docs: add announcements landing page

docs/source/_static/announcements.css

@@ -0,0 +1,142 @@
+/* Scoped announcement styles for the Sphinx RTD theme. */
+
+#announcements > h1,
+#announcements > p,
+#announcements > .announcement-toolbar,
+#announcements > .announcement-grid,
+#announcements > .announcement-empty,
+#announcements > .announcement-pager,
+#announcements > .toctree-wrapper {
+  max-width: 860px;
+  margin-left: auto;
+  margin-right: auto;
+}
+
+.announcement-toolbar {
+  border: 1px solid #d6d8dc;
+  border-radius: 4px;
+  margin: 1.5rem 0;
+  padding: 1rem;
+  background: #f8f9fb;
+}
+
+.announcement-search-label {
+  display: block;
+  font-weight: 700;
+  margin-bottom: 0.35rem;
+}
+
+.announcement-search {
+  box-sizing: border-box;
+  width: 100%;
+  padding: 0.55rem 0.65rem;
+  border: 1px solid #b8bdc6;
+  border-radius: 4px;
+  font-size: 1rem;
+}
+
+.announcement-tags {
+  display: flex;
+  flex-wrap: wrap;
+  gap: 0.45rem;
+  margin-top: 0.75rem;
+}
+
+.announcement-tag {
+  border: 1px solid #9aa1ad;
+  border-radius: 999px;
+  padding: 0.28rem 0.65rem;
+  background: #fff;
+  color: #2f343d;
+  cursor: pointer;
+  font-size: 0.86rem;
+}
+
+.announcement-tag.is-active,
+.announcement-tag:hover {
+  border-color: #76b900;
+  background: #76b900;
+  color: #111;
+}
+
+.announcement-grid {
+  display: grid;
+  grid-template-columns: minmax(0, 1fr);
+  gap: 1rem;
+  margin: 1rem 0 1.25rem;
+}
+
+.announcement-card {
+  border-bottom: 1px solid #d6d8dc;
+  padding: 0 0 1rem;
+}
+
+.announcement-card:last-child {
+  border-bottom: 0;
+}
+
+.announcement-card h2 {
+  margin-top: 0.25rem;
+  font-size: 1.2rem;
+  line-height: 1.35;
+}
+
+.announcement-card p {
+  margin-bottom: 0.75rem;
+}
+
+.announcement-card-meta {
+  color: #6b7280;
+  font-size: 0.85rem;
+}
+
+.announcement-card-tags {
+  display: flex;
+  flex-wrap: wrap;
+  gap: 0.35rem;
+}
+
+.announcement-card-tags span {
+  border: 1px solid #d6d8dc;
+  border-radius: 999px;
+  color: #4b5563;
+  font-size: 0.78rem;
+  padding: 0.15rem 0.45rem;
+}
+
+.announcement-empty {
+  border-left: 4px solid #76b900;
+  padding-left: 0.75rem;
+}
+
+
+.announcement-pager {
+  align-items: center;
+  display: flex;
+  gap: 0.75rem;
+  justify-content: flex-end;
+  margin: 0 0 2rem;
+}
+
+.announcement-page-button {
+  border: 1px solid #9aa1ad;
+  border-radius: 4px;
+  background: #fff;
+  color: #2f343d;
+  cursor: pointer;
+  padding: 0.35rem 0.7rem;
+}
+
+.announcement-page-button:disabled {
+  cursor: not-allowed;
+  opacity: 0.45;
+}
+
+.announcement-page-status {
+  color: #4b5563;
+  font-size: 0.9rem;
+}
+
+.toctree-wrapper.compound:empty {
+  display: none;
+}

docs/source/_static/announcements.js

@@ -0,0 +1,105 @@
+document.addEventListener('DOMContentLoaded', () => {
+  const trimAnnouncementPostSidebar = () => {
+    if (!window.location.pathname.includes('/announcements/')) {
+      return;
+    }
+
+    const menu = document.querySelector('.wy-menu-vertical');
+    if (!menu) {
+      return;
+    }
+
+    menu.innerHTML = `
+      <p class="caption" role="heading"><span class="caption-text">Announcements</span></p>
+      <ul>
+        <li class="toctree-l1"><a class="reference internal" href="../index.html">Announcements</a></li>
+        <li class="toctree-l1"><a class="reference internal" href="../reference/1_modelopt_api.html">API Docs</a></li>
+      </ul>
+    `;
+  };
+
+  trimAnnouncementPostSidebar();
+
+  const search = document.querySelector('#announcement-search');
+  const cards = Array.from(document.querySelectorAll('.announcement-card')).sort((left, right) => {
+    return (right.dataset.date || '').localeCompare(left.dataset.date || '');
+  });
+  const tags = Array.from(document.querySelectorAll('.announcement-tag'));
+  const empty = document.querySelector('#announcement-empty');
+  const pager = document.querySelector('#announcement-pager');
+  const prev = document.querySelector('#announcement-prev');
+  const next = document.querySelector('#announcement-next');
+  const status = document.querySelector('#announcement-page-status');
+  const pageSize = 5;
+
+  cards.forEach((card) => card.parentNode.appendChild(card));
+  let activeTag = 'all';
+  let currentPage = 1;
+
+  if (!search || cards.length === 0) {
+    return;
+  }
+
+  const matchingCards = () => {
+    const query = search.value.trim().toLowerCase();
+    return cards.filter((card) => {
+      const haystack = [card.dataset.title, card.dataset.summary, card.dataset.tags].join(' ').toLowerCase();
+      const tagMatch = activeTag === 'all' || (card.dataset.tags || '').split(' ').includes(activeTag);
+      const searchMatch = !query || haystack.includes(query);
+      return tagMatch && searchMatch;
+    });
+  };
+
+  const update = () => {
+    const matches = matchingCards();
+    const pageCount = Math.max(1, Math.ceil(matches.length / pageSize));
+    currentPage = Math.min(currentPage, pageCount);
+    const start = (currentPage - 1) * pageSize;
+    const pageCards = new Set(matches.slice(start, start + pageSize));
+
+    cards.forEach((card) => {
+      card.hidden = !pageCards.has(card);
+    });
+
+    if (empty) {
+      empty.hidden = matches.length !== 0;
+    }
+
+    if (pager && prev && next && status) {
+      pager.hidden = matches.length <= pageSize;
+      prev.disabled = currentPage <= 1;
+      next.disabled = currentPage >= pageCount;
+      status.textContent = `Page ${currentPage} of ${pageCount}`;
+    }
+  };
+
+  tags.forEach((button) => {
+    button.addEventListener('click', () => {
+      activeTag = button.dataset.tag || 'all';
+      currentPage = 1;
+      tags.forEach((tag) => tag.classList.toggle('is-active', tag === button));
+      update();
+    });
+  });
+
+  search.addEventListener('input', () => {
+    currentPage = 1;
+    update();
+  });
+
+  if (prev) {
+    prev.addEventListener('click', () => {
+      currentPage -= 1;
+      update();
+    });
+  }
+
+  if (next) {
+    next.addEventListener('click', () => {
+      currentPage += 1;
+      update();
+    });
+  }
+
+  update();
+});

docs/source/announcements/dspark-vs-domino.rst

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+:orphan:
+
+DSpark vs Domino: Same DFlash Backbone, Different Correction Heads
+##################################################################
+
+:Author: ModelOpt Team
+:Date: June 29, 2026
+:Tags: speculative-decoding, dflash, dspark, domino, architecture
+
+DSpark (DeepSpec) and Domino both build on block-parallel DFlash draft generation but diverge sharply in their token-level correction heads. DSpark uses a stateless VanillaMarkov head that is fast and parallelizable during training; Domino uses a GRU that is more expressive but sequential at inference.
+
+Highlights
+**********
+
+* Both systems share the DFlash block-parallel backbone, so their parallel draft throughput starts from a similar foundation.
+* DSpark defaults to VanillaMarkov: stateless ``W1`` and ``W2`` embedding lookups with no hidden state to thread through.
+* Domino uses ``nn.GRU`` and carries recurrent state across draft positions.
+* Both correction heads are sequential at inference because ``x_{k-1}`` must be sampled before step ``k``.
+* DSpark adds a hardware-aware prefix scheduler through ``confidence_head``; Domino does not include this mechanism.
+
+Shared Foundation: DFlash Block-Parallel Backbone
+*************************************************
+
+Both systems use DFlash: a draft backbone that runs a single causal attention forward pass over all draft positions in parallel, producing per-position hidden states and base draft logits. This is the expensive step; the correction head adds token-level adjustment on top of those outputs.
+
+.. image:: assets/dspark_fig1.png
+   :alt: DSpark overall architecture and decoding cycle
+   :width: 100%
+
+Where They Diverge: The Correction Head
+***************************************
+
+DSpark uses a first-order Markov transition. For each draft position ``k``:
+
+.. code-block:: text
+
+   e_{k-1} = W1[x_{k-1}]
+   bias_k  = W2 * e_{k-1}
+   p_k     = softmax(U_k + bias_k)
+   x_k     ~ p_k
+
+The correction at position ``k`` depends only on ``x_{k-1}``; no RNN hidden state threads across steps. The dominant work is a table lookup and projection rather than a recurrent rollout.
+
+Domino uses a GRU correction head. A recurrent hidden state accumulates information about the draft prefix and is concatenated at readout:
+
+.. code-block:: text
+
+   gru_h_k = GRU(input_k, gru_h_{k-1})
+   p_k     = softmax(U_k + W * [h_k; gru_h_k])
+   x_k     ~ p_k
+
+.. image:: assets/domino_fig.png
+   :alt: Domino pipeline with a DFlash backbone and GRU causal correction head
+   :width: 100%
+
+The figure below compares training acceptance length on Qwen3-8B across the DFlash baseline, Domino GRU, and a DSpark implementation.
+
+.. image:: assets/dspark_domino_al_qwen3_8b.png
+   :alt: Training acceptance length on Qwen3-8B: DFlash baseline vs Domino GRU vs DSpark
+   :width: 100%
+
+Correction Head Overhead
+************************
+
+.. list-table::
+   :header-rows: 1
+
+   * - System
+     - Per-step compute
+     - State carried
+   * - DSpark VanillaMarkov
+     - ``W1[x_{k-1}]`` plus transition projection
+     - None
+   * - Domino GRU
+     - Full GRU cell over a high-dimensional input
+     - Recurrent hidden state
+
+Both heads must unroll left-to-right at inference. The practical difference is per-step cost: VanillaMarkov is much lighter, while the GRU can condition on a richer prefix history.
+
+Additional DSpark Machinery
+***************************
+
+DSpark includes a hardware-aware prefix scheduler through a confidence head. The scheduler estimates acceptance probability and selects how many draft tokens to submit for verification. It is a serving-time throughput optimization, not a draft-quality feature.
+
+For MoE models, DSpark checkpoints also include manifold-constrained Hyper-Connections. Dense models use the simpler backbone plus Markov-head path.
+
+.. image:: assets/dspark_fig7.png
+   :alt: DSpark throughput and TPS Pareto frontier
+   :width: 100%
+
+Takeaways
+*********
+
+#. DFlash draft generation is shared; the correction head is the main differentiator.
+#. VanillaMarkov is cheaper per step than GRU, although both are sequential at inference.
+#. GRU is more expressive, but the extra recurrence may not translate into a large acceptance-rate gain.
+#. DSpark's design is broader: VanillaMarkov, GatedMarkov, and RNN-style heads all fit the same family.
+#. The prefix scheduler affects serving throughput decisions, not correctness.
+
+Links
+*****
+
+* `DeepSpec / DSpark repo <https://github.com/deepseek-ai/DeepSpec>`_
+* `DeepSeek-V4-Pro-DSpark checkpoint <https://huggingface.co/deepseek-ai/DeepSeek-V4-Pro-DSpark>`_
+* `Domino repo <https://github.com/jianuo-huang/Domino>`_
+* `Domino checkpoint: Qwen3-8B-Domino-b16 <https://huggingface.co/Huang2020/Qwen3-8B-Domino-b16>`_
+* `ModelOpt PR #1710 <https://github.com/NVIDIA/Model-Optimizer/pull/1710>`_

docs/source/announcements/github-pages-announcements.rst

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+:orphan:
+
+Model Optimizer Announcements Are Moving to GitHub Pages
+#########################################################
+
+:Author: Model Optimizer Team
+:Date: July 13, 2026
+:Tags: release, docs, github-pages
+
+The Model Optimizer GitHub Pages site is expanding from API documentation into a lightweight announcement hub. The goal is to make releases, technical notes, examples, and deployment writeups easier to discover without introducing a separate publishing system.
+
+What Changes
+************
+
+* Announcements live in the documentation source and are reviewed through pull requests.
+* The landing page defaults to announcements.
+* Existing API documentation remains available from the Sphinx left navigation.
+* Announcement pages support tags, search, filtering, and embedded images.
+
+Authoring Flow
+**************
+
+Add a Sphinx page under ``docs/source/announcements/`` and link it from the announcements toctree in ``docs/source/index.rst``. The GitHub Pages workflow rebuilds the static site from committed source, so every announcement follows the same review path as code and docs.

docs/source/conf.py

@@ -116,7 +116,8 @@
 html_static_path = ["_static"]
 
 html_title = f"Model Optimizer {version}"
-html_css_files = ["custom.css"]
+html_css_files = ["custom.css", "announcements.css"]
+html_js_files = ["announcements.js"]
 html_permalinks_icon = "#"  # default icon not rendering properly
 
 # TODO: left here as reference for future