Update docs index benchmark coverage and fix version selector navigation

docs/_templates/sidebar/scroll-end.html

@@ -1,22 +1 @@
-{%- set _versions = versions | default([]) -%}
-{%- set _current = current_version | default({'name': version}) -%}
-
-{%- if _versions %}
-<div class="sidebar-tree">
-  <p class="caption" role="heading">
-    <span class="caption-text">Version</span>
-  </p>
-  <label for="version-selector" class="visually-hidden">Version</label>
-  <select
-    id="version-selector"
-    onchange="if (this.value) window.location.href = this.value;"
-    style="width: 100%;"
-  >
-    {%- for item in _versions %}
-    <option value="{{ item.url }}" {% if item.name == _current.name %}selected{% endif %}>
-      {{ item.name }}
-    </option>
-    {%- endfor %}
-  </select>
-</div>
-{%- endif %}
+{# Version selector moved to scroll-start.html (top of sidebar). #}

docs/_templates/sidebar/scroll-start.html

@@ -0,0 +1,38 @@
+{%- set _versions = versions | default([]) -%}
+{%- set _current = current_version | default({'name': version}) -%}
+{%- set _docs_base = '/BLADE' -%}
+
+{%- if _versions %}
+<div class="sidebar-tree">
+  <p class="caption" role="heading">
+    <span class="caption-text">Version</span>
+  </p>
+  <label for="version-selector" class="visually-hidden">Version</label>
+  <select
+    id="version-selector"
+    style="width: 100%;"
+    onchange="
+      if (!this.value) return;
+      const target = this.value;
+      if (/^https?:\/\//.test(target)) {
+        window.location.href = target;
+      } else if (target.startsWith('{{ _docs_base }}/')) {
+        window.location.href = target;
+      } else if (target.startsWith('/')) {
+        window.location.href = '{{ _docs_base }}' + target;
+      } else {
+        window.location.href = '{{ _docs_base }}/' + target;
+      }
+    "
+  >
+    {%- for item in _versions %}
+    <option
+      value="{{ item.url }}"
+      {% if item.name == _current.name %}selected{% endif %}
+    >
+      {{ item.name }}
+    </option>
+    {%- endfor %}
+  </select>
+</div>
+{%- endif %}

docs/index.rst

@@ -33,18 +33,9 @@ BLADE
 
 **BLADE** is a Python framework for benchmarking the Llm Assisted Design and Evolution of algorithms.
 **BLADE** (Benchmark suite for LLM-driven Automated Design and Evolution) provides a standardized benchmark suite for evaluating automatic algorithm design algorithms, particularly those generating metaheuristics by large language models (LLMs).
-It focuses on **continuous black-box optimization** and integrates a diverse set of **problems** and **methods**, facilitating fair and comprehensive benchmarking.
+It focuses on **heuristic optimization** and integrates a diverse set of **problems** and **methods**, facilitating fair and comprehensive benchmarking.
 
 
-🔥 News
---------
-
-- 2025.03 ✨✨ **iohblade v0.0.1 released**!
-
-
-Features
---------
-
 Documentation Guide
 -------------------
 
@@ -88,8 +79,90 @@ BLADE incorporates several benchmark function sets to provide a comprehensive ev
      - A collection of 1,000 pre-defined instances from the GECCO MA-BBOB competition, evaluating algorithm performance on diverse affine-combined functions. `Reference <https://iohprofiler.github.io/competitions>`_
      - 1,000
      - Yes
+   * - **HLP** (High-Level Properties)
+     - Generated benchmarks guided by high-level property combinations (e.g., separable, multimodality).
+     - Generator-Based
+     - Yes
 
-In addition, several real-world applications are included, such as several photonics problems.
+In addition, several real-world applications are included.
+
+Real World Benchmarks
+---------------------
+
+.. list-table::
+   :header-rows: 1
+   :widths: 30 70
+
+   * - Name
+     - Description
+   * - **Analysis**
+     -
+   * - Auto-Correlation 1
+     - Minimise ``max(g) / I^2`` for non-negative signals under fixed discretisation of ``[-1/4, 1/4]``.
+   * - Auto-Correlation 2
+     - Maximise ``L_2^2 / (L_1 · L_∞)`` for non-negative signals using discrete auto-convolution.
+   * - Auto-Correlation 3
+     - Minimise ``max(||g||) / I^2`` for real-valued signals with non-zero integral.
+   * - **AutoML**
+     -
+   * - AutoML Pipelines
+     - Generate and evaluate machine learning pipelines using scikit-learn.
+   * - **Combinatorics**
+     -
+   * - Erdős Minimum-Overlap Problem
+     - Minimise the suprenum overlap integral between complementary measurable functions.
+   * - Euclidean Steiner Tree Problem
+     - Minimise ``MST(points + Steiner points) / MST(points)`` ratio by adding optimal Steiner nodes.
+   * - Graph Colouring Problem
+     - Minimise the number of colours needed to colour graph nodes so adjacent nodes never share a colour.
+   * - **Fourier**
+     -
+   * - Fourier Uncertainty Inequality
+     - Minimise uncertainty bound for functions of form ``P(x)e^{-πx²}`` under Hermite constraints.
+   * - **Geometry**
+     -
+   * - Heilbronn (Unit Triangle)
+     - Maximise the area of the smallest triangle formed by 11 points in a unit-area triangle.
+   * - Heilbronn (Unit Convex Region)
+     - Maximise the area of the smallest triangle formed by 13-14 points in a unit-area convex region.
+   * - Kissing Number (11D)
+     - Maximise number of integer vectors satisfying high-dimensional kissing constraints.
+   * - Min/Max Distance Ratio
+     - Minimise squared ratio of maximum to minimum pairwise distances (2D/3D variants).
+   * - Spherical Code
+     - Maximise the minimum pairwise angle among 30 points on a unit sphere.
+   * - **Kernel Tuner**
+     -
+   * - Kernel Tuning Benchmark
+     - Evaluate metaheuristics for hardware kernel optimisation under constraints.
+   * - **Logistics**
+     -
+   * - Travelling Salesman Problem
+     - Minimise total tour distance visiting each 2D point exactly once.
+   * - Vehicle Routing Problem
+     - Minimise total travel distance for capacitated vehicles serving weighted customers.
+   * - **Matrix Multiplication via Tensor Decomposition**
+     -
+   * - Tensor CP Factorisation
+     - Find smallest CP rank enabling exact matrix multiplication under quantised factors.
+   * - **Number Theory**
+     -
+   * - Sums vs Differences
+     - Maximise ``c(U)`` measuring imbalance between sumsets and difference sets.
+   * - **Packing**
+     -
+   * - Circle Packing
+     - Maximise total packed circle area inside a circular container without overlap.
+   * - Hexagonal Packing
+     - Minimise area of smallest enclosing hexagon containing disjoint regular hexagons.
+   * - Rectangle Packing
+     - Pack disjoint circles inside a fixed-perimeter rectangle under containment constraints.
+   * - Unit Square Packing
+     - Pack disjoint circles inside a unit square while satisfying non-overlap constraints.
+
+These benchmarks are provided with ready-to-run instances in
+``run_benchmarks/``, while reusable benchmark definitions are organized under
+``iohblade/benchmarks`` by domain.
 
 Included Search Methods
 -----------------------