JSONReport()
-    pytest_main_args_names = ["cov", "cov-report", "durations"]
-    pytest_main_args = [f"--{k}={sys_args_dict[k]}" for k in pytest_main_args_names]
-    pytest.main([sys_args_dict[1], *pytest_main_args], plugins=[json_plugin])
-    json_path = os.path.join(os.getcwd(), "tests", "tmp", f"tests_report_rn{configs.N_RANDOM_TESTS_PER_CASE}.json")
-    save_report = 't' in str(sys_args_dict["save_report"]).lower()
-    if save_report:
-        json_plugin.save_report(json_path)
-        json_data = json.load(open(json_path))
-        with open(json_path, "w") as f:
-            json.dump(json_data, f, indent=4)
diff --git a/setup.py b/setup.py
index 739cb90..53a465e 100644
--- a/setup.py
+++ b/setup.py
@@ -1,28 +1,29 @@
-from setuptools import setup
 import setuptools
+from setuptools import setup
 
 setup(
-    name='TorchPfaffian',
-    long_description='file: README.md',
+    name="TorchPfaffian",
+    long_description="file: README.md",
     long_description_content_type="text/markdown",
     package_dir={"": "src"},
     packages=setuptools.find_packages(where="src"),
-    python_requires=">=3.8",
+    python_requires=">=3.10",
     classifiers=[
         "Development Status :: 3 - Alpha",
         "Intended Audience :: Science/Research",
         "License :: OSI Approved :: Apache Software License",
         "Programming Language :: Python :: 3",
         "Programming Language :: Python :: 3.10",
+        "Programming Language :: Python :: 3.11",
         "Topic :: Scientific/Engineering",
         "Topic :: Scientific/Engineering :: Artificial Intelligence",
         "Topic :: Software Development :: Libraries :: Python Modules",
         "Operating System :: OS Independent",
     ],
     project_urls={
-        'Homepage': 'https://https://github.com/MatchCake/TorchPfaffian',
-        'Source': 'https://https://github.com/MatchCake/TorchPfaffian',
-        'Documentation': 'https://MatchCake.github.io/TorchPfaffian',
+        "Homepage": "https://github.com/MatchCake/TorchPfaffian",
+        "Source": "https://github.com/MatchCake/TorchPfaffian",
+        "Documentation": "https://MatchCake.github.io/TorchPfaffian",
     },
 )
 
@@ -37,4 +38,3 @@
 #   twine check dist/*
 #   twine upload --repository-url https://test.pypi.org/legacy/ dist/*
 #   twine upload dist/*
-
diff --git a/sphinx/clean_html_files.py b/sphinx/clean_html_files.py
index c066388..8499831 100644
--- a/sphinx/clean_html_files.py
+++ b/sphinx/clean_html_files.py
@@ -3,20 +3,20 @@
 
 
 def clean_html_files():
-	meta_filename = "docs/html_files_list.txt"
-	if not os.path.exists(meta_filename):
-		return
-	with open(meta_filename, "r") as f:
-		files = f.readlines()
-	for file in files:
-		path = os.path.join(os.getcwd(), "docs", file.strip())
-		if os.path.exists(path):
-			if os.path.isdir(path):
-				shutil.rmtree(path)
-			else:
-				os.remove(path)
-	os.remove(meta_filename)
+    meta_filename = "docs/html_files_list.txt"
+    if not os.path.exists(meta_filename):
+        return
+    with open(meta_filename, "r") as f:
+        files = f.readlines()
+    for file in files:
+        path = os.path.join(os.getcwd(), "docs", file.strip())
+        if os.path.exists(path):
+            if os.path.isdir(path):
+                shutil.rmtree(path)
+            else:
+                os.remove(path)
+    os.remove(meta_filename)
 
 
-if __name__ == '__main__':
-	clean_html_files()
+if __name__ == "__main__":
+    clean_html_files()
diff --git a/sphinx/generate_doc.py b/sphinx/generate_doc.py
index 25de62e..f2f30a3 100644
--- a/sphinx/generate_doc.py
+++ b/sphinx/generate_doc.py
@@ -1,17 +1,18 @@
-import os, sys
+import os
+import sys
 
 
-def generate_doc(path_to_root_dir: str = '.'):
-	commands = [
-		rf"sphinx-apidoc -f -o {path_to_root_dir}/sphinx/source {path_to_root_dir}/src/neurotorch",
-		rf"{path_to_root_dir}\sphinx\make clean html",
-		rf"{path_to_root_dir}\sphinx\make html",
-	]
-	for command in commands:
-		print(f"Executing: {command}")
-		os.system(command)
+def generate_doc(path_to_root_dir: str = "."):
+    commands = [
+        rf"sphinx-apidoc -f -o {path_to_root_dir}/sphinx/source {path_to_root_dir}/src/neurotorch",
+        rf"{path_to_root_dir}\sphinx\make clean html",
+        rf"{path_to_root_dir}\sphinx\make html",
+    ]
+    for command in commands:
+        print(f"Executing: {command}")
+        os.system(command)
 
 
-if __name__ == '__main__':
-	root_dir = sys.argv[1] if len(sys.argv) > 1 else '..'
-	generate_doc(root_dir)
+if __name__ == "__main__":
+    root_dir = sys.argv[1] if len(sys.argv) > 1 else ".."
+    generate_doc(root_dir)
diff --git a/sphinx/make_html_files_list.py b/sphinx/make_html_files_list.py
index 1422259..cc37bbf 100644
--- a/sphinx/make_html_files_list.py
+++ b/sphinx/make_html_files_list.py
@@ -2,12 +2,12 @@
 
 
 def make_html_file_list():
-	meta_filename = "./docs/html_files_list.txt"
-	os.makedirs(os.path.dirname(meta_filename), exist_ok=True)
-	files = os.listdir(os.path.join('sphinx', "build", "html"))
-	with open(meta_filename, "w") as f:
-		f.write("\n".join(files))
-	
-
-if __name__ == '__main__':
-	make_html_file_list()
+    meta_filename = "./docs/html_files_list.txt"
+    os.makedirs(os.path.dirname(meta_filename), exist_ok=True)
+    files = os.listdir(os.path.join("sphinx", "build", "html"))
+    with open(meta_filename, "w") as f:
+        f.write("\n".join(files))
+
+
+if __name__ == "__main__":
+    make_html_file_list()
diff --git a/sphinx/source/conf.py b/sphinx/source/conf.py
index 98193c8..cf7d59f 100644
--- a/sphinx/source/conf.py
+++ b/sphinx/source/conf.py
@@ -4,13 +4,18 @@
 # https://www.sphinx-doc.org/en/master/usage/configuration.html
 
 import os
-import pprint
-import sys
 import shutil
-basedir = os.path.abspath(os.path.join(os.path.dirname(__file__), '..', '..', 'src'))
+import sys
+
+basedir = os.path.abspath(os.path.join(os.path.dirname(__file__), "..", "..", "src"))
 sys.path.insert(0, basedir)
 import torch_pfaffian
 
+_nb_src = os.path.join(os.path.dirname(__file__), "..", "..", "notebooks")
+_nb_dst = os.path.join(os.path.dirname(__file__), "notebooks")
+if os.path.isdir(_nb_src):
+    shutil.rmtree(_nb_dst, ignore_errors=True)
+    shutil.copytree(_nb_src, _nb_dst)
 
 _html_folders_formatted = {}
 _allowed_special_methods = ["__init__", "__call__"]
@@ -24,44 +29,44 @@ def skip(app, what, name, obj, would_skip, options):
 
 def setup(app):
     app.connect("autodoc-skip-member", skip)
-    app.connect('html-page-context', change_pathto)
-    app.connect('build-finished', move_private_folders)
+    app.connect("html-page-context", change_pathto)
+    app.connect("build-finished", move_private_folders)
 
 
 def change_pathto(app, pagename, templatename, context, doctree):
     """
     Replace pathto helper to change paths to folders with a leading underscore.
     """
-    pathto = context.get('pathto')
-    
+    pathto = context.get("pathto")
+
     def gh_pathto(otheruri, *args, **kw):
-        if otheruri.startswith('_'):
+        if otheruri.startswith("_"):
             otheruri_fmt = otheruri[1:]
             _html_folders_formatted[os.path.dirname(otheruri)] = os.path.dirname(otheruri_fmt)
         else:
             otheruri_fmt = otheruri
         return pathto(otheruri_fmt, *args, **kw)
-    
-    context['pathto'] = gh_pathto
+
+    context["pathto"] = gh_pathto
 
 
 def move_private_folders(app, e):
     """
     Remove leading underscore from folders in in the output folder.
     """
-    
+
     def join(dir):
         return os.path.join(app.builder.outdir, dir)
-    
+
     for item in os.listdir(app.builder.outdir):
-        if item.startswith('_') and os.path.isdir(join(item)) and item in _html_folders_formatted:
+        if item.startswith("_") and os.path.isdir(join(item)) and item in _html_folders_formatted:
             shutil.move(join(item), join(item[1:]))
 
 
 # -- Project information -----------------------------------------------------
 # https://www.sphinx-doc.org/en/master/usage/configuration.html#project-information
 
-project = 'torch_pfaffian'
+project = "torch_pfaffian"
 copyright = torch_pfaffian.__copyright__.replace("Copyright ", "")
 author = torch_pfaffian.__author__
 version = torch_pfaffian.__version__
@@ -70,38 +75,41 @@ def join(dir):
 # https://www.sphinx-doc.org/en/master/usage/configuration.html#general-configuration
 
 extensions = [
-    'sphinx.ext.autodoc',
-    'sphinx.ext.napoleon',
-    'sphinx.ext.githubpages',
+    "sphinx.ext.autodoc",
+    "sphinx.ext.napoleon",
+    "sphinx.ext.githubpages",
     # 'sphinx_rtd_theme',
-    'sphinxcontrib.bibtex',
-    'sphinx_mdinclude',
-    # 'nbsphinx',
-    'sphinx.ext.mathjax',
+    "sphinxcontrib.bibtex",
+    "sphinx_mdinclude",
+    "myst_nb",
+    "sphinx.ext.mathjax",
     # 'sphinx.ext.mathbase',
-    'sphinx.ext.todo',
+    "sphinx.ext.todo",
 ]
 
-bibtex_bibfiles = ['references.bib']
+bibtex_bibfiles = ["references.bib"]
+
+nb_execution_mode = "force"
+nb_execution_timeout = 300
 
-templates_path = ['_templates']
+templates_path = ["_templates"]
 exclude_patterns = []
 
 
 # -- Options for HTML output -------------------------------------------------
 # https://www.sphinx-doc.org/en/master/usage/configuration.html#options-for-html-output
 
-html_theme = 'karma_sphinx_theme'
-html_static_path = ['_static']
+html_theme = "karma_sphinx_theme"
+html_static_path = ["_static"]
 # html_css_files = [
 #     'css/float_right.css',
 # ]
 # mathjax_path = "https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-mml-chtml.js"
 mathjax_path = "https://cdn.mathjax.org/mathjax/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML"
-latex_engine = 'xelatex'
+latex_engine = "xelatex"
 latex_elements = {
-    'preamble': r'\usepackage{physics}'
-                r'\usepackage{mathtools}'
-                r'\usepackage{amsmath}'
-                r'\usepackage{nicefrac}'
+    "preamble": r"\usepackage{physics}"
+    r"\usepackage{mathtools}"
+    r"\usepackage{amsmath}"
+    r"\usepackage{nicefrac}"
 }
diff --git a/sphinx/source/index.rst b/sphinx/source/index.rst
index 388f894..360fc34 100644
--- a/sphinx/source/index.rst
+++ b/sphinx/source/index.rst
@@ -12,6 +12,12 @@ Welcome to <PackageName>'s documentation!
 
     readme
 
+.. toctree::
+   :maxdepth: 4
+   :caption: Tutorials:
+
+   notebooks/strategies_benchmark
+
 .. toctree::
    :maxdepth: 4
    :caption: Modules:
diff --git a/src/torch_pfaffian/__init__.py b/src/torch_pfaffian/__init__.py
index 1461dad..53aa80b 100644
--- a/src/torch_pfaffian/__init__.py
+++ b/src/torch_pfaffian/__init__.py
@@ -1,5 +1,5 @@
 """
-Project description.
+TorchPfaffian is a Python package for efficiently computing the Pfaffian of skew-symmetric matrices using PyTorch.
 """
 
 __author__ = "Jérémie Gince"
@@ -14,16 +14,13 @@
 
 import torch
 
-warnings.filterwarnings("ignore", category=Warning, module="docutils")
-warnings.filterwarnings("ignore", category=Warning, module="sphinx")
-
 from .strategies import *
 from .utils import get_all_subclasses
 
-pfaffian_strategy_map = {
-    _cls.NAME.lower().strip(): _cls
-    for _cls in get_all_subclasses(PfaffianStrategy)
-}
+warnings.filterwarnings("ignore", category=Warning, module="docutils")
+warnings.filterwarnings("ignore", category=Warning, module="sphinx")
+
+pfaffian_strategy_map = {_cls.NAME.lower().strip(): _cls for _cls in get_all_subclasses(PfaffianStrategy)}
 
 
 def get_pfaffian_function(name: str = PfaffianFDBPf.NAME) -> Callable[[torch.Tensor], torch.Tensor]:
@@ -31,4 +28,3 @@ def get_pfaffian_function(name: str = PfaffianFDBPf.NAME) -> Callable[[torch.Ten
     if name not in pfaffian_strategy_map:
         raise ValueError(f"Unknown strategy name: {name}. Available strategies: {list(pfaffian_strategy_map.keys())}")
     return pfaffian_strategy_map[name].apply
-
diff --git a/src/torch_pfaffian/__main__.py b/src/torch_pfaffian/__main__.py
deleted file mode 100644
index 36f5609..0000000
--- a/src/torch_pfaffian/__main__.py
+++ /dev/null
@@ -1,15 +0,0 @@
-import argparse
-import sys
-
-
-def parse_args():
-    paser = argparse.ArgumentParser()
-    return paser.parse_args()
-
-
-def main():
-    pass
-
-
-if __name__ == '__main__':
-    sys.exit(main())
diff --git a/src/torch_pfaffian/py.typed b/src/torch_pfaffian/py.typed
new file mode 100644
index 0000000..e69de29
diff --git a/src/torch_pfaffian/strategies/__init__.py b/src/torch_pfaffian/strategies/__init__.py
index 983c798..5df968c 100644
--- a/src/torch_pfaffian/strategies/__init__.py
+++ b/src/torch_pfaffian/strategies/__init__.py
@@ -1,9 +1,8 @@
 from typing import Callable
+
 import torch
 
-from .strategy import PfaffianStrategy
+from .pfaffian_block_det import PfaffianBlockDet
+from .pfaffian_det import PfaffianDet
 from .pfaffian_fdbpf import PfaffianFDBPf
-# from .pfaffian_block_det import PfaffianBlockDet
-# from .pfaffian_perm import PfaffianPerm
-
-
+from .strategy import PfaffianStrategy
diff --git a/src/torch_pfaffian/strategies/pfaffian_block_det.py b/src/torch_pfaffian/strategies/pfaffian_block_det.py
index 0768598..526bd59 100644
--- a/src/torch_pfaffian/strategies/pfaffian_block_det.py
+++ b/src/torch_pfaffian/strategies/pfaffian_block_det.py
@@ -1,5 +1,7 @@
-from typing import Any
+from typing import cast
+
 import torch
+
 from .strategy import PfaffianStrategy
 
 
@@ -10,6 +12,7 @@ class PfaffianBlockDet(PfaffianStrategy):
 
     The input matrix is considered to be a skew-symmetric matrix.
     """
+
     NAME = "PfaffianBlockDet"
 
     @staticmethod
@@ -22,24 +25,31 @@ def forward(matrix: torch.Tensor):
         # take the upper right block of shape (..., N, N) of the input matrix
         n = matrix.shape[-1] // 2
         sub_matrix = matrix[..., :n, n:]
-        pf = (-1)**(n*(n-1)//2) * torch.linalg.det(sub_matrix)
+        pf = (-1) ** (n * (n - 1) // 2) * torch.linalg.det(sub_matrix)
         return pf
 
     @staticmethod
-    def backward(ctx: torch.autograd.function.FunctionCtx, grad_output):
+    def backward(ctx: torch.autograd.function.BackwardCFunction, grad_output):
         r"""
+        Compute the gradient of the Pfaffian with respect to the full input matrix.
 
-        ..math:
-            \frac{\partial \text{pf}(A)}{\partial A_{ij}} = \frac{\text{pf}(A)}{2} A^{-1}_{ji}
+        The forward depends only on the upper-right block ``block = matrix[..., :n, n:]`` through
+        ``pf = c * det(block)``. By Jacobi's formula ``d det(block) / d block = det(block) * (block^{-1})^T``,
+        so ``d pf / d block = pf * (block^{-1})^T``. The gradient is therefore zero everywhere except in
+        the upper-right block.
 
-        :param ctx: Context
-        :param grad_output: Gradient of the output
-        :return: Gradient of the input
+        :param ctx: Context holding the saved input matrix and the forward Pfaffian.
+        :param grad_output: Gradient of the output with respect to the loss.
+        :return: Gradient of the input matrix, or ``None`` when the input does not require grad.
+        :rtype: torch.Tensor | None
         """
-        matrix, pf = ctx.saved_tensors
+        matrix, pf = cast("tuple[torch.Tensor, torch.Tensor]", ctx.saved_tensors)
         grad_matrix = None
         if ctx.needs_input_grad[0]:
-            grad_matrix = torch.einsum('...,...ij->...ji', 0.5 * grad_output * pf, torch.linalg.pinv(matrix))
+            n = matrix.shape[-1] // 2
+            sub_matrix = matrix[..., :n, n:]  # (..., n, n) upper-right block
+            inverse_transpose = torch.linalg.inv(sub_matrix).transpose(-1, -2)
+            grad_block = grad_output[..., None, None] * pf[..., None, None] * inverse_transpose
+            grad_matrix = torch.zeros_like(matrix)
+            grad_matrix[..., :n, n:] = grad_block
         return grad_matrix
-
-
diff --git a/src/torch_pfaffian/strategies/pfaffian_det.py b/src/torch_pfaffian/strategies/pfaffian_det.py
new file mode 100644
index 0000000..7051779
--- /dev/null
+++ b/src/torch_pfaffian/strategies/pfaffian_det.py
@@ -0,0 +1,21 @@
+import torch
+
+from .strategy import PfaffianStrategy
+
+
+class PfaffianDet(PfaffianStrategy):
+    """
+    Compute the Pfaffian as ``pf = sqrt(|det(A)|)`` and rely on autograd for the backward pass.
+
+    The other strategies are full ``torch.autograd.Function`` implementations with a custom analytic
+    backward. This strategy instead differentiates straight through ``det`` and ``sqrt`` with PyTorch
+    autograd, so it overrides :meth:`apply` to bypass the ``torch.autograd.Function`` machinery. The
+    radicand is clamped to ``EPSILON`` for numerical stability, matching the other determinant-based
+    strategy.
+    """
+
+    NAME = "PfaffianDet"
+
+    @staticmethod
+    def apply(matrix: torch.Tensor) -> torch.Tensor:
+        return torch.sqrt(torch.clamp(torch.abs(torch.linalg.det(matrix)), min=PfaffianStrategy.EPSILON))
diff --git a/src/torch_pfaffian/strategies/pfaffian_fdbpf.py b/src/torch_pfaffian/strategies/pfaffian_fdbpf.py
index 1ab2b3f..1695251 100644
--- a/src/torch_pfaffian/strategies/pfaffian_fdbpf.py
+++ b/src/torch_pfaffian/strategies/pfaffian_fdbpf.py
@@ -1,5 +1,7 @@
-from typing import Any
+from typing import cast
+
 import torch
+
 from .strategy import PfaffianStrategy
 
 
@@ -8,6 +10,7 @@ class PfaffianFDBPf(PfaffianStrategy):
     This class implements the Pfaffian using the determinant of the matrix for the forward pass and the
     derivative of the Pfaffian with respect to the input matrix for the backward pass.
     """
+
     NAME = "PfaffianFDBPf"
 
     @staticmethod
@@ -16,11 +19,11 @@ def forward(matrix: torch.Tensor):
         if _2n % 2 != 0:
             return torch.zeros_like(matrix[..., 0, 0])
         det = torch.linalg.det(matrix)
-        pf = torch.sqrt(torch.abs(det) + PfaffianFDBPf.EPSILON)
+        pf = torch.sqrt(torch.clamp(torch.abs(det), min=PfaffianFDBPf.EPSILON))
         return pf
 
     @staticmethod
-    def backward(ctx: torch.autograd.function.FunctionCtx, grad_output):
+    def backward(ctx: torch.autograd.function.BackwardCFunction, grad_output):
         r"""
 
         ..math:
@@ -30,10 +33,8 @@ def backward(ctx: torch.autograd.function.FunctionCtx, grad_output):
         :param grad_output: Gradient of the output
         :return: Gradient of the input
         """
-        matrix, pf = ctx.saved_tensors
+        matrix, pf = cast("tuple[torch.Tensor, torch.Tensor]", ctx.saved_tensors)
         grad_matrix = None
         if ctx.needs_input_grad[0]:
-            grad_matrix = torch.einsum('...,...ij->...ji', 0.5 * grad_output * pf, torch.linalg.pinv(matrix))
+            grad_matrix = torch.einsum("...,...ij->...ji", 0.5 * grad_output * pf, torch.linalg.pinv(matrix))
         return grad_matrix
-
-
diff --git a/src/torch_pfaffian/strategies/strategy.py b/src/torch_pfaffian/strategies/strategy.py
index ff5b03f..7723d9f 100644
--- a/src/torch_pfaffian/strategies/strategy.py
+++ b/src/torch_pfaffian/strategies/strategy.py
@@ -1,14 +1,13 @@
-from abc import ABC, abstractmethod
 import torch
 
 
 class PfaffianStrategy(torch.autograd.Function):
-    EPSILON = 1e-12
+    EPSILON = 1e-30
     NAME = "PfaffianStrategy"
 
     @staticmethod
     def setup_context(ctx: torch.autograd.function.FunctionCtx, inputs, output):
-        matrix, = inputs
+        (matrix,) = inputs
         pf = output
         ctx.save_for_backward(matrix, pf)
 
@@ -17,7 +16,5 @@ def forward(matrix: torch.Tensor):
         pass
 
     @staticmethod
-    def backward(ctx: torch.autograd.function.FunctionCtx, grad_output):
+    def backward(ctx: torch.autograd.function.BackwardCFunction, grad_output):
         pass
-
-
diff --git a/tests/__init__.py b/tests/__init__.py
index 15102d4..bac659d 100644
--- a/tests/__init__.py
+++ b/tests/__init__.py
@@ -1,13 +1,6 @@
-import pytest
 import numpy as np
+import pytest
+
 from .configs import TEST_SEED
 
 np.random.seed(TEST_SEED)
-
-
-def get_slow_test_mark():
-    from .configs import RUN_SLOW_TESTS
-    return pytest.mark.skipif(
-        not RUN_SLOW_TESTS,
-        reason=f"Only run when configs.{RUN_SLOW_TESTS} is True.",
-    )
diff --git a/tests/configs.py b/tests/configs.py
index 2217dab..b2a9393 100644
--- a/tests/configs.py
+++ b/tests/configs.py
@@ -1,4 +1,3 @@
-
 N_RANDOM_TESTS_PER_CASE = 10
 TEST_SEED = 42
 
@@ -14,8 +13,6 @@
 ATOL_SHAPE_COMPARISON = 0
 RTOL_SHAPE_COMPARISON = 0
 
-RUN_SLOW_TESTS = True
-
 
 def set_seed(seed: int = TEST_SEED):
     import numpy as np
diff --git a/tests/conftest.py b/tests/conftest.py
index 8cf7624..e69de29 100644
--- a/tests/conftest.py
+++ b/tests/conftest.py
@@ -1,45 +0,0 @@
-import pytest
-from .configs import RUN_SLOW_TESTS
-
-RUN_SLOW_ARG_NAME = "run_slow"
-
-
-@pytest.hookimpl()
-def pytest_sessionstart(session):
-    pass
-
-
-# @pytest.hookimpl()
-# def pytest_sessionfinish(session, exitstatus):
-#     reporter = session.config.pluginmanager.get_plugin('terminalreporter')
-#     print('passed amount:', len(reporter.stats['passed']))
-
-
-def pytest_addoption(parser):
-    r"""
-    Add options to the pytest command.
-
-    See: https://jwodder.github.io/kbits/posts/pytest-mark-off/.
-
-    :param parser: The pytest parser.
-    :return: None
-    """
-    parser.addoption(
-        f"--{RUN_SLOW_ARG_NAME}",
-        action="store_true",
-        default=RUN_SLOW_TESTS,
-        help="Run slow tests",
-    )
-
-
-def pytest_collection_modifyitems(config, items):
-    if config.getoption(f"--{RUN_SLOW_ARG_NAME}"):
-    # if RUN_SLOW_TESTS:
-    #     print("Running slow tests")
-        pass
-    else:
-        # print("Skipping slow tests")
-        skipper = pytest.mark.skip(reason=f"Only run when '--{RUN_SLOW_ARG_NAME}=True' is given")
-        for item in items:
-            if "slow" in item.keywords:
-                item.add_marker(skipper)
diff --git a/tests/test_strategies/__init__.py b/tests/test_strategies/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/tests/test_strategies/test_pfaffian_block_det.py b/tests/test_strategies/test_pfaffian_block_det.py
new file mode 100644
index 0000000..c819f12
--- /dev/null
+++ b/tests/test_strategies/test_pfaffian_block_det.py
@@ -0,0 +1,82 @@
+import numpy as np
+import pytest
+import torch
+from torch.autograd import gradcheck
+
+from tests.configs import (
+    ATOL_APPROX_COMPARISON,
+    ATOL_MATRIX_COMPARISON,
+    N_RANDOM_TESTS_PER_CASE,
+    RTOL_APPROX_COMPARISON,
+    RTOL_MATRIX_COMPARISON,
+    TEST_SEED,
+)
+from torch_pfaffian.strategies.pfaffian_block_det import PfaffianBlockDet
+
+_RNG = np.random.default_rng(TEST_SEED)
+_BLOCK_SIZES = [1, 2, 3, 4]
+_RANDOM_BLOCKS = [_RNG.random((size, size)) for size in _BLOCK_SIZES for _ in range(N_RANDOM_TESTS_PER_CASE)]
+
+
+def _block_antidiagonal(block: np.ndarray) -> torch.Tensor:
+    # Build the skew-symmetric matrix [[0, block], [-block^T, 0]] that PfaffianBlockDet targets.
+    zero = np.zeros_like(block)
+    top = np.concatenate([zero, block], axis=-1)
+    bottom = np.concatenate([-np.einsum("...ij->...ji", block), zero], axis=-1)
+    return torch.tensor(np.concatenate([top, bottom], axis=-2))
+
+
+def _well_conditioned_blocks(sizes: list[int], count: int, min_abs_det: float = 0.1) -> list[np.ndarray]:
+    # The gradient relies on the inverse of the upper-right block, which is unstable for near-singular
+    # blocks, so the gradcheck inputs are restricted to blocks whose determinant is away from zero.
+    blocks = []
+    for size in sizes:
+        kept = 0
+        while kept < count:
+            candidate = _RNG.random((size, size))
+            if np.abs(np.linalg.det(candidate)) > min_abs_det:
+                blocks.append(candidate)
+                kept += 1
+    return blocks
+
+
+_GRADCHECK_BLOCKS = _well_conditioned_blocks(_BLOCK_SIZES, N_RANDOM_TESTS_PER_CASE)
+
+
+class TestPfaffianBlockDet:
+    @pytest.mark.parametrize("block", _RANDOM_BLOCKS)
+    def test_forward_square_of_pfaffian_matches_determinant(self, block):
+        matrix = _block_antidiagonal(block)
+        pfaffian = PfaffianBlockDet.apply(matrix)
+        determinant = torch.linalg.det(matrix)
+        torch.testing.assert_close(pfaffian**2, determinant, atol=ATOL_MATRIX_COMPARISON, rtol=RTOL_MATRIX_COMPARISON)
+
+    def test_forward_supports_batched_input(self):
+        matrix = _block_antidiagonal(_RNG.random((5, 3, 3)))
+        pfaffian = PfaffianBlockDet.apply(matrix)
+        assert pfaffian.shape == matrix.shape[:-2]
+        torch.testing.assert_close(
+            pfaffian**2, torch.linalg.det(matrix), atol=ATOL_MATRIX_COMPARISON, rtol=RTOL_MATRIX_COMPARISON
+        )
+
+    @pytest.mark.parametrize("dtype", [torch.float32, torch.float64])
+    def test_forward_preserves_dtype(self, dtype):
+        matrix = _block_antidiagonal(_RNG.random((3, 3))).to(dtype)
+        assert PfaffianBlockDet.apply(matrix).dtype == dtype
+
+    @pytest.mark.parametrize("block", _GRADCHECK_BLOCKS)
+    def test_backward_passes_gradcheck(self, block):
+        matrix = _block_antidiagonal(block).to(torch.float64).requires_grad_(True)
+        assert gradcheck(
+            PfaffianBlockDet.apply, (matrix,), eps=1e-6, atol=ATOL_APPROX_COMPARISON, rtol=RTOL_APPROX_COMPARISON
+        )
+
+    def test_backward_returns_none_when_input_does_not_require_grad(self):
+        matrix = _block_antidiagonal(_RNG.random((4, 4)))
+        pfaffian = PfaffianBlockDet.apply(matrix)
+
+        class _Context:
+            saved_tensors = (matrix, pfaffian)
+            needs_input_grad = (False,)
+
+        assert PfaffianBlockDet.backward(_Context(), torch.ones_like(pfaffian)) is None
diff --git a/tests/test_strategies/test_pfaffian_det.py b/tests/test_strategies/test_pfaffian_det.py
new file mode 100644
index 0000000..137821b
--- /dev/null
+++ b/tests/test_strategies/test_pfaffian_det.py
@@ -0,0 +1,78 @@
+import numpy as np
+import pytest
+import torch
+from torch.autograd import gradcheck
+
+from tests.configs import (
+    ATOL_APPROX_COMPARISON,
+    ATOL_MATRIX_COMPARISON,
+    N_RANDOM_TESTS_PER_CASE,
+    RTOL_APPROX_COMPARISON,
+    RTOL_MATRIX_COMPARISON,
+    TEST_SEED,
+)
+from torch_pfaffian.strategies.pfaffian_det import PfaffianDet
+from torch_pfaffian.strategies.pfaffian_fdbpf import PfaffianFDBPf
+
+_RNG = np.random.default_rng(TEST_SEED)
+
+
+def _skew(matrix: np.ndarray) -> np.ndarray:
+    return matrix - np.einsum("...ij->...ji", matrix)
+
+
+def _well_conditioned_skew_matrices(shapes: list[tuple[int, ...]], count: int, min_abs_det: float = 0.1):
+    matrices = []
+    for shape in shapes:
+        kept = 0
+        while kept < count:
+            candidate = _skew(_RNG.random(shape))
+            if np.all(np.abs(np.linalg.det(candidate)) > min_abs_det):
+                matrices.append(candidate)
+                kept += 1
+    return matrices
+
+
+_FORWARD_MATRICES = [
+    _skew(_RNG.random(shape)) for shape in [(8, 8), (16, 6, 6)] for _ in range(N_RANDOM_TESTS_PER_CASE)
+]
+_GRADCHECK_MATRICES = _well_conditioned_skew_matrices([(4, 4), (6, 6), (8, 4, 4)], N_RANDOM_TESTS_PER_CASE)
+
+
+class TestPfaffianDet:
+    @pytest.mark.parametrize("skew_matrix", _FORWARD_MATRICES)
+    def test_forward_square_of_pfaffian_matches_determinant(self, skew_matrix):
+        skew_tensor = torch.tensor(skew_matrix)
+        pfaffian = PfaffianDet.apply(skew_tensor)
+        determinant = torch.linalg.det(skew_tensor)
+        torch.testing.assert_close(pfaffian**2, determinant, atol=ATOL_MATRIX_COMPARISON, rtol=RTOL_MATRIX_COMPARISON)
+
+    @pytest.mark.parametrize("skew_matrix", _FORWARD_MATRICES)
+    def test_forward_matches_fdbpf(self, skew_matrix):
+        skew_tensor = torch.tensor(skew_matrix)
+        torch.testing.assert_close(
+            PfaffianDet.apply(skew_tensor),
+            PfaffianFDBPf.apply(skew_tensor),
+            atol=ATOL_MATRIX_COMPARISON,
+            rtol=RTOL_MATRIX_COMPARISON,
+        )
+
+    @pytest.mark.parametrize("skew_matrix", _GRADCHECK_MATRICES)
+    def test_backward_passes_gradcheck(self, skew_matrix):
+        skew_tensor = torch.tensor(skew_matrix, requires_grad=True)
+        assert gradcheck(
+            PfaffianDet.apply, (skew_tensor,), eps=1e-6, atol=ATOL_APPROX_COMPARISON, rtol=RTOL_APPROX_COMPARISON
+        )
+
+    @pytest.mark.parametrize("dtype", [torch.float32, torch.float64])
+    def test_forward_preserves_dtype(self, dtype):
+        matrix = torch.tensor(_skew(_RNG.random((6, 6))), dtype=dtype)
+        assert PfaffianDet.apply(matrix).dtype == dtype
+
+    def test_forward_supports_batched_input(self):
+        batched_matrix = torch.tensor(_skew(_RNG.random((5, 6, 6))))
+        pfaffian = PfaffianDet.apply(batched_matrix)
+        assert pfaffian.shape == batched_matrix.shape[:-2]
+        torch.testing.assert_close(
+            pfaffian**2, torch.linalg.det(batched_matrix), atol=ATOL_MATRIX_COMPARISON, rtol=RTOL_MATRIX_COMPARISON
+        )
diff --git a/tests/test_strategies/test_pfaffian_fdbpf.py b/tests/test_strategies/test_pfaffian_fdbpf.py
new file mode 100644
index 0000000..3f7246f
--- /dev/null
+++ b/tests/test_strategies/test_pfaffian_fdbpf.py
@@ -0,0 +1,103 @@
+import numpy as np
+import pytest
+import torch
+from torch.autograd import gradcheck
+
+from tests.configs import (
+    ATOL_APPROX_COMPARISON,
+    ATOL_MATRIX_COMPARISON,
+    N_RANDOM_TESTS_PER_CASE,
+    RTOL_APPROX_COMPARISON,
+    RTOL_MATRIX_COMPARISON,
+    TEST_SEED,
+)
+from torch_pfaffian.strategies.pfaffian_fdbpf import PfaffianFDBPf
+
+_RNG = np.random.default_rng(TEST_SEED)
+
+
+def _skew(matrix: np.ndarray) -> np.ndarray:
+    return matrix - np.einsum("...ij->...ji", matrix)
+
+
+def _random_skew_matrices(shapes: list[tuple[int, ...]], count: int) -> list[np.ndarray]:
+    return [_skew(_RNG.random(shape)) for shape in shapes for _ in range(count)]
+
+
+def _well_conditioned_skew_matrices(shapes: list[tuple[int, ...]], count: int, min_abs_det: float = 0.1):
+    # The gradient relies on the (pseudo)inverse, which is unstable for near-singular matrices, so the
+    # gradcheck inputs are restricted to skew matrices whose determinant is comfortably away from zero.
+    matrices = []
+    for shape in shapes:
+        kept = 0
+        while kept < count:
+            candidate = _skew(_RNG.random(shape))
+            if np.all(np.abs(np.linalg.det(candidate)) > min_abs_det):
+                matrices.append(candidate)
+                kept += 1
+    return matrices
+
+
+_FORWARD_MATRICES = _random_skew_matrices([(8, 8), (16, 6, 6), (18, 10, 10)], N_RANDOM_TESTS_PER_CASE)
+_GRADCHECK_MATRICES = _well_conditioned_skew_matrices([(4, 4), (6, 6), (8, 4, 4)], N_RANDOM_TESTS_PER_CASE)
+
+
+class TestPfaffianFDBPf:
+    @pytest.mark.parametrize("skew_matrix", _FORWARD_MATRICES)
+    def test_forward_square_of_pfaffian_matches_determinant(self, skew_matrix):
+        skew_tensor = torch.tensor(skew_matrix)
+        pfaffian = PfaffianFDBPf.apply(skew_tensor)
+        determinant = torch.linalg.det(skew_tensor)
+        torch.testing.assert_close(pfaffian**2, determinant, atol=ATOL_MATRIX_COMPARISON, rtol=RTOL_MATRIX_COMPARISON)
+
+    @pytest.mark.parametrize("skew_matrix", _GRADCHECK_MATRICES)
+    def test_backward_passes_gradcheck(self, skew_matrix):
+        skew_tensor = torch.tensor(skew_matrix, requires_grad=True)
+        assert gradcheck(
+            PfaffianFDBPf.apply, (skew_tensor,), eps=1e-6, atol=ATOL_APPROX_COMPARISON, rtol=RTOL_APPROX_COMPARISON
+        )
+
+    def test_forward_matches_known_pfaffian_magnitude(self):
+        # For the 2x2 skew matrix [[0, a], [-a, 0]] the Pfaffian is a, so its magnitude is |a|.
+        value = 0.7
+        matrix = torch.tensor([[0.0, value], [-value, 0.0]], dtype=torch.float64)
+        pfaffian = PfaffianFDBPf.apply(matrix)
+        expected = torch.tensor(abs(value), dtype=torch.float64)
+        torch.testing.assert_close(pfaffian, expected, atol=ATOL_MATRIX_COMPARISON, rtol=RTOL_MATRIX_COMPARISON)
+
+    def test_forward_returns_absolute_value_so_sign_is_not_recovered(self):
+        # The strategy computes |pf| via sqrt(|det|); the true Pfaffian of [[0, -a], [a, 0]] is -a,
+        # but the strategy returns +|a|.
+        value = 0.7
+        matrix = torch.tensor([[0.0, -value], [value, 0.0]], dtype=torch.float64)
+        pfaffian = PfaffianFDBPf.apply(matrix)
+        expected = torch.tensor(abs(value), dtype=torch.float64)
+        torch.testing.assert_close(pfaffian, expected, atol=ATOL_MATRIX_COMPARISON, rtol=RTOL_MATRIX_COMPARISON)
+
+    @pytest.mark.parametrize("dtype", [torch.float32, torch.float64])
+    def test_forward_preserves_dtype(self, dtype):
+        matrix = torch.tensor(_skew(_RNG.random((6, 6))), dtype=dtype)
+        assert PfaffianFDBPf.apply(matrix).dtype == dtype
+
+    def test_forward_supports_batched_input(self):
+        batched_matrix = torch.tensor(_skew(_RNG.random((5, 6, 6))))
+        pfaffian = PfaffianFDBPf.apply(batched_matrix)
+        assert pfaffian.shape == batched_matrix.shape[:-2]
+        torch.testing.assert_close(
+            pfaffian**2, torch.linalg.det(batched_matrix), atol=ATOL_MATRIX_COMPARISON, rtol=RTOL_MATRIX_COMPARISON
+        )
+
+    def test_forward_returns_zero_for_odd_dimension(self):
+        odd_matrix = torch.tensor(_skew(_RNG.random((3, 3))))
+        pfaffian = PfaffianFDBPf.apply(odd_matrix)
+        torch.testing.assert_close(pfaffian, torch.zeros_like(odd_matrix[..., 0, 0]))
+
+    def test_backward_returns_none_when_input_does_not_require_grad(self):
+        matrix = torch.tensor(_skew(_RNG.random((4, 4))))
+        pfaffian = PfaffianFDBPf.apply(matrix)
+
+        class _Context:
+            saved_tensors = (matrix, pfaffian)
+            needs_input_grad = (False,)
+
+        assert PfaffianFDBPf.backward(_Context(), torch.ones_like(pfaffian)) is None
diff --git a/tests/test_strategies/test_strategy.py b/tests/test_strategies/test_strategy.py
new file mode 100644
index 0000000..2ae0e93
--- /dev/null
+++ b/tests/test_strategies/test_strategy.py
@@ -0,0 +1,28 @@
+import torch
+
+from torch_pfaffian.strategies.strategy import PfaffianStrategy
+
+
+class _RecordingContext:
+    def save_for_backward(self, *tensors):
+        self.saved_tensors = tensors
+
+
+class TestPfaffianStrategy:
+    def test_class_attributes(self):
+        assert PfaffianStrategy.NAME == "PfaffianStrategy"
+        assert PfaffianStrategy.EPSILON == 1e-30
+
+    def test_setup_context_saves_inputs_and_output(self):
+        matrix = torch.eye(2, dtype=torch.float64)
+        pfaffian = torch.tensor(1.0, dtype=torch.float64)
+        context = _RecordingContext()
+        PfaffianStrategy.setup_context(context, (matrix,), pfaffian)
+        assert context.saved_tensors[0] is matrix
+        assert context.saved_tensors[1] is pfaffian
+
+    def test_base_forward_is_not_implemented(self):
+        assert PfaffianStrategy.forward(torch.eye(2, dtype=torch.float64)) is None
+
+    def test_base_backward_is_not_implemented(self):
+        assert PfaffianStrategy.backward(None, None) is None
diff --git a/tests/test_torch_pfaffian.py b/tests/test_torch_pfaffian.py
index d39f328..dcbea59 100644
--- a/tests/test_torch_pfaffian.py
+++ b/tests/test_torch_pfaffian.py
@@ -1,69 +1,32 @@
-import numpy as np
 import pytest
-
-from .configs import (
-    N_RANDOM_TESTS_PER_CASE,
-    TEST_SEED,
-    ATOL_APPROX_COMPARISON,
-    RTOL_APPROX_COMPARISON,
-    set_seed,
-)
 import torch
-from torch_pfaffian import pfaffian_strategy_map, get_pfaffian_function
-from torch.autograd import gradcheck
 
-set_seed(TEST_SEED)
+from torch_pfaffian import get_pfaffian_function, pfaffian_strategy_map
+from torch_pfaffian.strategies import PfaffianDet, PfaffianFDBPf
 
 
-@pytest.mark.parametrize(
-    "matrix, function_name",
-    [
-        (np.random.rand(*shape), function_name)
-        for shape in [
-            (8, 8),
-            (16, 6, 6),
-            (18, 10, 10),
-        ]
-        for _ in range(N_RANDOM_TESTS_PER_CASE)
-        for function_name in pfaffian_strategy_map.keys()
-    ]
-)
-def test_torch_pfaffian_gradcheck(matrix, function_name):
-    func = get_pfaffian_function(function_name)
-    skew_matrix = matrix - np.einsum("...ij->...ji", matrix)
-    skew_tensor = torch.tensor(skew_matrix, requires_grad=True)
-    assert gradcheck(
-        func, (skew_tensor,),
-        eps=1e-3,
-        atol=ATOL_APPROX_COMPARISON,
-        rtol=RTOL_APPROX_COMPARISON
-    )
+class TestTorchPfaffian:
+    def test_pfaffian_strategy_map_contains_registered_strategies(self):
+        assert PfaffianFDBPf.NAME.lower().strip() in pfaffian_strategy_map
+        assert PfaffianDet.NAME.lower().strip() in pfaffian_strategy_map
+        assert all(issubclass(cls, PfaffianFDBPf.__bases__[0]) for cls in pfaffian_strategy_map.values())
 
+    def test_get_pfaffian_function_returns_apply_of_registered_strategy(self):
+        function = get_pfaffian_function(PfaffianFDBPf.NAME)
+        assert function == PfaffianFDBPf.apply
 
-@pytest.mark.parametrize(
-    "matrix, function_name",
-    [
-        (np.random.rand(*shape), function_name)
-        for shape in [
-            (8, 8),
-            (16, 6, 6),
-            (18, 10, 10),
-        ]
-        for _ in range(N_RANDOM_TESTS_PER_CASE)
-        for function_name in pfaffian_strategy_map.keys()
-    ]
-)
-def test_torch_pfaffian_forward_against_det(matrix, function_name):
-    func = get_pfaffian_function(function_name)
-    skew_matrix = matrix - np.einsum("...ij->...ji", matrix)
-    skew_tensor = torch.tensor(skew_matrix, requires_grad=True)
+    def test_get_pfaffian_function_dispatches_to_pfaffian_det(self):
+        assert get_pfaffian_function(PfaffianDet.NAME) == PfaffianDet.apply
 
-    pf = func(skew_tensor)
-    det = torch.linalg.det(skew_tensor)
+    def test_get_pfaffian_function_default_name_is_callable(self):
+        function = get_pfaffian_function()
+        matrix = torch.tensor([[0.0, 1.0], [-1.0, 0.0]], dtype=torch.float64)
+        torch.testing.assert_close(function(matrix), matrix[..., 0, 1])
 
-    assert torch.allclose(
-        pf ** 2, det,
-        atol=ATOL_APPROX_COMPARISON,
-        rtol=RTOL_APPROX_COMPARISON
-    )
+    def test_get_pfaffian_function_is_case_and_whitespace_insensitive(self):
+        function = get_pfaffian_function(f"  {PfaffianFDBPf.NAME.upper()}  ")
+        assert function == PfaffianFDBPf.apply
 
+    def test_get_pfaffian_function_unknown_name_raises_value_error(self):
+        with pytest.raises(ValueError, match="Unknown strategy name"):
+            get_pfaffian_function("not_a_real_strategy")
diff --git a/tests/test_utils.py b/tests/test_utils.py
new file mode 100644
index 0000000..16f8505
--- /dev/null
+++ b/tests/test_utils.py
@@ -0,0 +1,30 @@
+from torch_pfaffian.utils import get_all_subclasses
+
+
+class TestUtils:
+    def test_get_all_subclasses_returns_empty_for_leaf(self):
+        class Leaf:
+            pass
+
+        assert get_all_subclasses(Leaf) == []
+
+    def test_get_all_subclasses_returns_direct_subclasses(self):
+        class Base:
+            pass
+
+        class Child(Base):
+            pass
+
+        assert get_all_subclasses(Base) == [Child]
+
+    def test_get_all_subclasses_returns_nested_subclasses(self):
+        class Base:
+            pass
+
+        class Child(Base):
+            pass
+
+        class GrandChild(Child):
+            pass
+
+        assert set(get_all_subclasses(Base)) == {Child, GrandChild}
diff --git a/uv.lock b/uv.lock
new file mode 100644
index 0000000..55df965
--- /dev/null
+++ b/uv.lock
@@ -0,0 +1,4379 @@
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+revision = 3
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+    "python_full_version < '3.11' and extra != 'extra-13-torchpfaffian-cpu' and extra != 'extra-13-torchpfaffian-cu128' and extra == 'extra-13-torchpfaffian-cu130'",
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