From a76d9bcedb0bca24dff7f1ba259a15e2416d023b Mon Sep 17 00:00:00 2001
From: Habib-Banwo <habib.ogunbanwo@arm.com>
Date: Tue, 1 Sep 2026 16:38:51 +0100
Subject: [PATCH 1/3] Update Namma Smart AI Apps Challenge page

---
 docs/Namma_Challenge_Page.html                | 304 +++++++++++++-----
 docs/_sass/components/_challengePage.scss     | 170 +++++++++-
 .../Arm_Brand_Logo_Guidelines_300x175px.jpg   | Bin 0 -> 238741 bytes
 docs/images/IIITB_logo1.jpg                   | Bin 0 -> 161244 bytes
 docs/images/Logo-CATS-GOK.jpg                 | Bin 0 -> 79949 bytes
 5 files changed, 398 insertions(+), 76 deletions(-)
 create mode 100644 docs/images/Arm_Brand_Logo_Guidelines_300x175px.jpg
 create mode 100644 docs/images/IIITB_logo1.jpg
 create mode 100644 docs/images/Logo-CATS-GOK.jpg

diff --git a/docs/Namma_Challenge_Page.html b/docs/Namma_Challenge_Page.html
index 50235217..e8d53a30 100644
--- a/docs/Namma_Challenge_Page.html
+++ b/docs/Namma_Challenge_Page.html
@@ -23,15 +23,32 @@
 <div class="challenge-wrapper namma-challenge">
   <header class="challenge-header">
     <div class="ch-header-content">
-      <div class="ch-badge"><span class="ch-badge-dot"></span><span>Namma Smart AI Apps Challenge</span></div>
-      <h1>Namma Smart AI Apps Challenge</h1>
-      <p class="ch-tagline">AI-Native Smart Applications for Digital Karnataka</p>
-      <p>In collaboration with Arm, IIIT Bangalore &amp; NASSCOM*</p>
-      <div class="ch-header-meta">
-        <div class="ch-meta-pill"><span class="ch-meta-label">Duration</span><span class="ch-meta-value">August – November 2026</span></div>
-        <div class="ch-meta-pill"><span class="ch-meta-label">Team Size</span><span class="ch-meta-value">1–3 Members</span></div>
-        <div class="ch-meta-pill"><span class="ch-meta-label">Participants</span><span class="ch-meta-value">Students &amp; Working Professionals / Developers</span></div>
-        <div class="ch-meta-pill"><span class="ch-meta-label">Eligibility</span><span class="ch-meta-value">Indian Nationals</span></div>
+      <div class="ch-hero-layout">
+        <div class="ch-hero-copy">
+          <div class="ch-badge"><span class="ch-badge-dot"></span><span>Namma Smart AI Apps Challenge</span></div>
+          <h1>Namma Smart AI Apps Challenge</h1>
+          <p class="ch-tagline">AI-Native Smart Applications for Digital Karnataka</p>
+          <p>In collaboration with Arm, IIIT Bangalore &amp; NASSCOM</p>
+          <div class="ch-header-meta">
+            <div class="ch-meta-pill"><span class="ch-meta-label">Duration</span><span class="ch-meta-value">September – November 2026</span></div>
+            <div class="ch-meta-pill"><span class="ch-meta-label">Team Size</span><span class="ch-meta-value">1–3 Members</span></div>
+            <div class="ch-meta-pill"><span class="ch-meta-label">Participants</span><span class="ch-meta-value">Students &amp; Working Professionals / Developers</span></div>
+            <div class="ch-meta-pill"><span class="ch-meta-label">Eligibility</span><span class="ch-meta-value">Indian Nationals</span></div>
+          </div>
+        </div>
+<div class="ch-logo-row" aria-label="Challenge partners">
+  <span class="ch-logo">
+    <img src="images/Arm_Brand_Logo_Guidelines_300x175px.jpg" alt="Arm">
+  </span>
+
+  <span class="ch-logo ch-logo--iiitb">
+    <img src="images/IIITB_logo1.jpg" alt="IIIT Bangalore">
+  </span>
+
+  <span class="ch-logo ch-logo--wide">
+    <img src="images/Logo-CATS-GOK.jpg" alt="NASSCOM AI Center of Excellence - CATS">
+  </span>
+</div>
       </div>
     </div>
   </header>
@@ -45,21 +62,21 @@ <h1>Namma Smart AI Apps Challenge</h1>
   <main>
     <section class="ch-section ch-section--featured">
       <h2>Program Overview</h2>
-      <p>The Namma Smart AI Apps Challenge is a national-level initiative jointly launched by Arm and IIIT Bangalore with support of NASSCOM, to build a skilled talent pipeline in on-device Edge AI app development.</p>
-      <p>Unlike prior student-only skilling initiatives, the program is open to both student developers (UG/PG) and working professionals, recognising that Edge AI application-development talent spans academia and industry.</p>
+      <p>The Namma Smart AI Apps Challenge is a national initiative from Arm and IIIT Bangalore, supported by NASSCOM, that helps participants build practical skills in on-device Edge AI application development.</p>
+      <p>The challenge is open to both student developers (UG/PG) and working professionals, reflecting the breadth of Edge AI application-development talent across academia and industry.</p>
       <p>Participants will build real-world applications with societal impact across healthcare, education, telemedicine, gaming, and application modernisation, with mentorship from Arm engineers and IIIT Bangalore faculty.</p>
-      <p>The program officially launches on 15 August 2026 and culminates in a live demo day and winners' announcement at Bangalore Tech Summit 2026 on 19 November 2026.</p>
+      <p>The program officially launches on 2 September 2026 and culminates in a live demo day and winners' announcement at Bengaluru Tech Summit 2026 on 19 November 2026.</p>
     </section>
 
     <section class="ch-section">
       <h2>Program Objectives</h2>
       <ul>
         <li>Build practical, industry-relevant skills in on-device AI and app development.</li>
-        <li>Create a joint academia–industry talent pipeline by inviting both students and working professionals into the challenge.</li>
+        <li>Create a joint academia–industry talent pipeline for students and working professionals.</li>
         <li>Translate technology into real, deployable applications addressing genuine societal problems.</li>
         <li>Establish IIIT Bangalore as an academic anchor for Edge AI curriculum, mentorship, and lab infrastructure.</li>
-        <li>Build a structured mentorship pipeline connecting participants directly with Arm engineers and IIIT Bangalore faculty.</li>
-        <li>Encourage cross-pollination between academic experimentation and industry engineering discipline.</li>
+        <li>Connect participants directly with Arm engineers and IIIT Bangalore faculty through structured mentorship.</li>
+        <li>Encourage collaboration between academic experimentation and industry engineering discipline.</li>
         <li>Strengthen collaboration between Arm, NASSCOM, and IIIT Bangalore.</li>
       </ul>
     </section>
@@ -80,20 +97,22 @@ <h2>What Makes This Challenge Different</h2>
 
     <section class="ch-section">
       <h2>Program Flow &amp; Timeline</h2>
-      <table class="ch-table ch-timeline"><thead><tr><th>Stage</th><th>Date</th><th>Activity</th></tr></thead><tbody>
-        <tr><td>Launch</td><td>15 August 2026</td><td>Portal live and promotion via official handles</td></tr>
-        <tr><td>Registration</td><td>15 August – 15 September 2026</td><td>Team formation and selection of problem statements</td></tr>
-        <tr><td>Phase 1 — Ideation</td><td>1 September – 30 September 2026</td><td>Mentoring by experts, project baseline development, project report submission and demo video</td></tr>
-        <tr><td>Phase 1 Evaluation &amp; Shortlist</td><td>1 October – 10 October 2026</td><td>Assessment of submitted projects by experts and shortlisting</td></tr>
-        <tr><td>Phase 2 — PoC Development</td><td>11 October – 30 October 2026</td><td>Full application development for shortlisted teams</td></tr>
-        <tr><td>Phase 2 Evaluation</td><td>1 November – 15 November 2026</td><td>Teams showcase a live virtual demo of their projects</td></tr>
-        <tr><td>Winners' Announcement</td><td>19 November 2026</td><td>Cash prize distribution at Bangalore Tech Summit 2026</td></tr>
-      </tbody></table>
+      <div class="ch-table-scroll" tabindex="0" role="region" aria-label="Program timeline">
+        <table class="ch-table ch-timeline"><thead><tr><th>Milestone</th><th>Timing</th><th>What Happens</th></tr></thead><tbody>
+          <tr><td>Launch</td><td>2nd September 2026</td><td>The challenge portal opens</td></tr>
+          <tr><td>Registration</td><td>2nd Sept – 20 Sept 2026</td><td>Participants register their teams and select a problem statement</td></tr>
+          <tr><td>Phase 1 – Ideation</td><td>20th – 30th Sept 2026</td><td>Develop your initial solution with mentoring support and submit your report and demo video</td></tr>
+          <tr><td>Phase 1 Evaluation &amp; Shortlist</td><td>1 – 10 Oct 2026</td><td>Submissions are evaluated and shortlisted teams are selected for Phase 2</td></tr>
+          <tr><td>Phase 2 – PoC Development</td><td>11 – 30 Oct 2026</td><td>Shortlisted teams develop their full proof-of-concept application</td></tr>
+          <tr><td>Phase 2 Evaluation</td><td>1 – 15 Nov 2026</td><td>Shortlisted teams present their solutions through a live virtual demo</td></tr>
+          <tr><td>Winners' Announcement</td><td>19 Nov 2026</td><td>Winners are announced at Bengaluru Tech Summit 2026</td></tr>
+        </tbody></table>
+      </div>
     </section>
 
     <section class="ch-section phase-one">
       <h2>Phase 1 — Open Qualifying Round</h2>
-      <p>All registered participants submit a project report with:</p>
+      <p>In Phase 1, all registered participants submit a project report containing:</p>
       <div class="phase-deliverables">
         <div class="phase-deliverable"><div class="phase-deliverable-title">Problem Statement Alignment</div><div class="phase-deliverable-text">Clear articulation of the chosen problem and proposed solution approach.</div></div>
         <div class="phase-deliverable"><div class="phase-deliverable-title">Technical Architecture</div><div class="phase-deliverable-text">System design, model selection and Arm backend selection.</div></div>
@@ -117,47 +136,195 @@ <h2>Phase 2 — App Development Round (Invitation Only)</h2>
 
     <section class="ch-section ch-problem-statements">
       <h2>Project Problem Statements</h2>
-      <details class="ch-accordion"><summary><span class="ch-acc-title">1. AI Consultation Preparation Assistant for Patients</span><span class="ch-acc-domain">Domain: Healthcare</span></summary><div class="ch-acc-body">
+      <details class="ch-accordion"><summary><span class="ch-acc-title">1. AI Consultation Preparation Assistant for Patients</span> <span class="ch-acc-domain">Domain: Healthcare</span></summary><div class="ch-acc-body">
         <p><strong>Domain:</strong> Healthcare<br><strong>Applicable for:</strong> Students</p>
-        <p>In Karnataka's Tier-2 and Tier-3 towns and rural taluks, patients accessing online consultations under state public health initiatives such as Arogya Karnataka through Centre for Applied AI for Tech Solutions (CATS) often struggle to organise their symptoms, medical history and questions before the call, especially where the consultation is conducted in English while the patient is more comfortable in Kannada.</p>
-        <p>Build a desktop application that accepts a patient's symptoms, health notes, prescriptions and medical reports and generates:</p>
-        <ul><li>a patient-friendly summary</li><li>relevant questions to ask the doctor</li><li>a consultation checklist</li></ul>
-        <p>Kannada-language support must be treated as a core feature.</p>
-        <p>The application must run entirely on-device to protect sensitive health data and should be achievable by a student team using a small language model or ONNX-based text summarization pipeline.</p>
+        <p>In Karnataka's Tier-2 and Tier-3 towns and rural taluks, patients accessing online consultations can struggle to organize symptoms, medical history, prescriptions, reports and questions before a consultation, particularly when the consultation is in English and the patient is more comfortable in Kannada or any other Indian language.</p>
+        <p>Build an Arm64-native consultation-preparation application that uses AI to transform patient-provided notes and documents into a patient-friendly summary, relevant questions for the doctor and a consultation checklist, with Kannada or any other Indian language as a core experience.</p>
+        <p>The solution should prioritize privacy-conscious processing, useful offline/local workflows where feasible, and efficient Arm64 execution.</p>
+        <p>Students may develop, test and demonstrate the solution using the free Azure account and B2pts v2 Arm-based burstable VM allocation (up to 750 hours/month).</p>
+        <p>Physical WoA hardware, NPU access and SME2 are not prerequisites.</p>
+        <p>Teams should document which parts run locally on the Arm VM, any cloud services used, latency and resource usage, and how the architecture could map to a future Windows on Arm client.</p>
+        <p>Teams are encouraged to use ONNX Runtime for deploying and running AI models efficiently on Arm64 and explore Arm KleidiAI integration/optimizations, where applicable, to build an Arm-optimized AI software stack.</p>
+        <p>The Azure Arm64 VM can be used for development, model conversion, compilation, functional validation, inference and performance/resource analysis.</p>
+        <p>Teams should document:</p>
+        <ul>
+          <li>AI models and runtimes used</li>
+          <li>ONNX Runtime/KleidiAI integration where applicable</li>
+          <li>components running locally on the Arm VM</li>
+          <li>any external cloud services used</li>
+          <li>latency and resource utilization</li>
+          <li>how the architecture could subsequently map to a Windows on Arm client</li>
+          <li>potential path to SME2-based optimization on compatible hardware</li>
+        </ul>
+        <h3>Checklist</h3>
+        <div class="ch-table-scroll" tabindex="0" role="region" aria-label="Problem statement 1 checklist">
+          <table class="ch-table ch-table-compact ch-ps-table ch-ps-checklist"><thead><tr><th>Participant category</th><th>Azure Arm64 access</th><th>AI inference optimization</th><th>CPU optimization</th><th>Use of KleidiAI</th><th>Primary technical validation</th></tr></thead><tbody>
+            <tr><td>Students</td><td>Free B2pts v2 Arm VM; $100 Azure credit for student account.</td><td>Recommended</td><td>Recommended</td><td>Optional / where applicable</td><td>Arm64-native AI workflow; latency; CPU/memory efficiency; privacy/offline design; Windows readiness</td></tr>
+          </tbody></table>
+        </div>
+        <h3>Evaluation Criteria</h3>
+        <div class="ch-table-scroll" tabindex="0" role="region" aria-label="Problem statement 1 evaluation criteria">
+          <table class="ch-table ch-table-compact ch-ps-table ch-ps-evaluation"><thead><tr><th>Evaluation Criterion</th><th>Weight</th><th>What Judges Assess</th></tr></thead><tbody>
+            <tr><td>Usefulness &amp; Patient Experience</td><td>20%</td><td>Quality/relevance of summaries, questions and checklist; Kannada/other Indian language-first usability.</td></tr>
+            <tr><td>AI Output Quality &amp; Latency</td><td>20%</td><td>Faithfulness, relevance and end-to-end response time on the stated Arm VM environment.</td></tr>
+            <tr><td>Privacy, Offline/Local Design</td><td>15%</td><td>Privacy-conscious architecture and degree to which core processing can operate locally or with limited connectivity.</td></tr>
+            <tr><td>Arm64-Native Implementation</td><td>15%</td><td>Arm64-native components, dependency choices and avoidance of unnecessary x64-only components.</td></tr>
+            <tr><td>Arm VM Efficiency &amp; Reproducibility</td><td>15%</td><td>CPU/memory utilization, responsiveness and repeatable measurements on the Azure Arm VM.</td></tr>
+            <tr><td>Engineering Quality &amp; Windows Readiness</td><td>15%</td><td>Reliability, testing, packaging/design readiness and a credible path to a Windows on Arm client.</td></tr>
+          </tbody></table>
+        </div>
       </div></details>
-      <details class="ch-accordion"><summary><span class="ch-acc-title">2. Personalized Study Guide, Planner and Knowledge Copilot for Students</span><span class="ch-acc-domain">Domain: EdTech</span></summary><div class="ch-acc-body">
+      <details class="ch-accordion"><summary><span class="ch-acc-title">2. Personalized Study Guide, Planner and Knowledge Copilot for Students</span> <span class="ch-acc-domain">Domain: EdTech</span></summary><div class="ch-acc-body">
         <p><strong>Domain:</strong> EdTech<br><strong>Applicable for:</strong> Students</p>
-        <p>Students across Karnataka often have study material spread across notes, PDFs, lecture summaries and slides with no simple way to search it, test understanding or plan revision.</p>
-        <p>Build a desktop application that ingests a student's study material and allows the student to:</p>
-        <ul><li>ask questions</li><li>generate flashcards</li><li>summarize topics</li><li>produce a personalized revision plan</li></ul>
-        <p>The scope should remain lightweight and focus on a clean, usable local AI workflow rather than complex system integrations.</p>
+        <p>Students often work with study material scattered across notes, PDFs, lecture summaries and slides, with no simple way to search, test understanding or plan revision.</p>
+        <p>Build an Arm64-native study companion that ingests a student's material and uses AI to:</p>
+        <ul><li>answer questions</li><li>summarize topics</li><li>generate flashcards or quizzes</li><li>create a personalized revision plan</li></ul>
+        <p>The experience should remain useful with limited connectivity and should emphasize efficient Arm64 inference and a clean user workflow rather than complex integrations.</p>
+        <p>Students may use the free Azure account and B2pts v2 Arm-based burstable VM allocation (up to 750 hours/month) for development, testing and demonstration.</p>
+        <p>Physical WoA hardware, NPU access and SME2 are not mandatory.</p>
+        <p>Teams should report AI latency, throughput/resource utilization and reproducible Arm64 measurements, and explain how the implementation can transition to a native Windows on Arm application.</p>
+        <p>Teams are encouraged to use ONNX Runtime for efficient AI model deployment and inference on Arm64 and explore Arm KleidiAI integration/optimizations, where applicable, to build an Arm-optimized AI software stack.</p>
+        <p>The Azure Arm64 VM can be used for model conversion, development, compilation, functional validation, inference, benchmarking and resource-utilization analysis.</p>
+        <p>Teams should report:</p>
+        <ul>
+          <li>AI models and runtimes used</li>
+          <li>ONNX Runtime/KleidiAI integration where applicable</li>
+          <li>inference latency</li>
+          <li>throughput</li>
+          <li>resource utilization</li>
+          <li>reproducible Arm64 measurements</li>
+          <li>path to a native Windows on Arm client</li>
+          <li>potential path to SME2-based optimization on compatible hardware</li>
+        </ul>
+        <h3>Checklist</h3>
+        <div class="ch-table-scroll" tabindex="0" role="region" aria-label="Problem statement 2 checklist">
+          <table class="ch-table ch-table-compact ch-ps-table ch-ps-checklist"><thead><tr><th>Participant category</th><th>Azure Arm64 access</th><th>AI inference optimization</th><th>CPU optimization</th><th>Use of KleidiAI</th><th>Primary technical validation</th></tr></thead><tbody>
+            <tr><td>Students</td><td>Free B2pts v2 Arm VM; $100 Azure credit for student account.</td><td>Recommended</td><td>Recommended</td><td>Optional / where applicable</td><td>Arm64-native AI workflow; latency/throughput; CPU/memory efficiency; local study-data workflow</td></tr>
+          </tbody></table>
+        </div>
+        <h3>Evaluation Criteria</h3>
+        <div class="ch-table-scroll" tabindex="0" role="region" aria-label="Problem statement 2 evaluation criteria">
+          <table class="ch-table ch-table-compact ch-ps-table ch-ps-evaluation"><thead><tr><th>Evaluation Criterion</th><th>Weight</th><th>What Judges Assess</th></tr></thead><tbody>
+            <tr><td>Learning Usefulness &amp; UX</td><td>20%</td><td>Effectiveness of Q&amp;A, summaries, flashcards/quizzes and revision planning.</td></tr>
+            <tr><td>AI Quality &amp; Latency</td><td>20%</td><td>Grounding in supplied study material and measured response time.</td></tr>
+            <tr><td>Offline / Limited-Connectivity Capability</td><td>15%</td><td>Usefulness when network access is limited and sensible data handling.</td></tr>
+            <tr><td>Arm64-Native Implementation</td><td>15%</td><td>Native Arm64 build/dependencies and efficient execution.</td></tr>
+            <tr><td>Arm VM Performance</td><td>15%</td><td>Throughput, CPU/memory utilization and reproducible performance evidence.</td></tr>
+            <tr><td>Engineering Quality &amp; Windows Readiness</td><td>15%</td><td>File ingestion, reliability, testing, documentation and path to native Windows on Arm.</td></tr>
+          </tbody></table>
+        </div>
       </div></details>
-      <details class="ch-accordion"><summary><span class="ch-acc-title">3. Telemedicine Consultation Copilot with Local AI Summarization and Clinical Workflow Support</span><span class="ch-acc-domain">Domain: Telemedicine</span></summary><div class="ch-acc-body">
-        <p><strong>Domain:</strong> Telemedicine<br><strong>Applicable for:</strong> Working Professionals / Developers</p>
-        <p>Karnataka's public health system includes online consultations connecting patients in rural districts to doctors at taluk and district hospitals.</p>
-        <p>Clinicians spend significant time documenting symptoms, medications, investigations and follow-up actions.</p>
-        <p>Develop a desktop application that:</p>
-        <ul><li>captures or imports consultation transcripts</li><li>extracts relevant clinical details</li><li>generates a structured clinician-facing note</li><li>generates a separate patient-friendly summary</li></ul>
-        <p>Kannada must be supported as a first-class language.</p>
-        <p>Advanced teams may extend the solution with:</p>
-        <ul><li>additional regional-language support</li><li>retrieval from medical reference documents</li><li>structured follow-up planning</li></ul>
+      <details class="ch-accordion"><summary><span class="ch-acc-title">3. Telemedicine Consultation Copilot with Local AI Summarization and Clinical Workflow Support</span> <span class="ch-acc-domain">Domain: Healthcare</span></summary><div class="ch-acc-body">
+        <p><strong>Domain:</strong> Healthcare<br><strong>Applicable for:</strong> Working Professionals / Developers</p>
+        <p>Clinicians conducting remote consultations can spend significant time documenting symptoms, medications, investigations and follow-up actions, reducing time available for patient care and increasing the risk of omissions.</p>
+        <p>Develop an Arm64-native telemedicine consultation copilot that captures or imports consultation transcripts, extracts relevant clinical details and generates both:</p>
+        <ul><li>a structured clinician-facing note</li><li>a separate patient-friendly summary</li></ul>
+        <p>Kannada/other Indian language should be a first-class language.</p>
+        <p>Optional extensions include:</p>
+        <ul><li>additional regional languages</li><li>retrieval from medical reference documents</li><li>structured follow-up planning</li></ul>
+        <p>Because a physical WoA device is not assumed, Phase 1 should be developed and validated on the free Azure B2pts v2 Arm-based VM environment (up to 750 hours/month).</p>
+        <p>Shortlisted Phase 2 teams may use the stated $200 Azure credit to scale or deepen the PoC.</p>
+        <p>SME2 is not required or scored.</p>
+        <p>Evaluation should focus on available Arm64-native execution and measurable CPU/resource performance rather than unavailable device-specific NPU/battery metrics, including:</p>
+        <ul><li>Arm64-native execution</li><li>output quality</li><li>latency</li><li>reproducibility</li><li>privacy-conscious architecture</li><li>CPU/resource optimization</li><li>practical workflow usefulness</li></ul>
+        <p>Teams are encouraged to use ONNX Runtime for efficient AI model deployment and inference on Arm64 and explore Arm KleidiAI integration/optimizations, where applicable, to build an Arm-optimized AI software stack.</p>
+        <p>The Azure Arm64 VM may be used for model conversion, development, compilation, functional validation, inference, benchmarking and resource-utilization analysis.</p>
+        <p>Teams should also document:</p>
+        <ul><li>AI models and runtimes</li><li>ONNX Runtime/KleidiAI integration where applicable</li><li>transition path to a Windows on Arm client</li><li>potential path to SME2-based optimization on compatible hardware</li></ul>
+        <h3>Checklist</h3>
+        <div class="ch-table-scroll" tabindex="0" role="region" aria-label="Problem statement 3 checklist">
+          <table class="ch-table ch-table-compact ch-ps-table ch-ps-checklist"><thead><tr><th>Participant category</th><th>Azure Arm64 access</th><th>AI inference optimization</th><th>CPU optimization</th><th>Use of KleidiAI</th><th>Primary technical validation</th></tr></thead><tbody>
+            <tr><td>Working Professionals / Developers</td><td>Free B2pts v2 Arm VM; $200 Azure credit for new account.</td><td>Recommended</td><td>Required</td><td>Optional / where applicable</td><td>Arm64-native execution; CPU optimization; AI latency/throughput/memory; reproducibility; clinical workflow quality</td></tr>
+          </tbody></table>
+        </div>
+        <h3>Evaluation Criteria</h3>
+        <div class="ch-table-scroll" tabindex="0" role="region" aria-label="Problem statement 3 evaluation criteria">
+          <table class="ch-table ch-table-compact ch-ps-table ch-ps-evaluation"><thead><tr><th>Evaluation Criterion</th><th>Weight</th><th>What Judges Assess</th></tr></thead><tbody>
+            <tr><td>Clinical Workflow Usefulness &amp; Output Quality</td><td>25%</td><td>Completeness and practical usefulness of extracted details, clinician note and patient summary; no diagnosis requirement.</td></tr>
+            <tr><td>AI Accuracy &amp; Latency</td><td>20%</td><td>Faithfulness to consultation input, extraction/summarization quality and measured latency.</td></tr>
+            <tr><td>Language, Privacy &amp; Local Design</td><td>15%</td><td>Kannada/other Indian language-first workflow, optional regional languages and privacy-conscious processing.</td></tr>
+            <tr><td>Arm64-Native Implementation &amp; CPU Optimization</td><td>15%</td><td>Native Arm64 components, optimized CPU execution and dependency quality.</td></tr>
+            <tr><td>Azure Arm Performance &amp; Reproducibility</td><td>10%</td><td>Repeatable latency/throughput/memory/CPU measurements on the provided Arm VM.</td></tr>
+            <tr><td>Engineering Quality &amp; Scalability</td><td>15%</td><td>Robustness, testing, documentation, deployment quality and effective use of Phase 2 Azure resources.</td></tr>
+          </tbody></table>
+        </div>
       </div></details>
-      <details class="ch-accordion"><summary><span class="ch-acc-title">4. Real-Time AI Gaming Coach and Multimedia Highlights Studio</span><span class="ch-acc-domain">Domain: Gaming</span></summary><div class="ch-acc-body">
+      <details class="ch-accordion"><summary><span class="ch-acc-title">4. Real-Time AI Gaming Coach and Multimedia Highlights Studio</span> <span class="ch-acc-domain">Domain: Gaming</span></summary><div class="ch-acc-body">
         <p><strong>Domain:</strong> Gaming<br><strong>Applicable for:</strong> Working Professionals / Developers</p>
-        <p>Build a desktop application that analyses recorded gameplay clips or live screen/video input and:</p>
-        <ul><li>detects key events</li><li>tags important moments</li><li>generates coaching insights</li><li>generates highlight summaries</li><li>combines computer vision with audio/text metadata</li><li>presents findings through a local assistant interface</li><li>surfaces mistakes, achievements and strategy recommendations</li></ul>
-        <p>This problem targets experienced developers because it requires real-time video processing, multi-model orchestration and a performance-aware desktop UX.</p>
+        <p>Build an Arm64-native AI gaming and multimedia analysis application that processes recorded gameplay clips or suitable streamed/video inputs, detects and tags key events, and generates coaching insights and highlight summaries through an assistant-style experience.</p>
+        <p>The pipeline may combine computer vision with audio/text metadata and should be profiled and optimized for Arm64 execution.</p>
+        <p>Phase 1 should run on the free Azure B2pts v2 Arm-based VM environment (up to 750 hours/month), with the stated $200 Azure credit available to shortlisted teams in Phase 2.</p>
+        <p>SME2, NPU utilization, battery measurements and device-only real-time capture are not mandatory.</p>
+        <p>Teams should instead demonstrate measurable improvements through reproducible Arm64 profiling, for example:</p>
+        <ul><li>inference latency</li><li>throughput</li><li>CPU utilization</li><li>memory footprint</li><li>processing time per minute of video</li><li>cost-efficiency</li></ul>
+        <p>Use a clearly defined baseline where a valid comparison can be produced.</p>
+        <p>Teams are encouraged to use ONNX Runtime for efficient AI model deployment and inference on Arm64 and explore Arm KleidiAI integration/optimizations, where applicable, to build an Arm-optimized AI software stack.</p>
+        <p>The Azure Arm64 VM may be used for model conversion, development, compilation, functional validation, inference, benchmarking and resource-utilization analysis.</p>
+        <p>Teams should also document:</p>
+        <ul><li>AI models and runtimes</li><li>ONNX Runtime/KleidiAI integration where applicable</li><li>path to a Windows on Arm client</li><li>potential path to SME2-based optimization on compatible hardware</li></ul>
+        <h3>Checklist</h3>
+        <div class="ch-table-scroll" tabindex="0" role="region" aria-label="Problem statement 4 checklist">
+          <table class="ch-table ch-table-compact ch-ps-table ch-ps-checklist"><thead><tr><th>Participant category</th><th>Azure Arm64 access</th><th>AI inference optimization</th><th>CPU optimization</th><th>Use of KleidiAI</th><th>Primary technical validation</th></tr></thead><tbody>
+            <tr><td>Working Professionals / Developers</td><td>Free B2pts v2 Arm VM; $200 Azure credit for new account.</td><td>Recommended</td><td>Required</td><td>Optional / where applicable</td><td>Arm64 profiling; media/AI throughput; latency; CPU/memory efficiency; repeatable baseline comparison where valid</td></tr>
+          </tbody></table>
+        </div>
+        <h3>Evaluation Criteria</h3>
+        <div class="ch-table-scroll" tabindex="0" role="region" aria-label="Problem statement 4 evaluation criteria">
+          <table class="ch-table ch-table-compact ch-ps-table ch-ps-evaluation"><thead><tr><th>Evaluation Criterion</th><th>Weight</th><th>What Judges Assess</th></tr></thead><tbody>
+            <tr><td>Arm64 Performance Improvement</td><td>25%</td><td>Measured improvement against a clearly defined and valid baseline using cloud-accessible metrics.</td></tr>
+            <tr><td>AI / Multimedia Pipeline Quality</td><td>20%</td><td>Robust event detection, tagging, coaching insights and highlight generation.</td></tr>
+            <tr><td>Throughput &amp; Resource Efficiency</td><td>15%</td><td>Inference latency, throughput, CPU utilization, memory footprint and processing efficiency.</td></tr>
+            <tr><td>User Experience &amp; Usefulness</td><td>15%</td><td>Actionable output and clear value to gamers/creators; demo usability.</td></tr>
+            <tr><td>Native Arm64 Completeness</td><td>10%</td><td>Arm64-native application/pipeline and dependencies where feasible.</td></tr>
+            <tr><td>Engineering &amp; Benchmark Reproducibility</td><td>15%</td><td>Repeatable methodology, profiling evidence, testing, reliability and documentation.</td></tr>
+          </tbody></table>
+        </div>
       </div></details>
-      <details class="ch-accordion"><summary><span class="ch-acc-title">5. Desktop Application Migration &amp; Optimization with Performance Benchmarking Suite</span><span class="ch-acc-domain">Domain: App Migration</span></summary><div class="ch-acc-body">
-        <p><strong>Domain:</strong> App Migration<br><strong>Applicable for:</strong> Students &amp; Working Professionals / Developers</p>
-        <p>Select an existing open-source or self-built x86 Windows desktop application and migrate it to run natively on Windows on Arm (Arm64).</p>
-        <p>Address real migration issues where relevant, including:</p>
-        <ul><li>unavailable or x86-only dependencies</li><li>native library recompilation</li><li>driver or SDK incompatibilities</li><li>installer or packaging changes</li></ul>
-        <p>The deliverable must include both:</p><ol><li>a working native Arm64 port</li><li>a rigorous and repeatable benchmarking suite</li></ol>
-        <p>The benchmarking suite must compare x86-emulated and native Arm64 versions across:</p><ul><li>performance</li><li>power consumption</li><li>memory utilisation</li></ul>
-        <p>Student teams may choose a smaller, self-contained application such as a utility, lightweight productivity app or sample ONNX/AI inference app.</p>
-        <p>Professional teams are encouraged to attempt a larger, dependency-heavy or AI-inference-capable application demonstrating production complexity.</p>
+      <details class="ch-accordion"><summary><span class="ch-acc-title">5. Desktop Application Migration &amp; Optimization with Performance Benchmarking Suite</span> <span class="ch-acc-domain">Domain: App Migration / Open Source / Performance</span></summary><div class="ch-acc-body">
+        <p><strong>Domain:</strong> App Migration / Open Source / Performance<br><strong>Applicable for:</strong> Students &amp; Working Professionals / Developers</p>
+        <p>Select an existing, meaningful Windows application, open-source library, runtime, framework, SDK or developer tool that people or developers actually use and advance it toward a complete native Arm64 experience.</p>
+        <p>Teams may pursue:</p>
+        <h3>Port It Native</h3>
+        <p>Port a popular application or component to Arm64.</p>
+        <h3>Open Source Hero / Unlock the Ecosystem</h3>
+        <p>Make an important dependency or developer technology Arm64-native and unlock downstream applications.</p>
+        <h3>Faster on Arm</h3>
+        <p>Optimize an existing workload and demonstrate a meaningful native Arm64 performance improvement.</p>
+        <p>Resolve real migration issues such as:</p>
+        <ul><li>x86/x64-only dependencies</li><li>native library recompilation</li><li>build-system changes</li><li>CI/testing</li><li>SDK compatibility</li><li>installers</li><li>packaging</li></ul>
+        <p>Students may use the free Azure account with B2pts v2 Arm-based burstable VM access (up to 750 hours/month).</p>
+        <p>Working professionals/developers use the same free Arm VM environment in Phase 1 and may use the stated $200 Azure credit in Phase 2.</p>
+        <p>Where Windows-specific GUI or installer behavior cannot be exercised on the provided Azure Arm VM, teams should clearly separate:</p>
+        <ul><li>what has been implemented</li><li>what has been cross-compiled</li><li>what has been CI-validated</li><li>what remains device-dependent</li></ul>
+        <p>Judging should reward:</p>
+        <ul><li>native-port completeness</li><li>native dependencies</li><li>ecosystem impact</li><li>technical difficulty</li><li>implementation quality</li><li>reproducible Arm64 benchmarking</li><li>CI/testing</li><li>upstream contribution</li><li>user/developer impact</li></ul>
+        <p>For AI/ML applications, runtimes or frameworks, teams are encouraged to use ONNX Runtime for efficient Arm64 inference and explore Arm KleidiAI integration/optimizations where applicable.</p>
+        <p>ONNX Runtime/KleidiAI usage should be applied only where relevant and is not mandatory for non-AI software ports.</p>
+        <p>For applicable AI workloads, teams should additionally demonstrate and document:</p>
+        <ul><li>ONNX Runtime/KleidiAI integration</li><li>inference performance</li><li>CPU/resource optimization</li></ul>
+        <h3>Checklist</h3>
+        <div class="ch-table-scroll" tabindex="0" role="region" aria-label="Problem statement 5 checklist">
+          <table class="ch-table ch-table-compact ch-ps-table ch-ps-checklist"><thead><tr><th>Participant category</th><th>Azure Arm64 access</th><th>AI inference optimization</th><th>CPU optimization</th><th>Use of KleidiAI</th><th>Primary technical validation</th></tr></thead><tbody>
+            <tr><td>Students</td><td>Free B2pts v2 Arm VM; $100 Azure credit for new account.</td><td>As applicable to selected project</td><td>Required</td><td>Optional / if AI workload</td><td>Native Arm64 port/dependencies; build &amp; CI; reproducible benchmarks; ecosystem impact</td></tr>
+            <tr><td>Working Professionals / Developers</td><td>Free B2pts v2 Arm VM; $200 Azure credit for new account.</td><td>As applicable to selected project</td><td>Required</td><td>Optional / if AI workload</td><td>Native Arm64 port/dependencies; CI/testing; reproducible benchmarks; upstream acceptance &amp; ecosystem impact</td></tr>
+          </tbody></table>
+        </div>
+        <h3>Evaluation Criteria</h3>
+        <div class="ch-table-scroll" tabindex="0" role="region" aria-label="Problem statement 5 evaluation criteria">
+          <table class="ch-table ch-table-compact ch-ps-table ch-ps-evaluation"><thead><tr><th>Evaluation Criterion</th><th>Weight</th><th>What Judges Assess</th></tr></thead><tbody>
+            <tr><td>Completeness of Native Arm64 Port / Enablement</td><td>20%</td><td>Functional completeness and reduction/elimination of x86/x64-only dependencies within the accessible environment.</td></tr>
+            <tr><td>Ecosystem / User Impact</td><td>20%</td><td>Importance/popularity of project and downstream users/apps/developers unlocked.</td></tr>
+            <tr><td>Arm64 Performance Evidence</td><td>15%</td><td>Repeatable startup, throughput, latency, memory, CPU or other relevant metrics; valid baseline required for comparison claims.</td></tr>
+            <tr><td>Technical Difficulty &amp; Native Dependencies</td><td>15%</td><td>Depth of dependency, build-system, SDK, CI, packaging or migration work.</td></tr>
+            <tr><td>Implementation Quality, CI &amp; Testing</td><td>15%</td><td>Maintainability, correctness, automated Arm64 testing, documentation and reproducibility.</td></tr>
+            <tr><td>Upstream Acceptance &amp; Sustainability</td><td>15%</td><td>Quality of upstream contribution: accepted/merged changes receive strongest credit; submitted Pull Requests/issues with maintainer engagement and a credible upstream path also receive credit.</td></tr>
+          </tbody></table>
+        </div>
       </div></details>
+      <div class="ch-note">
+        <p>Azure VM availability is subject to Microsoft subscription, region, quota and capacity restrictions. Teams should use the Azure Free Services configuration where available and report any subscription or regional limitations to the organizers.</p>
+        <p>Teams are not expected to run large foundation models entirely on the B2pts v2 VM. Lightweight, quantized, distilled or otherwise resource-efficient models are encouraged. Cloud AI services may be used where appropriate, but Arm64-local components must be clearly identified and benchmarked where required.</p>
+      </div>
     </section>
 
     <section class="ch-section">
@@ -172,28 +339,15 @@ <h3>Team Rules</h3><ul>
       </ul>
     </section>
 
-    <section class="ch-section">
-      <h2>Problem Statement Compliance Checklist</h2>
-      <div style="overflow-x: auto;"><table class="ch-table ch-compliance-table"><thead><tr><th>PS #</th><th>Domain</th><th>Participant Category</th><th>Free Azure Account</th><th>WoA Device</th><th>SVE2</th><th>SME2</th><th>Inference Acceleration</th><th>CPU Optimization</th><th>KleidiAI</th></tr></thead><tbody>
-        <tr><td>1</td><td>Healthcare</td><td>Students</td><td><span class="ch-status ch-status--mandatory">Mandatory</span></td><td><span class="ch-status ch-status--recommended">Optional / Recommended</span></td><td><span class="ch-status ch-status--mandatory">Mandatory</span></td><td><span class="ch-status ch-status--recommended">Optional / Recommended</span></td><td><span class="ch-status ch-status--recommended">Optional / Recommended</span></td><td><span class="ch-status ch-status--recommended">Optional / Recommended</span></td><td><span class="ch-status ch-status--recommended">Optional / Recommended</span></td></tr>
-        <tr><td>2</td><td>EdTech</td><td>Students</td><td><span class="ch-status ch-status--mandatory">Mandatory</span></td><td><span class="ch-status ch-status--recommended">Optional / Recommended</span></td><td><span class="ch-status ch-status--mandatory">Mandatory</span></td><td><span class="ch-status ch-status--recommended">Optional / Recommended</span></td><td><span class="ch-status ch-status--recommended">Optional / Recommended</span></td><td><span class="ch-status ch-status--recommended">Optional / Recommended</span></td><td><span class="ch-status ch-status--recommended">Optional / Recommended</span></td></tr>
-        <tr><td>3</td><td>Telemedicine</td><td>Working Professionals / Developers</td><td><span class="ch-status ch-status--not-required">Not Required</span></td><td><span class="ch-status ch-status--mandatory">Mandatory</span></td><td><span class="ch-status ch-status--not-required">Not Required</span></td><td><span class="ch-status ch-status--mandatory">Mandatory</span></td><td><span class="ch-status ch-status--mandatory">Mandatory</span></td><td><span class="ch-status ch-status--mandatory">Mandatory</span></td><td><span class="ch-status ch-status--mandatory">Mandatory</span></td></tr>
-        <tr><td>4</td><td>Gaming</td><td>Working Professionals / Developers</td><td><span class="ch-status ch-status--not-required">Not Required</span></td><td><span class="ch-status ch-status--mandatory">Mandatory</span></td><td><span class="ch-status ch-status--not-required">Not Required</span></td><td><span class="ch-status ch-status--mandatory">Mandatory</span></td><td><span class="ch-status ch-status--mandatory">Mandatory</span></td><td><span class="ch-status ch-status--mandatory">Mandatory</span></td><td><span class="ch-status ch-status--mandatory">Mandatory</span></td></tr>
-        <tr><td>5</td><td>App Migration</td><td>Students</td><td><span class="ch-status ch-status--mandatory">Mandatory</span></td><td><span class="ch-status ch-status--recommended">Optional / Recommended</span></td><td><span class="ch-status ch-status--mandatory">Mandatory</span></td><td><span class="ch-status ch-status--recommended">Optional / Recommended</span></td><td><span class="ch-status ch-status--recommended">Optional / Recommended</span></td><td><span class="ch-status ch-status--recommended">Optional / Recommended</span></td><td><span class="ch-status ch-status--recommended">Optional / Recommended</span></td></tr>
-        <tr><td>5</td><td>App Migration</td><td>Working Professionals / Developers</td><td><span class="ch-status ch-status--not-required">Not Required</span></td><td><span class="ch-status ch-status--mandatory">Mandatory</span></td><td><span class="ch-status ch-status--not-required">Not Required</span></td><td><span class="ch-status ch-status--mandatory">Mandatory</span></td><td><span class="ch-status ch-status--mandatory">Mandatory</span></td><td><span class="ch-status ch-status--mandatory">Mandatory</span></td><td><span class="ch-status ch-status--mandatory">Mandatory</span></td></tr>
-      </tbody></table></div>
-      <div class="ch-note">Challenge 5 is open to both Students and Working Professionals / Developers. Each participant category is held to its respective track's compliance requirements.</div>
-    </section>
-
     <section class="ch-section ch-section--featured"><h2>Recognition &amp; Rewards</h2><ul>
-      <li>Participation Certificates for all participants submitting a working project.</li><li>Arm-branded merchandise for the Top 40 finalists.</li><li>Cash awards for winning teams across each of the five problem statements.</li><li>A few of the top winning projects may be featured at national-level industry conferences.</li>
+      <li>Participants who submit a working project will receive a certificate of participation.</li><li>The Top 40 finalists will receive Arm-branded merchandise.</li><li>Winning teams across each of the five problem statements will receive cash awards.</li><li>A few of the top winning projects may be featured at national-level industry conferences.</li>
     </ul></section>
 
 <section class="ch-section ch-section--featured" id="registration">
   <h2>Registration</h2>
 
   <p>
-    Complete the registration form below to register for the
+    Complete the form below to register for the
     Namma Smart AI Apps Challenge.
   </p>
 
@@ -219,8 +373,8 @@ <h2>Registration</h2>
       <details class="ch-accordion"><summary><span class="ch-acc-title">8. Can a team change its composition or problem statement?</span></summary><div class="ch-acc-body"><p>One change of team composition or problem statement is permitted within 15 days of registration.</p></div></details>
       <details class="ch-accordion"><summary><span class="ch-acc-title">9. What happens in Phase 1?</span></summary><div class="ch-acc-body"><p>Participants submit problem statement alignment, technical architecture, a working code repository, a performance baseline and a 3–5-minute demo video.</p></div></details>
       <details class="ch-accordion"><summary><span class="ch-acc-title">10. What support do shortlisted teams receive?</span></summary><div class="ch-acc-body"><p>Hardware support, dedicated mentorship and cloud compute credits.</p></div></details>
-      <details class="ch-accordion"><summary><span class="ch-acc-title">11. When are winners announced?</span></summary><div class="ch-acc-body"><p>19 November 2026 at Bangalore Tech Summit 2026.</p></div></details>
-      <details class="ch-accordion"><summary><span class="ch-acc-title">12. What rewards are available?</span></summary><div class="ch-acc-body"><p>Participation certificates for participants submitting a working project, Arm-branded merchandise for the Top 40 finalists, cash awards for winning teams across all five problem statements, and potential national-level conference exposure for some top winning projects. Exact cash award amounts are not currently specified.</p></div></details>
+      <details class="ch-accordion"><summary><span class="ch-acc-title">11. When are winners announced?</span></summary><div class="ch-acc-body"><p>19 November 2026 at Bengaluru Tech Summit 2026.</p></div></details>
+      <details class="ch-accordion"><summary><span class="ch-acc-title">12. What rewards are available?</span></summary><div class="ch-acc-body"><p>Participants who submit a working project will receive a certificate of participation. The Top 40 finalists will receive Arm-branded merchandise, and winning teams across all five problem statements will receive cash awards. A few of the top winning projects may also be featured at national-level industry conferences. Exact cash award amounts are not currently specified.</p></div></details>
     </section>
   </main>
 </div>
diff --git a/docs/_sass/components/_challengePage.scss b/docs/_sass/components/_challengePage.scss
index c62d5904..8d8b5b92 100644
--- a/docs/_sass/components/_challengePage.scss
+++ b/docs/_sass/components/_challengePage.scss
@@ -309,7 +309,7 @@ html {
 .ch-accordion summary::after {
   content: "▾";
   margin-left: auto;
-  font-size: 12px;
+  font-size: 30px;
   opacity: 0.6;
   transition: transform 0.2s ease;
 }
@@ -570,6 +570,76 @@ html {
   z-index: 1;
 }
 
+.namma-challenge .ch-hero-layout {
+  display: grid;
+  grid-template-columns: minmax(0, 1fr) minmax(480px, 0.9fr);
+  align-items: center;
+  gap: 24px;
+}
+
+.namma-challenge .ch-hero-copy {
+  min-width: 0;
+}
+
+.namma-challenge .ch-logo-row {
+  display: flex;
+  flex-flow: row wrap;
+  align-items: center;
+  justify-content: flex-end;
+  gap: 6px;
+  min-width: 0;
+}
+
+.namma-challenge .ch-logo {
+  display: inline-flex;
+  align-items: center;
+  justify-content: center;
+  flex: 0 0 auto;
+
+  height: auto;
+  padding: 0;
+
+  border: 0;
+  background: transparent;
+  box-shadow: none;
+
+  overflow: visible;
+}
+
+.namma-challenge .ch-logo img {
+  display: block;
+  width: auto;
+  height: auto;
+
+  max-height: 74px;
+
+  object-fit: contain;
+  border-radius: 10px;
+}
+
+.namma-challenge .ch-logo--wide img {
+  max-width: 330px;
+}
+
+
+
+.namma-challenge .ch-logo--iiitb img {
+  max-height: 150px;
+}
+
+@media (max-width: 1040px) {
+  .namma-challenge .ch-hero-layout {
+    grid-template-columns: minmax(0, 1fr) minmax(340px, 0.85fr);
+    gap: 18px;
+  }
+
+  .namma-challenge .ch-logo-row {
+    justify-content: center;
+    max-width: 360px;
+    margin-left: auto;
+  }
+}
+
 .namma-challenge .ch-badge-dot {
   background: var(--purple-accent);
 }
@@ -695,7 +765,65 @@ html {
   font-weight: 800;
 }
 
+.namma-challenge .ch-problem-statements .ch-acc-body > h3 {
+  margin: 20px 0 8px;
+  color: var(--purple-dark);
+  font-size: 16px;
+}
+
+.namma-challenge .ch-table-scroll {
+  max-width: 100%;
+  overflow-x: auto;
+  border: 1px solid var(--purple-border);
+  border-radius: 8px;
+  -webkit-overflow-scrolling: touch;
+}
+
+.namma-challenge .ch-table-scroll:focus-visible {
+  outline: 3px solid rgba(3, 102, 214, 0.28);
+  outline-offset: 2px;
+}
+
+.namma-challenge .ch-table-scroll .ch-table {
+  margin-top: 0;
+}
+
+.namma-challenge .ch-ps-table {
+  min-width: 760px;
+}
+
+.namma-challenge .ch-ps-checklist {
+  min-width: 980px;
+}
+
+.namma-challenge .ch-ps-table th,
+.namma-challenge .ch-ps-table td {
+  line-height: 1.4;
+  vertical-align: top;
+}
+
+.namma-challenge .ch-ps-evaluation th:nth-child(1),
+.namma-challenge .ch-ps-evaluation td:nth-child(1) {
+  width: 30%;
+}
+
+.namma-challenge .ch-ps-evaluation th:nth-child(2),
+.namma-challenge .ch-ps-evaluation td:nth-child(2) {
+  width: 10%;
+  text-align: center;
+  white-space: nowrap;
+}
+
+.namma-challenge .ch-ps-evaluation th:nth-child(3),
+.namma-challenge .ch-ps-evaluation td:nth-child(3) {
+  width: 60%;
+}
+
 /* Timeline emphasis without changing the table structure. */
+.namma-challenge .ch-timeline {
+  min-width: 700px;
+}
+
 .namma-challenge .ch-timeline td:first-child {
   color: var(--purple-dark);
   font-weight: 700;
@@ -823,6 +951,30 @@ html {
     margin: 8px 0;
   }
 
+  .namma-challenge .ch-hero-layout {
+    grid-template-columns: minmax(0, 1fr);
+    gap: 20px;
+  }
+
+  .namma-challenge .ch-hero-layout > .ch-logo-row {
+    flex-direction: row !important;
+    justify-content: center;
+    max-width: none;
+    margin-left: 0;
+  }
+
+  .namma-challenge .ch-logo {
+    height: 68px;
+  }
+
+  .namma-challenge .ch-hero-layout .ch-logo-row .ch-logo img {
+    width: auto !important;
+    height: auto !important;
+    max-height: 52px !important;
+    padding: 0 !important;
+    border-radius: 0 !important;
+  }
+
   .ch-tagline {
     font-size: 16px;
     line-height: 1.5;
@@ -1010,6 +1162,22 @@ html {
     font-size: 20px;
   }
 
+  .namma-challenge .ch-hero-layout > .ch-logo-row {
+    flex-direction: row !important;
+  }
+
+  .namma-challenge .ch-logo {
+    max-width: 100%;
+  }
+
+  .namma-challenge .ch-hero-layout .ch-logo-row .ch-logo img {
+    max-height: 48px !important;
+  }
+
+  .namma-challenge .ch-logo--wide img {
+    max-width: min(252px, calc(100vw - 74px));
+  }
+
   .ch-tagline {
     font-size: 15px;
     line-height: 1.5;
diff --git a/docs/images/Arm_Brand_Logo_Guidelines_300x175px.jpg b/docs/images/Arm_Brand_Logo_Guidelines_300x175px.jpg
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From a7b34e497227207b27bac51d7eb57f4dcea5659d Mon Sep 17 00:00:00 2001
From: Habib-Banwo <habib.ogunbanwo@arm.com>
Date: Fri, 4 Sep 2026 11:28:25 +0100
Subject: [PATCH 2/3] Namma and Bharat page updated

---
 docs/Bharat_AI_SoC_2026_27.html           | 158 ++++++++++----------
 docs/Namma_Challenge_Page.html            | 171 +++++++++++-----------
 docs/_sass/components/_challengePage.scss |  70 ++++++++-
 3 files changed, 240 insertions(+), 159 deletions(-)

diff --git a/docs/Bharat_AI_SoC_2026_27.html b/docs/Bharat_AI_SoC_2026_27.html
index 00339aa7..a90ac0fc 100644
--- a/docs/Bharat_AI_SoC_2026_27.html
+++ b/docs/Bharat_AI_SoC_2026_27.html
@@ -1,7 +1,7 @@
 ---
 layout: page
 title: Bharat AI-SoC Challenge — 2026–27
-published: false
+<!--published: false-->
 show_title: false
 description: Bharat AI-SoC Challenge — 2026–27, 2nd Edition
 ---
@@ -27,17 +27,21 @@ <h1>Bharat AI-SoC Challenge — 2026–27</h1>
         </div>
         <div class="ch-meta-pill">
           <span class="ch-meta-label">Format</span>
-          <span class="ch-meta-value">Three-Phase Innovation Pipeline</span>
+          <span class="ch-meta-value">Open Qualifying &amp; PoC Development</span>
         </div>
       </div>
     </div>
   </header>
 
+  <div class="ch-register-cta">
+    <a href="#registration" class="ch-register-button">Register Now</a>
+  </div>
+
   <main>
     <section class="ch-section ch-section--featured bharat-overview">
       <h2>Overview</h2>
-      <p>The inaugural Bharat AI-SoC Student Challenge attracted more than 3,000 students across 1,238 teams. Through three levels of evaluation and virtual mentorship, 150 shortlisted projects addressing five industry and societal problem statements were narrowed to five winners.</p>
-      <p>The 2nd Edition scales the challenge nationally, widens eligibility and introduces a multi-stage qualification process. Additional milestone-based grant support will help top teams progress beyond Proof of Concept (PoC) toward working prototypes and product readiness.</p>
+      <p>The inaugural Bharat AI-SoC Student Challenge, organised by Arm Education, MeitY C2S and IIT Delhi as Lead Mentor, attracted more than 3,000 students across 1,238 teams. Following three levels of evaluation and virtual mentorship, 150 shortlisted teams were narrowed to five winners.</p>
+      <p>The 2nd Edition expands eligibility nationally to students and working professionals / developers. Participants compete on equal terms across five problem statements, progressing from Open Qualifying to Proof of Concept (PoC) Development.</p>
     </section>
 
     <section class="ch-section bharat-whats-new">
@@ -48,21 +52,21 @@ <h2>What’s New in the 2nd Edition</h2>
           <div class="phase-deliverable-text">Students (UG/PG) and Working Professionals / Developers compete on equal terms across five problem statements.</div>
         </div>
         <div class="phase-deliverable">
-          <div class="phase-deliverable-title">Three-Phase Innovation Pipeline</div>
-          <div class="phase-deliverable-text">An approximately 8-week Open Qualifying phase, an approximately 6-week PoC Development phase, and a 6–12 month Product Development phase for winning teams.</div>
+          <div class="phase-deliverable-title">Two Challenge Phases</div>
+          <div class="phase-deliverable-text">The Open Qualifying phase runs from 21 October to 21 December 2026. Shortlisted teams then advance to the PoC Development phase, which concludes on 5 March 2027.</div>
         </div>
         <div class="phase-deliverable">
           <div class="phase-deliverable-title">One Winner per Problem Statement</div>
-          <div class="phase-deliverable-text">Winners are selected after a live virtual demo and jury Q&amp;A. Top performers advance to the Post-Challenge Product Development Grant.</div>
+          <div class="phase-deliverable-text">One winner for each problem statement is selected after the final live virtual demo and jury Q&amp;A.</div>
         </div>
       </div>
     </section>
 
     <section class="ch-section bharat-tech-theme">
       <h2>Technology Theme: Applied AI on SoC</h2>
-      <p>AI is moving from cloud-centric systems toward AI executed directly on System-on-Chips at the edge. This enables local processing, real-time response, privacy and operation without continuous connectivity, while meeting performance, power and cost constraints through the integration of AI software and semiconductor hardware.</p>
+      <p>Applied AI on SoC brings AI processing onto edge devices. Local processing can support real-time operation, privacy-aware use of data and offline capability, while teams consider performance, power and cost constraints.</p>
       <p>The major shift in the 2nd Edition is from <strong>building AI applications</strong> to <strong>engineering AI-enabled products on SoCs</strong>.</p>
-      <p>The end-to-end engineering journey spans:</p>
+      <p>Participants will explore how AI models integrate with processors, memory, accelerators and software stacks. The end-to-end engineering journey spans:</p>
       <ul>
         <li>AI algorithm development</li>
         <li>Optimisation</li>
@@ -75,22 +79,17 @@ <h2>Technology Theme: Applied AI on SoC</h2>
     </section>
 
     <section class="ch-section bharat-pipeline">
-      <h2>Three-Phase Innovation Pipeline</h2>
+      <h2>Challenge Format</h2>
       <div class="phase-deliverables">
         <div class="phase-deliverable bharat-phase bharat-phase--1">
           <div class="phase-deliverable-title">Phase 1 — Open Qualifying</div>
-          <div class="phase-deliverable-text"><strong>Duration:</strong> ~8 weeks</div>
-          <div class="phase-deliverable-text">Open qualifying stage where teams develop and submit their initial solution, report, code and demo.</div>
+          <div class="phase-deliverable-text"><strong>21 October – 21 December 2026</strong></div>
+          <div class="phase-deliverable-text">Teams develop their initial solution and submit their report, source code and demo.</div>
         </div>
         <div class="phase-deliverable bharat-phase bharat-phase--2">
           <div class="phase-deliverable-title">Phase 2 — PoC Development</div>
-          <div class="phase-deliverable-text"><strong>Duration:</strong> ~6 weeks</div>
-          <div class="phase-deliverable-text">Shortlisted teams receive mentorship and develop a fully deployed Proof of Concept.</div>
-        </div>
-        <div class="phase-deliverable bharat-phase bharat-phase--3">
-          <div class="phase-deliverable-title">Phase 3 — Product Development</div>
-          <div class="phase-deliverable-text"><strong>Duration:</strong> 6–12 months</div>
-          <div class="phase-deliverable-text">Winning teams receive continued technical, financial and ecosystem support to mature their PoCs into working prototypes / deployable products and accelerate go-to-market.</div>
+          <div class="phase-deliverable-text"><strong>End of January – 5 March 2027</strong></div>
+          <div class="phase-deliverable-text">Shortlisted teams receive mentorship and develop and complete their proof of concept.</div>
         </div>
       </div>
     </section>
@@ -107,7 +106,7 @@ <h2>Project Problem Statements</h2>
           <h3>Objective</h3>
           <p>Develop an AI-powered assistant that helps farmers identify crop diseases, detect pests and receive actionable advisory recommendations, running entirely on-device without internet connectivity.</p>
           <h3>Project Description</h3>
-          <p>Students build an AI system capable of analysing crop images to:</p>
+          <p>Teams will build an AI system capable of analysing crop images to:</p>
           <ul>
             <li>Detect diseases and pests</li>
             <li>Classify severity levels</li>
@@ -129,12 +128,14 @@ <h3>Hardware</h3>
           <p>Teams may use any Arm-based platform appropriate for their solution, including:</p>
           <ul>
             <li>Arm-based Android smartphones</li>
-            <li>Raspberry Pi 5</li>
-            <li>Raspberry Pi AI Kit (Hailo AI Accelerator)</li>
+            <li>Raspberry Pi 5, CPU-only</li>
+            <li>Raspberry Pi 5 with AI HAT+ / AI HAT+ 2</li>
             <li>Arm-based Linux devices</li>
           </ul>
+          <p>If a Hailo accelerator is used for vision workloads, vision models run through HailoRT on the Hailo NPU, while Llama runs through ExecuTorch on the Raspberry Pi Arm CPU. These are separate pipelines and should be clearly documented.</p>
           <h3>Required Technologies</h3>
           <ul><li>Llama</li><li>ExecuTorch</li></ul>
+          <p><strong>Technical requirement:</strong> A model from the Llama family must be used for the advisory or reasoning component and deployed on-device using ExecuTorch as the reference runtime (for example, Llama 3.2 1B/3B-Instruct or a fine-tuned or distilled variant).</p>
           <h3>Optional Technologies</h3>
           <ul>
             <li>OpenCV</li>
@@ -150,14 +151,14 @@ <h3>Deliverables</h3>
 
       <details class="ch-accordion">
         <summary>
-          <span class="ch-acc-title"><span class="bharat-ps-number">PS2</span><span class="bharat-ps-separator"> — </span><span class="bharat-ps-title">Smart recycling &amp; waste-segregation assistant, multimodal interfaces</span></span>
+          <span class="ch-acc-title"><span class="bharat-ps-number">PS2</span><span class="bharat-ps-separator"> — </span><span class="bharat-ps-title">Smart recycling &amp; waste-segregation assistant using multimodal interfaces</span></span>
           <span class="ch-acc-domain">Theme: Environment &amp; Climate</span>
         </summary>
         <div class="ch-acc-body">
           <h3>Objective</h3>
           <p>Develop an AI-powered assistant that helps users identify, classify and properly dispose of waste materials using Llama and ExecuTorch running on Arm-based devices.</p>
           <h3>Project Description</h3>
-          <p>Students develop an AI-powered assistant capable of:</p>
+          <p>Teams will develop an AI-powered assistant capable of:</p>
           <ul>
             <li>Identifying waste categories</li>
             <li>Providing disposal recommendations</li>
@@ -165,6 +166,7 @@ <h3>Project Description</h3>
             <li>Promoting sustainable waste management practices</li>
             <li>Educating users about environmental impact</li>
           </ul>
+          <p>Teams are free to design the most appropriate user experience for their solution.</p>
           <h3>Possible Interfaces</h3>
           <ul>
             <li>Android applications</li>
@@ -185,12 +187,14 @@ <h3>Example Application Areas</h3>
           <h3>Hardware</h3>
           <ul>
             <li>Arm-based Android smartphones</li>
-            <li>Raspberry Pi 5</li>
-            <li>Raspberry Pi AI Kit (Hailo AI Accelerator)</li>
+            <li>Raspberry Pi 5, CPU-only</li>
+            <li>Raspberry Pi 5 with AI HAT+ / AI HAT+ 2</li>
             <li>Arm-based Linux devices</li>
           </ul>
+          <p>If a Hailo accelerator is used for vision workloads, vision models run through HailoRT on the Hailo NPU, while Llama runs through ExecuTorch on the Raspberry Pi Arm CPU. These are separate pipelines and should be clearly documented.</p>
           <h3>Required Technologies</h3>
           <ul><li>Llama</li><li>ExecuTorch</li></ul>
+          <p><strong>Technical requirement:</strong> A model from the Llama family must be used for the advisory or reasoning component and deployed on-device using ExecuTorch as the reference runtime.</p>
           <h3>Optional Technologies</h3>
           <ul><li>OpenCV</li><li>Hailo SDK</li><li>Speech processing frameworks</li><li>Computer vision models</li></ul>
           <h3>Functional Requirements</h3>
@@ -202,27 +206,27 @@ <h3>Functional Requirements</h3>
             <li>Demonstrate use of ExecuTorch</li>
           </ul>
           <h3>Deliverables</h3>
-          <ul><li>Source code</li><li>Application or interface implementation</li><li>Demonstration video</li><li>Technical report</li></ul>
+          <ul><li>Source code</li><li>Application or interface</li><li>Demonstration video</li><li>Technical report</li></ul>
         </div>
       </details>
 
       <details class="ch-accordion">
         <summary>
-          <span class="ch-acc-title"><span class="bharat-ps-number">PS3</span><span class="bharat-ps-separator"> — </span><span class="bharat-ps-title">On-device threat, phishing &amp; scam detection with full data privacy</span></span>
+          <span class="ch-acc-title"><span class="bharat-ps-number">PS3</span><span class="bharat-ps-separator"> — </span><span class="bharat-ps-title">On-device threat, phishing &amp; scam detection</span></span>
           <span class="ch-acc-domain">Theme: Cybersecurity &amp; Digital Safety</span>
         </summary>
         <div class="ch-acc-body">
           <h3>Objective</h3>
           <p>Develop an on-device AI system that detects cybersecurity threats, including network intrusions, malware behaviour and phishing or scam communications, running entirely on Arm-based edge hardware without cloud dependency.</p>
           <h3>Project Description</h3>
-          <p>Students build an AI-powered security assistant capable of:</p>
+          <p>Teams will build an AI-powered security assistant capable of:</p>
           <ul>
             <li>Monitoring and classifying network traffic for anomalies and intrusions</li>
             <li>Detecting phishing URLs</li>
             <li>Detecting scam SMS messages or fraudulent communications</li>
             <li>Explaining threats to non-expert users in plain language</li>
             <li>Recommending remediation actions</li>
-            <li>Preserving privacy because no data leaves the device</li>
+            <li>Processing data locally on the device without relying on cloud services</li>
           </ul>
           <h3>Example Application Areas</h3>
           <ul>
@@ -236,12 +240,14 @@ <h3>Example Application Areas</h3>
           <h3>Hardware</h3>
           <ul>
             <li>Arm-based Android smartphones</li>
-            <li>Raspberry Pi 5</li>
-            <li>Raspberry Pi AI Kit (Hailo AI Accelerator)</li>
+            <li>Raspberry Pi 5, CPU-only</li>
+            <li>Raspberry Pi 5 with AI HAT+ / AI HAT+ 2</li>
             <li>Arm-based Linux devices</li>
           </ul>
+          <p>If a Hailo accelerator is used for compatible workloads, it runs through HailoRT on the Hailo NPU, while Llama runs through ExecuTorch on the Raspberry Pi Arm CPU. These are separate pipelines and should be clearly documented.</p>
           <h3>Required Technologies</h3>
           <ul><li>Llama</li><li>ExecuTorch</li></ul>
+          <p><strong>Technical requirement:</strong> A model from the Llama family must be used for the advisory or reasoning component and deployed on-device using ExecuTorch as the reference runtime.</p>
           <h3>Optional Technologies</h3>
           <ul>
             <li>CICIDS / UNSW-NB15 / NSL-KDD datasets</li>
@@ -257,14 +263,14 @@ <h3>Deliverables</h3>
 
       <details class="ch-accordion">
         <summary>
-          <span class="ch-acc-title"><span class="bharat-ps-number">PS4</span><span class="bharat-ps-separator"> — </span><span class="bharat-ps-title">On-device personalized learning assistant</span></span>
-          <span class="ch-acc-domain">Theme:</span>
+          <span class="ch-acc-title"><span class="bharat-ps-number">PS4</span><span class="bharat-ps-separator"> — </span><span class="bharat-ps-title">On-device personalised learning assistant</span></span>
+          <span class="ch-acc-domain">Theme: Education</span>
         </summary>
         <div class="ch-acc-body">
           <h3>Objective</h3>
-          <p>Develop an AI learning companion that creates personalized learning pathways based on learner goals, interests and current skill levels.</p>
+          <p>Develop an AI learning companion that creates personalised learning pathways based on learner goals, interests and current skill levels.</p>
           <h3>Project Description</h3>
-          <p>Students create an AI assistant capable of:</p>
+          <p>Teams will create an AI assistant capable of:</p>
           <ul>
             <li>Understanding learner goals</li>
             <li>Identifying skill gaps</li>
@@ -283,6 +289,7 @@ <h3>Example Application Areas</h3>
           </ul>
           <h3>Required Technologies</h3>
           <ul><li>Llama</li><li>ExecuTorch</li><li>Arm-based Android smartphone</li></ul>
+          <p><strong>Technical requirement:</strong> A model from the Llama family must be used for the advisory or reasoning component and deployed on-device using ExecuTorch as the reference runtime.</p>
           <h3>Deliverables</h3>
           <ul><li>Source code</li><li>Application</li><li>Demo video</li><li>Technical report</li></ul>
         </div>
@@ -297,7 +304,7 @@ <h3>Deliverables</h3>
           <h3>Objective</h3>
           <p>Develop an on-device AI system that performs real-time visual inspection of manufactured components, detects surface defects and anomalies, and assists quality control operators on the factory floor.</p>
           <h3>Project Description</h3>
-          <p>Students build an AI-powered quality control assistant capable of:</p>
+          <p>Teams will build an AI-powered quality control assistant capable of:</p>
           <ul>
             <li>Detecting surface defects, cracks and anomalies</li>
             <li>Classifying defect type and severity</li>
@@ -316,13 +323,15 @@ <h3>Example Application Areas</h3>
           </ul>
           <h3>Hardware</h3>
           <ul>
-            <li>Raspberry Pi 5</li>
-            <li>Raspberry Pi AI Kit (Hailo AI Accelerator)</li>
+            <li>Raspberry Pi 5, CPU-only</li>
+            <li>Raspberry Pi 5 with AI HAT+ / AI HAT+ 2</li>
             <li>Arm-based Linux devices</li>
             <li>Arm-based Android devices with camera</li>
           </ul>
+          <p>If a Hailo accelerator is used for vision workloads, vision models run through HailoRT on the Hailo NPU, while Llama runs through ExecuTorch on the Raspberry Pi Arm CPU. These are separate pipelines and should be clearly documented.</p>
           <h3>Required Technologies</h3>
           <ul><li>Llama</li><li>ExecuTorch</li></ul>
+          <p><strong>Technical requirement:</strong> A model from the Llama family must be used for the advisory or reasoning component and deployed on-device using ExecuTorch as the reference runtime.</p>
           <h3>Optional Technologies</h3>
           <ul>
             <li>OpenCV</li>
@@ -339,72 +348,71 @@ <h3>Deliverables</h3>
 
     <section class="ch-section bharat-timeline">
       <h2>Timeline</h2>
-      <p><strong>Oct 2026 – Mar 2027, + 9-month product development</strong></p>
       <div class="bharat-table-scroll">
         <table class="ch-table ch-timeline">
           <thead>
-            <tr><th>Milestone</th><th>Proposed Schedule</th><th>Outcome</th></tr>
+            <tr><th>Milestone</th><th>Date</th><th>What happens</th></tr>
           </thead>
           <tbody>
-            <tr><td>Launch</td><td>19 Sep 2026</td><td>Launch at Semicon India 2026</td></tr>
-            <tr><td>Registration</td><td>19 Sep – 20 Oct 2026</td><td>Target 10K+ registrations</td></tr>
-            <tr><td>Phase 1 — Open Qualifying</td><td>21 Oct 2026 – 3 Jan 2027</td><td>Report, code, demo submitted</td></tr>
-            <tr><td>Phase 1 Evaluation &amp; Shortlist</td><td>End Jan 2027</td><td>Top teams per PS advance</td></tr>
-            <tr><td>Phase 2 — PoC Development</td><td>End Jan – 5 Mar 2027</td><td>Fully deployed PoC built</td></tr>
-            <tr><td>Finals &amp; Winners' Announcement</td><td>10 – 25 Mar 2027</td><td>Top 5 Winner selections</td></tr>
-            <tr><td>Post-Challenge Product Development</td><td>1 Apr – 30 Dec 2027</td><td>PoC → working prototype / product</td></tr>
+            <tr><td>Launch</td><td>19 September 2026</td><td>The Bharat AI-SoC Challenge 2026–27 launches at Semicon India 2026.</td></tr>
+            <tr><td>Registration</td><td>19 September – 20 October 2026</td><td>Registration is open for eligible participants to form their teams and prepare for the challenge.</td></tr>
+            <tr><td>Phase 1 — Open Qualifying</td><td>21 October – 21 December 2026</td><td>Teams develop their initial solution and submit their report, source code and demo.</td></tr>
+            <tr><td>Phase 1 Evaluation &amp; Shortlist</td><td>Mid-January 2027</td><td>Submissions are evaluated and shortlisted teams advance to the PoC Development phase.</td></tr>
+            <tr><td>Phase 2 — PoC Development</td><td>End of January – 5 March 2027</td><td>Shortlisted teams develop and complete their proof of concept.</td></tr>
+            <tr><td>Finals &amp; Winners' Announcement</td><td>10–25 March 2027</td><td>Finalists take part in the final evaluation, with one winner selected for each problem statement.</td></tr>
           </tbody>
         </table>
       </div>
     </section>
 
     <section class="ch-section ch-section--featured bharat-prizes">
-      <h2>Prizes &amp; Post-Challenge Product Development Grant</h2>
+      <h2>Rewards &amp; Recognition</h2>
       <table class="ch-table bharat-prize-table">
         <thead>
-          <tr><th>Recognition</th><th>Recipients</th><th>Amount</th></tr>
+          <tr><th>Recognition</th><th>Recipients</th><th>Reward</th></tr>
         </thead>
         <tbody>
-          <tr><td data-label="Recognition">Winner</td><td data-label="Recipients">1 per problem statement (5 total)</td><td data-label="Amount">₹10,00,000</td></tr>
-          <tr><td data-label="Recognition">Runner-up</td><td data-label="Recipients">1 per problem statement (5 total)</td><td data-label="Amount">₹5,00,000</td></tr>
-          <tr><td data-label="Recognition">Certificates</td><td data-label="Recipients">All Phase 1 completions and Phase 2 shortlists</td><td data-label="Amount">—</td></tr>
+          <tr><td data-label="Recognition">Winner</td><td data-label="Recipients">1 per problem statement (5 winners total)</td><td data-label="Reward">₹1,00,000 per winner</td></tr>
+          <tr><td data-label="Recognition">Certificates</td><td data-label="Recipients">All Phase 1 completions and Phase 2 shortlists</td><td data-label="Reward">Certificate</td></tr>
+          <tr><td data-label="Recognition">Finalist recognition</td><td data-label="Recipients">Finalist team members</td><td data-label="Reward">Branded swag from IIT Delhi</td></tr>
         </tbody>
       </table>
-      <h3 class="bharat-grant-heading">Post-Challenge Product Development Grant</h3>
-      <p class="bharat-grant-summary">A total pool of <strong>₹1,25,00,000</strong> provides <strong>₹25,00,000 per problem statement winner</strong>, released across three milestone-linked tranches over nine months.</p>
-      <p>Milestone progression covers:</p>
-      <ol>
-        <li>Plan approval</li>
-        <li>Validated model</li>
-        <li>Integrated prototype</li>
-        <li>Tapeout-readiness / production prototype</li>
-      </ol>
-      <ul>
-        <li>Governed by a Joint Oversight Committee involving MeitY and Industry</li>
-        <li>Managed by IIT Delhi</li>
-        <li>Industry/academic mentor per team</li>
-        <li>IP retained by the student/institution</li>
-        <li>Possible outcomes include Demo Day, patent-filing support and VC introductions</li>
-      </ul>
     </section>
 
     <section class="ch-section bharat-partners">
-      <h2>Organizing Partners</h2>
+      <h2>Organising Partners</h2>
       <table class="ch-table bharat-partners-table">
         <thead>
           <tr><th>Category</th><th>Partner</th><th>Role</th></tr>
         </thead>
         <tbody>
-          <tr><td data-label="Category">Government</td><td data-label="Partner">MeitY C2S</td><td data-label="Role">Sponsor, overall guidance, outreach and product development support</td></tr>
-          <tr><td data-label="Category">Academia</td><td data-label="Partner">IIT Delhi</td><td data-label="Role">Manage overall process — mentorship, evaluation, jury and product development</td></tr>
-          <tr><td data-label="Category">Industry</td><td data-label="Partner">Arm (primary) and <span class="bharat-tentative">Meta (tentative - tech support)</span></td><td data-label="Role">Industry mentorship, evaluation and program support, including 3rd party agency for end-to-end execution</td></tr>
+          <tr><td data-label="Category">Government</td><td data-label="Partner">MeitY C2S</td><td data-label="Role">Government partner supporting the challenge</td></tr>
+          <tr><td data-label="Category">Academia</td><td data-label="Partner">IIT Delhi</td><td data-label="Role">Lead academic mentor for the challenge</td></tr>
+          <tr><td data-label="Category">Industry</td><td data-label="Partner">Arm (primary industry partner) and Meta (tentative tech support)</td><td data-label="Role">Industry mentorship, technical guidance and evaluation support</td></tr>
         </tbody>
       </table>
     </section>
 
     <section class="ch-section ch-section--featured bharat-registration" id="registration">
-      <h2>Registration</h2>
-      <p class="ch-note">Registration details will be added soon.</p>
+      <h2>Eligibility &amp; Registration</h2>
+      <p>Students (UG/PG) and Working Professionals / Developers are eligible to participate and compete on equal terms across all five problem statements.</p>
+      <p>Registration runs from 19 September to 20 October 2026.</p>
+      <h3>Original Work Requirements</h3>
+      <p>Entries must:</p>
+      <ul>
+        <li>Be the original work of the entrant</li>
+        <li>Be solely owned by the entrant</li>
+        <li>Not violate the intellectual property rights of any other person or entity</li>
+      </ul>
+      <p>Complete the form below to register for the Bharat AI-SoC Challenge 2026–27.</p>
+
+      <div class="registration-form-wrapper">
+        <script src="//engaged.arm.com/js/forms2/js/forms2.min.js"></script>
+        <form id="mktoForm_4309"></form>
+        <script>
+          MktoForms2.loadForm("//engaged.arm.com", "479-ZYW-907", 4309);
+        </script>
+      </div>
     </section>
   </main>
 </div>
diff --git a/docs/Namma_Challenge_Page.html b/docs/Namma_Challenge_Page.html
index e8d53a30..cd46ca91 100644
--- a/docs/Namma_Challenge_Page.html
+++ b/docs/Namma_Challenge_Page.html
@@ -27,7 +27,7 @@
         <div class="ch-hero-copy">
           <div class="ch-badge"><span class="ch-badge-dot"></span><span>Namma Smart AI Apps Challenge</span></div>
           <h1>Namma Smart AI Apps Challenge</h1>
-          <p class="ch-tagline">AI-Native Smart Applications for Digital Karnataka</p>
+          <p class="ch-tagline">AI-Native Smart Applications for Digital Karnataka &amp; Digital India</p>
           <p>In collaboration with Arm, IIIT Bangalore &amp; NASSCOM</p>
           <div class="ch-header-meta">
             <div class="ch-meta-pill"><span class="ch-meta-label">Duration</span><span class="ch-meta-value">September – November 2026</span></div>
@@ -61,23 +61,22 @@ <h1>Namma Smart AI Apps Challenge</h1>
 
   <main>
     <section class="ch-section ch-section--featured">
-      <h2>Program Overview</h2>
-      <p>The Namma Smart AI Apps Challenge is a national initiative from Arm and IIIT Bangalore, supported by NASSCOM, that helps participants build practical skills in on-device Edge AI application development.</p>
-      <p>The challenge is open to both student developers (UG/PG) and working professionals, reflecting the breadth of Edge AI application-development talent across academia and industry.</p>
+      <h2>Programme Overview</h2>
+      <p>The Namma Smart AI Apps Challenge is a national initiative from Arm and IIIT Bangalore, supported by NASSCOM, that helps participants build practical skills in Edge AI application development on edge devices.</p>
+      <p>The challenge is open to both students (UG/PG) and working professionals &amp; developers, reflecting the breadth of Edge AI application-development talent across academia and industry.</p>
+      <p>The programme has a strong focus on skill development and provides project-based learning opportunities for all participants.</p>
       <p>Participants will build real-world applications with societal impact across healthcare, education, telemedicine, gaming, and application modernisation, with mentorship from Arm engineers and IIIT Bangalore faculty.</p>
-      <p>The program officially launches on 2 September 2026 and culminates in a live demo day and winners' announcement at Bengaluru Tech Summit 2026 on 19 November 2026.</p>
     </section>
 
     <section class="ch-section">
-      <h2>Program Objectives</h2>
+      <h2>Programme Objectives</h2>
       <ul>
-        <li>Build practical, industry-relevant skills in on-device AI and app development.</li>
-        <li>Create a joint academia–industry talent pipeline for students and working professionals.</li>
-        <li>Translate technology into real, deployable applications addressing genuine societal problems.</li>
-        <li>Establish IIIT Bangalore as an academic anchor for Edge AI curriculum, mentorship, and lab infrastructure.</li>
-        <li>Connect participants directly with Arm engineers and IIIT Bangalore faculty through structured mentorship.</li>
-        <li>Encourage collaboration between academic experimentation and industry engineering discipline.</li>
-        <li>Strengthen collaboration between Arm, NASSCOM, and IIIT Bangalore.</li>
+        <li>Build practical, industry-relevant skills in AI and application development on edge devices.</li>
+        <li>Develop real-world applications that address genuine societal challenges.</li>
+        <li>Gain hands-on experience in optimisation, benchmarking and deployment.</li>
+        <li>Learn directly from Arm engineers and IIIT Bangalore faculty through structured mentorship.</li>
+        <li>Gain experience working at the intersection of academic learning and industry engineering.</li>
+        <li>Strengthen practical understanding of Edge AI development on Arm-based platforms.</li>
       </ul>
     </section>
 
@@ -92,15 +91,15 @@ <h3>AI-Native Smart Applications for Digital Karnataka</h3>
     <section class="ch-section">
       <h2>What Makes This Challenge Different</h2>
       <p>This is an Edge AI challenge rather than a traditional cloud-only hackathon. Participants build AI-native on-device applications designed for privacy, low latency, responsiveness, offline capability, and power efficiency.</p>
-      <p>Real-world societal problem statements are paired with mentorship from Arm and academic guidance from IIIT Bangalore. Students and working professionals gain hands-on experience in building, optimisation, benchmarking, and deployment.</p>
+      <p>Real-world societal challenge areas are paired with mentorship from Arm and academic guidance from IIIT Bangalore. Students and working professionals gain hands-on experience in building, optimisation, benchmarking, and deployment.</p>
     </section>
 
     <section class="ch-section">
-      <h2>Program Flow &amp; Timeline</h2>
-      <div class="ch-table-scroll" tabindex="0" role="region" aria-label="Program timeline">
+      <h2>Programme Flow &amp; Timeline</h2>
+      <div class="ch-table-scroll" tabindex="0" role="region" aria-label="Programme timeline">
         <table class="ch-table ch-timeline"><thead><tr><th>Milestone</th><th>Timing</th><th>What Happens</th></tr></thead><tbody>
           <tr><td>Launch</td><td>2nd September 2026</td><td>The challenge portal opens</td></tr>
-          <tr><td>Registration</td><td>2nd Sept – 20 Sept 2026</td><td>Participants register their teams and select a problem statement</td></tr>
+          <tr><td>Registration</td><td>2nd Sept – 20 Sept 2026</td><td>Participants register their teams and select a challenge area</td></tr>
           <tr><td>Phase 1 – Ideation</td><td>20th – 30th Sept 2026</td><td>Develop your initial solution with mentoring support and submit your report and demo video</td></tr>
           <tr><td>Phase 1 Evaluation &amp; Shortlist</td><td>1 – 10 Oct 2026</td><td>Submissions are evaluated and shortlisted teams are selected for Phase 2</td></tr>
           <tr><td>Phase 2 – PoC Development</td><td>11 – 30 Oct 2026</td><td>Shortlisted teams develop their full proof-of-concept application</td></tr>
@@ -114,13 +113,13 @@ <h2>Program Flow &amp; Timeline</h2>
       <h2>Phase 1 — Open Qualifying Round</h2>
       <p>In Phase 1, all registered participants submit a project report containing:</p>
       <div class="phase-deliverables">
-        <div class="phase-deliverable"><div class="phase-deliverable-title">Problem Statement Alignment</div><div class="phase-deliverable-text">Clear articulation of the chosen problem and proposed solution approach.</div></div>
+        <div class="phase-deliverable"><div class="phase-deliverable-title">Challenge Area Alignment</div><div class="phase-deliverable-text">Clear articulation of the chosen challenge and proposed solution approach.</div></div>
         <div class="phase-deliverable"><div class="phase-deliverable-title">Technical Architecture</div><div class="phase-deliverable-text">System design, model selection and Arm backend selection.</div></div>
         <div class="phase-deliverable"><div class="phase-deliverable-title">Working Code Repository</div><div class="phase-deliverable-text">A public or shared GitHub repository with runnable code, README, setup instructions and at least a basic working prototype.</div></div>
         <div class="phase-deliverable"><div class="phase-deliverable-title">Performance Baseline</div><div class="phase-deliverable-text">Initial benchmarking results or projections for on-device latency, memory footprint and accuracy.</div></div>
         <div class="phase-deliverable"><div class="phase-deliverable-title">Demo Video</div><div class="phase-deliverable-text">A 3–5-minute video demonstrating the working prototype.</div></div>
       </div>
-      <div class="phase-note">An expert panel evaluates Phase 1 submissions and shortlists the top teams per problem statement for Phase 2.</div>
+      <div class="phase-note">An expert panel evaluates Phase 1 submissions and shortlists the top teams per challenge area for Phase 2.</div>
     </section>
 
     <section class="ch-section phase-two">
@@ -128,23 +127,24 @@ <h2>Phase 2 — App Development Round (Invitation Only)</h2>
       <p>Shortlisted teams receive hardware support, dedicated mentorship, and cloud compute credits to build a fully functional app.</p>
       <div class="phase-deliverables">
         <div class="phase-deliverable"><div class="phase-deliverable-title">Fully Deployed App</div><div class="phase-deliverable-text">End-to-end on-device inference on target hardware, demonstrating all claimed use cases.</div></div>
-        <div class="phase-deliverable"><div class="phase-deliverable-title">Optimization Report</div><div class="phase-deliverable-text">Quantitative benchmarking across latency, memory and accuracy, with comparison of at least two backend/quantization configurations.</div></div>
+        <div class="phase-deliverable"><div class="phase-deliverable-title">Optimisation Report</div><div class="phase-deliverable-text">Quantitative benchmarking across latency, memory and accuracy, with comparison of at least two backend/quantisation configurations.</div></div>
         <div class="phase-deliverable"><div class="phase-deliverable-title">Updated Codebase</div><div class="phase-deliverable-text">Production-quality code with documentation suitable for handover.</div></div>
         <div class="phase-deliverable"><div class="phase-deliverable-title">Live Virtual Demo</div><div class="phase-deliverable-text">Presented to a jury panel with a Q&amp;A session.</div></div>
       </div>
     </section>
 
     <section class="ch-section ch-problem-statements">
-      <h2>Project Problem Statements</h2>
+      <h2>Project Challenge Areas</h2>
       <details class="ch-accordion"><summary><span class="ch-acc-title">1. AI Consultation Preparation Assistant for Patients</span> <span class="ch-acc-domain">Domain: Healthcare</span></summary><div class="ch-acc-body">
         <p><strong>Domain:</strong> Healthcare<br><strong>Applicable for:</strong> Students</p>
-        <p>In Karnataka's Tier-2 and Tier-3 towns and rural taluks, patients accessing online consultations can struggle to organize symptoms, medical history, prescriptions, reports and questions before a consultation, particularly when the consultation is in English and the patient is more comfortable in Kannada or any other Indian language.</p>
+        <p>In Karnataka's Tier-2 and Tier-3 towns and rural taluks, patients accessing online consultations can struggle to organise symptoms, medical history, prescriptions, reports and questions before a consultation, particularly when the consultation is in English and the patient is more comfortable in Kannada or any other Indian language.</p>
         <p>Build an Arm64-native consultation-preparation application that uses AI to transform patient-provided notes and documents into a patient-friendly summary, relevant questions for the doctor and a consultation checklist, with Kannada or any other Indian language as a core experience.</p>
-        <p>The solution should prioritize privacy-conscious processing, useful offline/local workflows where feasible, and efficient Arm64 execution.</p>
+        <div class="ch-note"><p><strong>Disclaimer:</strong> This solution is intended solely as a prototype/proof of concept for development, research and demonstration purposes. It is not medically validated, certified or approved and must not be used for diagnosis, treatment, clinical decision-making or real-world patient care.</p></div>
+        <p>The solution should prioritise privacy-conscious processing, useful offline/local workflows where feasible, and efficient Arm64 execution.</p>
         <p>Students may develop, test and demonstrate the solution using the free Azure account and B2pts v2 Arm-based burstable VM allocation (up to 750 hours/month).</p>
         <p>Physical WoA hardware, NPU access and SME2 are not prerequisites.</p>
         <p>Teams should document which parts run locally on the Arm VM, any cloud services used, latency and resource usage, and how the architecture could map to a future Windows on Arm client.</p>
-        <p>Teams are encouraged to use ONNX Runtime for deploying and running AI models efficiently on Arm64 and explore Arm KleidiAI integration/optimizations, where applicable, to build an Arm-optimized AI software stack.</p>
+        <p>Teams are encouraged to use ONNX Runtime for deploying and running AI models efficiently on Arm64 and explore Arm KleidiAI integration/optimisations, where applicable, to build an Arm-optimised AI software stack.</p>
         <p>The Azure Arm64 VM can be used for development, model conversion, compilation, functional validation, inference and performance/resource analysis.</p>
         <p>Teams should document:</p>
         <ul>
@@ -152,73 +152,74 @@ <h2>Project Problem Statements</h2>
           <li>ONNX Runtime/KleidiAI integration where applicable</li>
           <li>components running locally on the Arm VM</li>
           <li>any external cloud services used</li>
-          <li>latency and resource utilization</li>
+          <li>latency and resource utilisation</li>
           <li>how the architecture could subsequently map to a Windows on Arm client</li>
-          <li>potential path to SME2-based optimization on compatible hardware</li>
+          <li>potential path to SME2-based optimisation on compatible hardware</li>
         </ul>
         <h3>Checklist</h3>
-        <div class="ch-table-scroll" tabindex="0" role="region" aria-label="Problem statement 1 checklist">
-          <table class="ch-table ch-table-compact ch-ps-table ch-ps-checklist"><thead><tr><th>Participant category</th><th>Azure Arm64 access</th><th>AI inference optimization</th><th>CPU optimization</th><th>Use of KleidiAI</th><th>Primary technical validation</th></tr></thead><tbody>
+        <div class="ch-table-scroll" tabindex="0" role="region" aria-label="Challenge area 1 checklist">
+          <table class="ch-table ch-table-compact ch-ps-table ch-ps-checklist"><thead><tr><th>Participant category</th><th>Azure Arm64 access</th><th>AI inference optimisation</th><th>CPU optimisation</th><th>Use of KleidiAI</th><th>Primary technical validation</th></tr></thead><tbody>
             <tr><td>Students</td><td>Free B2pts v2 Arm VM; $100 Azure credit for student account.</td><td>Recommended</td><td>Recommended</td><td>Optional / where applicable</td><td>Arm64-native AI workflow; latency; CPU/memory efficiency; privacy/offline design; Windows readiness</td></tr>
           </tbody></table>
         </div>
         <h3>Evaluation Criteria</h3>
-        <div class="ch-table-scroll" tabindex="0" role="region" aria-label="Problem statement 1 evaluation criteria">
+        <div class="ch-table-scroll" tabindex="0" role="region" aria-label="Challenge area 1 evaluation criteria">
           <table class="ch-table ch-table-compact ch-ps-table ch-ps-evaluation"><thead><tr><th>Evaluation Criterion</th><th>Weight</th><th>What Judges Assess</th></tr></thead><tbody>
             <tr><td>Usefulness &amp; Patient Experience</td><td>20%</td><td>Quality/relevance of summaries, questions and checklist; Kannada/other Indian language-first usability.</td></tr>
             <tr><td>AI Output Quality &amp; Latency</td><td>20%</td><td>Faithfulness, relevance and end-to-end response time on the stated Arm VM environment.</td></tr>
             <tr><td>Privacy, Offline/Local Design</td><td>15%</td><td>Privacy-conscious architecture and degree to which core processing can operate locally or with limited connectivity.</td></tr>
             <tr><td>Arm64-Native Implementation</td><td>15%</td><td>Arm64-native components, dependency choices and avoidance of unnecessary x64-only components.</td></tr>
-            <tr><td>Arm VM Efficiency &amp; Reproducibility</td><td>15%</td><td>CPU/memory utilization, responsiveness and repeatable measurements on the Azure Arm VM.</td></tr>
+            <tr><td>Arm VM Efficiency &amp; Reproducibility</td><td>15%</td><td>CPU/memory utilisation, responsiveness and repeatable measurements on the Azure Arm VM.</td></tr>
             <tr><td>Engineering Quality &amp; Windows Readiness</td><td>15%</td><td>Reliability, testing, packaging/design readiness and a credible path to a Windows on Arm client.</td></tr>
           </tbody></table>
         </div>
       </div></details>
-      <details class="ch-accordion"><summary><span class="ch-acc-title">2. Personalized Study Guide, Planner and Knowledge Copilot for Students</span> <span class="ch-acc-domain">Domain: EdTech</span></summary><div class="ch-acc-body">
+      <details class="ch-accordion"><summary><span class="ch-acc-title">2. Personalised Study Guide, Planner and Knowledge Copilot for Students</span> <span class="ch-acc-domain">Domain: EdTech</span></summary><div class="ch-acc-body">
         <p><strong>Domain:</strong> EdTech<br><strong>Applicable for:</strong> Students</p>
         <p>Students often work with study material scattered across notes, PDFs, lecture summaries and slides, with no simple way to search, test understanding or plan revision.</p>
         <p>Build an Arm64-native study companion that ingests a student's material and uses AI to:</p>
-        <ul><li>answer questions</li><li>summarize topics</li><li>generate flashcards or quizzes</li><li>create a personalized revision plan</li></ul>
-        <p>The experience should remain useful with limited connectivity and should emphasize efficient Arm64 inference and a clean user workflow rather than complex integrations.</p>
+        <ul><li>answer questions</li><li>summarise topics</li><li>generate flashcards or quizzes</li><li>create a personalised revision plan</li></ul>
+        <p>The experience should remain useful with limited connectivity and should emphasise efficient Arm64 inference and a clean user workflow rather than complex integrations.</p>
         <p>Students may use the free Azure account and B2pts v2 Arm-based burstable VM allocation (up to 750 hours/month) for development, testing and demonstration.</p>
         <p>Physical WoA hardware, NPU access and SME2 are not mandatory.</p>
-        <p>Teams should report AI latency, throughput/resource utilization and reproducible Arm64 measurements, and explain how the implementation can transition to a native Windows on Arm application.</p>
-        <p>Teams are encouraged to use ONNX Runtime for efficient AI model deployment and inference on Arm64 and explore Arm KleidiAI integration/optimizations, where applicable, to build an Arm-optimized AI software stack.</p>
-        <p>The Azure Arm64 VM can be used for model conversion, development, compilation, functional validation, inference, benchmarking and resource-utilization analysis.</p>
+        <p>Teams should report AI latency, throughput/resource utilisation and reproducible Arm64 measurements, and explain how the implementation can transition to a native Windows on Arm application.</p>
+        <p>Teams are encouraged to use ONNX Runtime for efficient AI model deployment and inference on Arm64 and explore Arm KleidiAI integration/optimisations, where applicable, to build an Arm-optimised AI software stack.</p>
+        <p>The Azure Arm64 VM can be used for model conversion, development, compilation, functional validation, inference, benchmarking and resource-utilisation analysis.</p>
         <p>Teams should report:</p>
         <ul>
           <li>AI models and runtimes used</li>
           <li>ONNX Runtime/KleidiAI integration where applicable</li>
           <li>inference latency</li>
           <li>throughput</li>
-          <li>resource utilization</li>
+          <li>resource utilisation</li>
           <li>reproducible Arm64 measurements</li>
           <li>path to a native Windows on Arm client</li>
-          <li>potential path to SME2-based optimization on compatible hardware</li>
+          <li>potential path to SME2-based optimisation on compatible hardware</li>
         </ul>
         <h3>Checklist</h3>
-        <div class="ch-table-scroll" tabindex="0" role="region" aria-label="Problem statement 2 checklist">
-          <table class="ch-table ch-table-compact ch-ps-table ch-ps-checklist"><thead><tr><th>Participant category</th><th>Azure Arm64 access</th><th>AI inference optimization</th><th>CPU optimization</th><th>Use of KleidiAI</th><th>Primary technical validation</th></tr></thead><tbody>
+        <div class="ch-table-scroll" tabindex="0" role="region" aria-label="Challenge area 2 checklist">
+          <table class="ch-table ch-table-compact ch-ps-table ch-ps-checklist"><thead><tr><th>Participant category</th><th>Azure Arm64 access</th><th>AI inference optimisation</th><th>CPU optimisation</th><th>Use of KleidiAI</th><th>Primary technical validation</th></tr></thead><tbody>
             <tr><td>Students</td><td>Free B2pts v2 Arm VM; $100 Azure credit for student account.</td><td>Recommended</td><td>Recommended</td><td>Optional / where applicable</td><td>Arm64-native AI workflow; latency/throughput; CPU/memory efficiency; local study-data workflow</td></tr>
           </tbody></table>
         </div>
         <h3>Evaluation Criteria</h3>
-        <div class="ch-table-scroll" tabindex="0" role="region" aria-label="Problem statement 2 evaluation criteria">
+        <div class="ch-table-scroll" tabindex="0" role="region" aria-label="Challenge area 2 evaluation criteria">
           <table class="ch-table ch-table-compact ch-ps-table ch-ps-evaluation"><thead><tr><th>Evaluation Criterion</th><th>Weight</th><th>What Judges Assess</th></tr></thead><tbody>
             <tr><td>Learning Usefulness &amp; UX</td><td>20%</td><td>Effectiveness of Q&amp;A, summaries, flashcards/quizzes and revision planning.</td></tr>
             <tr><td>AI Quality &amp; Latency</td><td>20%</td><td>Grounding in supplied study material and measured response time.</td></tr>
             <tr><td>Offline / Limited-Connectivity Capability</td><td>15%</td><td>Usefulness when network access is limited and sensible data handling.</td></tr>
             <tr><td>Arm64-Native Implementation</td><td>15%</td><td>Native Arm64 build/dependencies and efficient execution.</td></tr>
-            <tr><td>Arm VM Performance</td><td>15%</td><td>Throughput, CPU/memory utilization and reproducible performance evidence.</td></tr>
+            <tr><td>Arm VM Performance</td><td>15%</td><td>Throughput, CPU/memory utilisation and reproducible performance evidence.</td></tr>
             <tr><td>Engineering Quality &amp; Windows Readiness</td><td>15%</td><td>File ingestion, reliability, testing, documentation and path to native Windows on Arm.</td></tr>
           </tbody></table>
         </div>
       </div></details>
-      <details class="ch-accordion"><summary><span class="ch-acc-title">3. Telemedicine Consultation Copilot with Local AI Summarization and Clinical Workflow Support</span> <span class="ch-acc-domain">Domain: Healthcare</span></summary><div class="ch-acc-body">
+      <details class="ch-accordion"><summary><span class="ch-acc-title">3. Telemedicine Consultation Copilot with Local AI Summarisation and Clinical Workflow Support</span> <span class="ch-acc-domain">Domain: Healthcare</span></summary><div class="ch-acc-body">
         <p><strong>Domain:</strong> Healthcare<br><strong>Applicable for:</strong> Working Professionals / Developers</p>
         <p>Clinicians conducting remote consultations can spend significant time documenting symptoms, medications, investigations and follow-up actions, reducing time available for patient care and increasing the risk of omissions.</p>
         <p>Develop an Arm64-native telemedicine consultation copilot that captures or imports consultation transcripts, extracts relevant clinical details and generates both:</p>
         <ul><li>a structured clinician-facing note</li><li>a separate patient-friendly summary</li></ul>
+        <div class="ch-note"><p><strong>Disclaimer:</strong> This solution is intended solely as a prototype/proof of concept for development, research and demonstration purposes. It is not medically validated, certified or approved and must not be used for diagnosis, treatment, clinical decision-making or real-world patient care.</p></div>
         <p>Kannada/other Indian language should be a first-class language.</p>
         <p>Optional extensions include:</p>
         <ul><li>additional regional languages</li><li>retrieval from medical reference documents</li><li>structured follow-up planning</li></ul>
@@ -226,24 +227,24 @@ <h3>Evaluation Criteria</h3>
         <p>Shortlisted Phase 2 teams may use the stated $200 Azure credit to scale or deepen the PoC.</p>
         <p>SME2 is not required or scored.</p>
         <p>Evaluation should focus on available Arm64-native execution and measurable CPU/resource performance rather than unavailable device-specific NPU/battery metrics, including:</p>
-        <ul><li>Arm64-native execution</li><li>output quality</li><li>latency</li><li>reproducibility</li><li>privacy-conscious architecture</li><li>CPU/resource optimization</li><li>practical workflow usefulness</li></ul>
-        <p>Teams are encouraged to use ONNX Runtime for efficient AI model deployment and inference on Arm64 and explore Arm KleidiAI integration/optimizations, where applicable, to build an Arm-optimized AI software stack.</p>
-        <p>The Azure Arm64 VM may be used for model conversion, development, compilation, functional validation, inference, benchmarking and resource-utilization analysis.</p>
+        <ul><li>Arm64-native execution</li><li>output quality</li><li>latency</li><li>reproducibility</li><li>privacy-conscious architecture</li><li>CPU/resource optimisation</li><li>practical workflow usefulness</li></ul>
+        <p>Teams are encouraged to use ONNX Runtime for efficient AI model deployment and inference on Arm64 and explore Arm KleidiAI integration/optimisations, where applicable, to build an Arm-optimised AI software stack.</p>
+        <p>The Azure Arm64 VM may be used for model conversion, development, compilation, functional validation, inference, benchmarking and resource-utilisation analysis.</p>
         <p>Teams should also document:</p>
-        <ul><li>AI models and runtimes</li><li>ONNX Runtime/KleidiAI integration where applicable</li><li>transition path to a Windows on Arm client</li><li>potential path to SME2-based optimization on compatible hardware</li></ul>
+        <ul><li>AI models and runtimes</li><li>ONNX Runtime/KleidiAI integration where applicable</li><li>transition path to a Windows on Arm client</li><li>potential path to SME2-based optimisation on compatible hardware</li></ul>
         <h3>Checklist</h3>
-        <div class="ch-table-scroll" tabindex="0" role="region" aria-label="Problem statement 3 checklist">
-          <table class="ch-table ch-table-compact ch-ps-table ch-ps-checklist"><thead><tr><th>Participant category</th><th>Azure Arm64 access</th><th>AI inference optimization</th><th>CPU optimization</th><th>Use of KleidiAI</th><th>Primary technical validation</th></tr></thead><tbody>
-            <tr><td>Working Professionals / Developers</td><td>Free B2pts v2 Arm VM; $200 Azure credit for new account.</td><td>Recommended</td><td>Required</td><td>Optional / where applicable</td><td>Arm64-native execution; CPU optimization; AI latency/throughput/memory; reproducibility; clinical workflow quality</td></tr>
+        <div class="ch-table-scroll" tabindex="0" role="region" aria-label="Challenge area 3 checklist">
+          <table class="ch-table ch-table-compact ch-ps-table ch-ps-checklist"><thead><tr><th>Participant category</th><th>Azure Arm64 access</th><th>AI inference optimisation</th><th>CPU optimisation</th><th>Use of KleidiAI</th><th>Primary technical validation</th></tr></thead><tbody>
+            <tr><td>Working Professionals / Developers</td><td>Free B2pts v2 Arm VM; $200 Azure credit for new account.</td><td>Recommended</td><td>Required</td><td>Optional / where applicable</td><td>Arm64-native execution; CPU optimisation; AI latency/throughput/memory; reproducibility; clinical workflow quality</td></tr>
           </tbody></table>
         </div>
         <h3>Evaluation Criteria</h3>
-        <div class="ch-table-scroll" tabindex="0" role="region" aria-label="Problem statement 3 evaluation criteria">
+        <div class="ch-table-scroll" tabindex="0" role="region" aria-label="Challenge area 3 evaluation criteria">
           <table class="ch-table ch-table-compact ch-ps-table ch-ps-evaluation"><thead><tr><th>Evaluation Criterion</th><th>Weight</th><th>What Judges Assess</th></tr></thead><tbody>
             <tr><td>Clinical Workflow Usefulness &amp; Output Quality</td><td>25%</td><td>Completeness and practical usefulness of extracted details, clinician note and patient summary; no diagnosis requirement.</td></tr>
-            <tr><td>AI Accuracy &amp; Latency</td><td>20%</td><td>Faithfulness to consultation input, extraction/summarization quality and measured latency.</td></tr>
+            <tr><td>AI Accuracy &amp; Latency</td><td>20%</td><td>Faithfulness to consultation input, extraction/summarisation quality and measured latency.</td></tr>
             <tr><td>Language, Privacy &amp; Local Design</td><td>15%</td><td>Kannada/other Indian language-first workflow, optional regional languages and privacy-conscious processing.</td></tr>
-            <tr><td>Arm64-Native Implementation &amp; CPU Optimization</td><td>15%</td><td>Native Arm64 components, optimized CPU execution and dependency quality.</td></tr>
+            <tr><td>Arm64-Native Implementation &amp; CPU Optimisation</td><td>15%</td><td>Native Arm64 components, optimised CPU execution and dependency quality.</td></tr>
             <tr><td>Azure Arm Performance &amp; Reproducibility</td><td>10%</td><td>Repeatable latency/throughput/memory/CPU measurements on the provided Arm VM.</td></tr>
             <tr><td>Engineering Quality &amp; Scalability</td><td>15%</td><td>Robustness, testing, documentation, deployment quality and effective use of Phase 2 Azure resources.</td></tr>
           </tbody></table>
@@ -252,35 +253,35 @@ <h3>Evaluation Criteria</h3>
       <details class="ch-accordion"><summary><span class="ch-acc-title">4. Real-Time AI Gaming Coach and Multimedia Highlights Studio</span> <span class="ch-acc-domain">Domain: Gaming</span></summary><div class="ch-acc-body">
         <p><strong>Domain:</strong> Gaming<br><strong>Applicable for:</strong> Working Professionals / Developers</p>
         <p>Build an Arm64-native AI gaming and multimedia analysis application that processes recorded gameplay clips or suitable streamed/video inputs, detects and tags key events, and generates coaching insights and highlight summaries through an assistant-style experience.</p>
-        <p>The pipeline may combine computer vision with audio/text metadata and should be profiled and optimized for Arm64 execution.</p>
+        <p>The pipeline may combine computer vision with audio/text metadata and should be profiled and optimised for Arm64 execution.</p>
         <p>Phase 1 should run on the free Azure B2pts v2 Arm-based VM environment (up to 750 hours/month), with the stated $200 Azure credit available to shortlisted teams in Phase 2.</p>
-        <p>SME2, NPU utilization, battery measurements and device-only real-time capture are not mandatory.</p>
+        <p>SME2, NPU utilisation, battery measurements and device-only real-time capture are not mandatory.</p>
         <p>Teams should instead demonstrate measurable improvements through reproducible Arm64 profiling, for example:</p>
-        <ul><li>inference latency</li><li>throughput</li><li>CPU utilization</li><li>memory footprint</li><li>processing time per minute of video</li><li>cost-efficiency</li></ul>
+        <ul><li>inference latency</li><li>throughput</li><li>CPU utilisation</li><li>memory footprint</li><li>processing time per minute of video</li><li>cost-efficiency</li></ul>
         <p>Use a clearly defined baseline where a valid comparison can be produced.</p>
-        <p>Teams are encouraged to use ONNX Runtime for efficient AI model deployment and inference on Arm64 and explore Arm KleidiAI integration/optimizations, where applicable, to build an Arm-optimized AI software stack.</p>
-        <p>The Azure Arm64 VM may be used for model conversion, development, compilation, functional validation, inference, benchmarking and resource-utilization analysis.</p>
+        <p>Teams are encouraged to use ONNX Runtime for efficient AI model deployment and inference on Arm64 and explore Arm KleidiAI integration/optimisations, where applicable, to build an Arm-optimised AI software stack.</p>
+        <p>The Azure Arm64 VM may be used for model conversion, development, compilation, functional validation, inference, benchmarking and resource-utilisation analysis.</p>
         <p>Teams should also document:</p>
-        <ul><li>AI models and runtimes</li><li>ONNX Runtime/KleidiAI integration where applicable</li><li>path to a Windows on Arm client</li><li>potential path to SME2-based optimization on compatible hardware</li></ul>
+        <ul><li>AI models and runtimes</li><li>ONNX Runtime/KleidiAI integration where applicable</li><li>path to a Windows on Arm client</li><li>potential path to SME2-based optimisation on compatible hardware</li></ul>
         <h3>Checklist</h3>
-        <div class="ch-table-scroll" tabindex="0" role="region" aria-label="Problem statement 4 checklist">
-          <table class="ch-table ch-table-compact ch-ps-table ch-ps-checklist"><thead><tr><th>Participant category</th><th>Azure Arm64 access</th><th>AI inference optimization</th><th>CPU optimization</th><th>Use of KleidiAI</th><th>Primary technical validation</th></tr></thead><tbody>
+        <div class="ch-table-scroll" tabindex="0" role="region" aria-label="Challenge area 4 checklist">
+          <table class="ch-table ch-table-compact ch-ps-table ch-ps-checklist"><thead><tr><th>Participant category</th><th>Azure Arm64 access</th><th>AI inference optimisation</th><th>CPU optimisation</th><th>Use of KleidiAI</th><th>Primary technical validation</th></tr></thead><tbody>
             <tr><td>Working Professionals / Developers</td><td>Free B2pts v2 Arm VM; $200 Azure credit for new account.</td><td>Recommended</td><td>Required</td><td>Optional / where applicable</td><td>Arm64 profiling; media/AI throughput; latency; CPU/memory efficiency; repeatable baseline comparison where valid</td></tr>
           </tbody></table>
         </div>
         <h3>Evaluation Criteria</h3>
-        <div class="ch-table-scroll" tabindex="0" role="region" aria-label="Problem statement 4 evaluation criteria">
+        <div class="ch-table-scroll" tabindex="0" role="region" aria-label="Challenge area 4 evaluation criteria">
           <table class="ch-table ch-table-compact ch-ps-table ch-ps-evaluation"><thead><tr><th>Evaluation Criterion</th><th>Weight</th><th>What Judges Assess</th></tr></thead><tbody>
             <tr><td>Arm64 Performance Improvement</td><td>25%</td><td>Measured improvement against a clearly defined and valid baseline using cloud-accessible metrics.</td></tr>
             <tr><td>AI / Multimedia Pipeline Quality</td><td>20%</td><td>Robust event detection, tagging, coaching insights and highlight generation.</td></tr>
-            <tr><td>Throughput &amp; Resource Efficiency</td><td>15%</td><td>Inference latency, throughput, CPU utilization, memory footprint and processing efficiency.</td></tr>
+            <tr><td>Throughput &amp; Resource Efficiency</td><td>15%</td><td>Inference latency, throughput, CPU utilisation, memory footprint and processing efficiency.</td></tr>
             <tr><td>User Experience &amp; Usefulness</td><td>15%</td><td>Actionable output and clear value to gamers/creators; demo usability.</td></tr>
             <tr><td>Native Arm64 Completeness</td><td>10%</td><td>Arm64-native application/pipeline and dependencies where feasible.</td></tr>
             <tr><td>Engineering &amp; Benchmark Reproducibility</td><td>15%</td><td>Repeatable methodology, profiling evidence, testing, reliability and documentation.</td></tr>
           </tbody></table>
         </div>
       </div></details>
-      <details class="ch-accordion"><summary><span class="ch-acc-title">5. Desktop Application Migration &amp; Optimization with Performance Benchmarking Suite</span> <span class="ch-acc-domain">Domain: App Migration / Open Source / Performance</span></summary><div class="ch-acc-body">
+      <details class="ch-accordion"><summary><span class="ch-acc-title">5. Desktop Application Migration &amp; Optimisation with Performance Benchmarking Suite</span> <span class="ch-acc-domain">Domain: App Migration / Open Source / Performance</span></summary><div class="ch-acc-body">
         <p><strong>Domain:</strong> App Migration / Open Source / Performance<br><strong>Applicable for:</strong> Students &amp; Working Professionals / Developers</p>
         <p>Select an existing, meaningful Windows application, open-source library, runtime, framework, SDK or developer tool that people or developers actually use and advance it toward a complete native Arm64 experience.</p>
         <p>Teams may pursue:</p>
@@ -289,28 +290,28 @@ <h3>Port It Native</h3>
         <h3>Open Source Hero / Unlock the Ecosystem</h3>
         <p>Make an important dependency or developer technology Arm64-native and unlock downstream applications.</p>
         <h3>Faster on Arm</h3>
-        <p>Optimize an existing workload and demonstrate a meaningful native Arm64 performance improvement.</p>
+        <p>Optimise an existing workload and demonstrate a meaningful native Arm64 performance improvement.</p>
         <p>Resolve real migration issues such as:</p>
         <ul><li>x86/x64-only dependencies</li><li>native library recompilation</li><li>build-system changes</li><li>CI/testing</li><li>SDK compatibility</li><li>installers</li><li>packaging</li></ul>
         <p>Students may use the free Azure account with B2pts v2 Arm-based burstable VM access (up to 750 hours/month).</p>
         <p>Working professionals/developers use the same free Arm VM environment in Phase 1 and may use the stated $200 Azure credit in Phase 2.</p>
-        <p>Where Windows-specific GUI or installer behavior cannot be exercised on the provided Azure Arm VM, teams should clearly separate:</p>
+        <p>Where Windows-specific GUI or installer behaviour cannot be exercised on the provided Azure Arm VM, teams should clearly separate:</p>
         <ul><li>what has been implemented</li><li>what has been cross-compiled</li><li>what has been CI-validated</li><li>what remains device-dependent</li></ul>
         <p>Judging should reward:</p>
         <ul><li>native-port completeness</li><li>native dependencies</li><li>ecosystem impact</li><li>technical difficulty</li><li>implementation quality</li><li>reproducible Arm64 benchmarking</li><li>CI/testing</li><li>upstream contribution</li><li>user/developer impact</li></ul>
-        <p>For AI/ML applications, runtimes or frameworks, teams are encouraged to use ONNX Runtime for efficient Arm64 inference and explore Arm KleidiAI integration/optimizations where applicable.</p>
+        <p>For AI/ML applications, runtimes or frameworks, teams are encouraged to use ONNX Runtime for efficient Arm64 inference and explore Arm KleidiAI integration/optimisations where applicable.</p>
         <p>ONNX Runtime/KleidiAI usage should be applied only where relevant and is not mandatory for non-AI software ports.</p>
         <p>For applicable AI workloads, teams should additionally demonstrate and document:</p>
-        <ul><li>ONNX Runtime/KleidiAI integration</li><li>inference performance</li><li>CPU/resource optimization</li></ul>
+        <ul><li>ONNX Runtime/KleidiAI integration</li><li>inference performance</li><li>CPU/resource optimisation</li></ul>
         <h3>Checklist</h3>
-        <div class="ch-table-scroll" tabindex="0" role="region" aria-label="Problem statement 5 checklist">
-          <table class="ch-table ch-table-compact ch-ps-table ch-ps-checklist"><thead><tr><th>Participant category</th><th>Azure Arm64 access</th><th>AI inference optimization</th><th>CPU optimization</th><th>Use of KleidiAI</th><th>Primary technical validation</th></tr></thead><tbody>
+        <div class="ch-table-scroll" tabindex="0" role="region" aria-label="Challenge area 5 checklist">
+          <table class="ch-table ch-table-compact ch-ps-table ch-ps-checklist"><thead><tr><th>Participant category</th><th>Azure Arm64 access</th><th>AI inference optimisation</th><th>CPU optimisation</th><th>Use of KleidiAI</th><th>Primary technical validation</th></tr></thead><tbody>
             <tr><td>Students</td><td>Free B2pts v2 Arm VM; $100 Azure credit for new account.</td><td>As applicable to selected project</td><td>Required</td><td>Optional / if AI workload</td><td>Native Arm64 port/dependencies; build &amp; CI; reproducible benchmarks; ecosystem impact</td></tr>
             <tr><td>Working Professionals / Developers</td><td>Free B2pts v2 Arm VM; $200 Azure credit for new account.</td><td>As applicable to selected project</td><td>Required</td><td>Optional / if AI workload</td><td>Native Arm64 port/dependencies; CI/testing; reproducible benchmarks; upstream acceptance &amp; ecosystem impact</td></tr>
           </tbody></table>
         </div>
         <h3>Evaluation Criteria</h3>
-        <div class="ch-table-scroll" tabindex="0" role="region" aria-label="Problem statement 5 evaluation criteria">
+        <div class="ch-table-scroll" tabindex="0" role="region" aria-label="Challenge area 5 evaluation criteria">
           <table class="ch-table ch-table-compact ch-ps-table ch-ps-evaluation"><thead><tr><th>Evaluation Criterion</th><th>Weight</th><th>What Judges Assess</th></tr></thead><tbody>
             <tr><td>Completeness of Native Arm64 Port / Enablement</td><td>20%</td><td>Functional completeness and reduction/elimination of x86/x64-only dependencies within the accessible environment.</td></tr>
             <tr><td>Ecosystem / User Impact</td><td>20%</td><td>Importance/popularity of project and downstream users/apps/developers unlocked.</td></tr>
@@ -322,8 +323,8 @@ <h3>Evaluation Criteria</h3>
         </div>
       </div></details>
       <div class="ch-note">
-        <p>Azure VM availability is subject to Microsoft subscription, region, quota and capacity restrictions. Teams should use the Azure Free Services configuration where available and report any subscription or regional limitations to the organizers.</p>
-        <p>Teams are not expected to run large foundation models entirely on the B2pts v2 VM. Lightweight, quantized, distilled or otherwise resource-efficient models are encouraged. Cloud AI services may be used where appropriate, but Arm64-local components must be clearly identified and benchmarked where required.</p>
+        <p>Azure VM availability is subject to Microsoft subscription, region, quota and capacity restrictions. Teams should use the Azure Free Services configuration where available and report any subscription or regional limitations to the organisers.</p>
+        <p>Teams are not expected to run large foundation models entirely on the B2pts v2 VM. Lightweight, quantised, distilled or otherwise resource-efficient models are encouraged. Cloud AI services may be used where appropriate, but Arm64-local components must be clearly identified and benchmarked where required.</p>
       </div>
     </section>
 
@@ -331,16 +332,16 @@ <h3>Evaluation Criteria</h3>
       <h2>Eligibility Criteria</h2>
       <p>The challenge is open to Indian nationals in two categories.</p>
       <table class="ch-table"><thead><tr><th>Category</th><th>Who qualifies</th><th>Team Lead Requirement</th></tr></thead><tbody>
-        <tr><td>Students</td><td>UG or PG students enrolled in a regular full-time course at a recognized Indian HEI with a background in AI/ML, digital systems, or software.</td><td>Team Leader must be an enrolled student. Faculty Mentor required.</td></tr>
+        <tr><td>Students</td><td>UG or PG students enrolled in a regular full-time course at a recognised Indian HEI with a background in AI/ML, digital systems, or software.</td><td>Team Leader must be an enrolled student. Faculty Mentor required.</td></tr>
         <tr><td>Working Professionals / Developers</td><td>Individuals currently employed in industry, startups, or independent developers with demonstrable technical experience in AI/ML or embedded systems.</td><td>Team Leader is a professional. Faculty Mentor not required.</td></tr>
       </tbody></table>
       <h3>Team Rules</h3><ul>
-        <li>Team size: 1 to 3 members per team.</li><li>Students: All members must be from the same institution and campus; they may span different branches and semesters.</li><li>Faculty Mentor: Each student team should have a Faculty Mentor.</li><li>Professional teams: Members may be from different organizations.</li><li>Problem statement: Each team selects one problem statement.</li><li>One change of team composition or problem statement is permitted within 15 days of registration.</li>
+        <li>Team size: 1 to 3 members per team.</li><li>Students: All members must be from the same institution and campus; they may span different branches and semesters.</li><li>Faculty Mentor: Each student team should have a Faculty Mentor.</li><li>Professional teams: Members may be from different organisations.</li><li>Challenge area: Each team selects one challenge area.</li><li>One change of team composition or challenge area is permitted within 15 days of registration.</li>
       </ul>
     </section>
 
     <section class="ch-section ch-section--featured"><h2>Recognition &amp; Rewards</h2><ul>
-      <li>Participants who submit a working project will receive a certificate of participation.</li><li>The Top 40 finalists will receive Arm-branded merchandise.</li><li>Winning teams across each of the five problem statements will receive cash awards.</li><li>A few of the top winning projects may be featured at national-level industry conferences.</li>
+      <li>Participants who submit a working project will receive a certificate of participation.</li><li>Arm-branded merchandise for all finalists.</li><li>IIT Delhi will provide a cash award of ₹1 lakh to each winning team, with one winning team selected for each challenge area.</li><li>The Top 20 finalists will have the opportunity to enrol in advanced Edge AI programmes involving IIIT Bangalore, NASSCOM CoE and Arm, or to have their projects from this challenge showcased at national-level industry conferences.</li>
     </ul></section>
 
 <section class="ch-section ch-section--featured" id="registration">
@@ -367,14 +368,20 @@ <h2>Registration</h2>
       <details class="ch-accordion"><summary><span class="ch-acc-title">2. How many people can be in a team?</span></summary><div class="ch-acc-body"><p>1 to 3 members.</p></div></details>
       <details class="ch-accordion"><summary><span class="ch-acc-title">3. Can students from different institutions form a team?</span></summary><div class="ch-acc-body"><p>No. Student team members must be from the same institution and campus, although they may come from different branches and semesters.</p></div></details>
       <details class="ch-accordion"><summary><span class="ch-acc-title">4. Do student teams need a Faculty Mentor?</span></summary><div class="ch-acc-body"><p>Yes. Each student team should have a Faculty Mentor.</p></div></details>
-      <details class="ch-accordion"><summary><span class="ch-acc-title">5. Can professionals from different organizations form a team?</span></summary><div class="ch-acc-body"><p>Yes.</p></div></details>
-      <details class="ch-accordion"><summary><span class="ch-acc-title">6. Which challenges can students enter?</span></summary><div class="ch-acc-body"><p>Challenges 1, 2 and 5.</p></div></details>
-      <details class="ch-accordion"><summary><span class="ch-acc-title">7. Which challenges can professionals/developers enter?</span></summary><div class="ch-acc-body"><p>Challenges 3, 4 and 5.</p></div></details>
-      <details class="ch-accordion"><summary><span class="ch-acc-title">8. Can a team change its composition or problem statement?</span></summary><div class="ch-acc-body"><p>One change of team composition or problem statement is permitted within 15 days of registration.</p></div></details>
-      <details class="ch-accordion"><summary><span class="ch-acc-title">9. What happens in Phase 1?</span></summary><div class="ch-acc-body"><p>Participants submit problem statement alignment, technical architecture, a working code repository, a performance baseline and a 3–5-minute demo video.</p></div></details>
+      <details class="ch-accordion"><summary><span class="ch-acc-title">5. Can professionals from different organisations form a team?</span></summary><div class="ch-acc-body"><p>Yes.</p></div></details>
+      <details class="ch-accordion"><summary><span class="ch-acc-title">6. Which challenge areas can students enter?</span></summary><div class="ch-acc-body"><p>Challenge Areas 1, 2 and 5.</p></div></details>
+      <details class="ch-accordion"><summary><span class="ch-acc-title">7. Which challenge areas can professionals/developers enter?</span></summary><div class="ch-acc-body"><p>Challenge Areas 3, 4 and 5.</p></div></details>
+      <details class="ch-accordion"><summary><span class="ch-acc-title">8. Can a team change its composition or challenge area?</span></summary><div class="ch-acc-body"><p>One change of team composition or challenge area is permitted within 15 days of registration.</p></div></details>
+      <details class="ch-accordion"><summary><span class="ch-acc-title">9. What happens in Phase 1?</span></summary><div class="ch-acc-body"><p>Participants submit challenge area alignment, technical architecture, a working code repository, a performance baseline and a 3–5-minute demo video.</p></div></details>
       <details class="ch-accordion"><summary><span class="ch-acc-title">10. What support do shortlisted teams receive?</span></summary><div class="ch-acc-body"><p>Hardware support, dedicated mentorship and cloud compute credits.</p></div></details>
       <details class="ch-accordion"><summary><span class="ch-acc-title">11. When are winners announced?</span></summary><div class="ch-acc-body"><p>19 November 2026 at Bengaluru Tech Summit 2026.</p></div></details>
-      <details class="ch-accordion"><summary><span class="ch-acc-title">12. What rewards are available?</span></summary><div class="ch-acc-body"><p>Participants who submit a working project will receive a certificate of participation. The Top 40 finalists will receive Arm-branded merchandise, and winning teams across all five problem statements will receive cash awards. A few of the top winning projects may also be featured at national-level industry conferences. Exact cash award amounts are not currently specified.</p></div></details>
+      <details class="ch-accordion"><summary><span class="ch-acc-title">12. What rewards are available?</span></summary><div class="ch-acc-body"><p>Participants who submit a working project will receive a certificate of participation. All finalists will receive Arm-branded merchandise. IIT Delhi will provide a cash award of ₹1 lakh to each winning team, with one winning team selected for each challenge area. The Top 20 finalists will also have the opportunity to enrol in advanced Edge AI programmes involving IIIT Bangalore, NASSCOM CoE and Arm, or to have their projects from this challenge showcased at national-level industry conferences.</p></div></details>
+      <details class="ch-accordion"><summary><span class="ch-acc-title">13. How should participants handle materials and intellectual property related to their project submission?</span></summary><div class="ch-acc-body">
+        <p>Participants are responsible for ensuring that they have all necessary rights, permissions and authorisations to use and submit any intellectual property, software, code, data, materials or other content used in connection with their participation in the Namma Smart AI Apps Challenge (“Participant Materials”).</p>
+        <p>Participants must not submit or disclose any confidential, proprietary or third-party information unless they are authorised to do so and such disclosure does not breach any confidentiality, employment, contractual or other obligation owed to any third party.</p>
+        <p>Participation in the Challenge does not require participants to disclose their own confidential information. Any Participant Materials voluntarily submitted or disclosed as part of the Challenge are provided at the participant’s own discretion and responsibility.</p>
+        <p>Each participant remains responsible for ensuring that their participation and submission do not infringe, misappropriate or otherwise violate any third-party intellectual property or other rights.</p>
+      </div></details>
     </section>
   </main>
 </div>
diff --git a/docs/_sass/components/_challengePage.scss b/docs/_sass/components/_challengePage.scss
index 8d8b5b92..4c2df18e 100644
--- a/docs/_sass/components/_challengePage.scss
+++ b/docs/_sass/components/_challengePage.scss
@@ -1418,12 +1418,17 @@ html {
   color: var(--text-muted);
 }
 
-.bharat-2026-challenge .bharat-whats-new .phase-deliverables,
-.bharat-2026-challenge .bharat-pipeline .phase-deliverables {
+.bharat-2026-challenge .bharat-whats-new .phase-deliverables {
   grid-template-columns: repeat(3, minmax(0, 1fr));
   align-items: stretch;
 }
 
+.bharat-2026-challenge .bharat-pipeline .phase-deliverables {
+  grid-template-columns: repeat(2, minmax(0, 316px));
+  justify-content: center;
+  align-items: stretch;
+}
+
 .bharat-2026-challenge .bharat-whats-new .phase-deliverable {
   border-top: 3px solid rgba(182, 101, 38, 0.65);
 }
@@ -1711,6 +1716,67 @@ html {
   box-shadow: inset 0 0 0 1px rgba(239, 191, 117, 0.18);
 }
 
+.bharat-2026-challenge .ch-register-cta {
+  margin: -44px 0 24px;
+}
+
+.bharat-2026-challenge #registration {
+  scroll-margin-top: 100px;
+}
+
+.bharat-2026-challenge .bharat-registration {
+  border-color: rgba(217, 154, 61, 0.58);
+  background:
+    radial-gradient(circle at 92% 12%, rgba(217, 154, 61, 0.14), transparent 30%),
+    linear-gradient(135deg, #20211e, #2c2b26);
+  box-shadow: inset 4px 0 0 #d99a3d,
+              0 10px 24px rgba(32, 32, 29, 0.18);
+}
+
+.bharat-2026-challenge .bharat-registration > h2 {
+  color: #fffaf2;
+}
+
+.bharat-2026-challenge .bharat-registration > h3 {
+  color: #efbf75;
+}
+
+.bharat-2026-challenge .bharat-registration > p,
+.bharat-2026-challenge .bharat-registration > ul li {
+  color: #f5eee4;
+}
+
+.bharat-2026-challenge .registration-form-wrapper {
+  border-color: rgba(217, 154, 61, 0.55);
+  background: #fffdfa;
+  box-shadow: inset 4px 0 0 rgba(217, 154, 61, 0.72),
+              0 8px 20px rgba(0, 0, 0, 0.16);
+}
+
+.bharat-2026-challenge .registration-form-wrapper input[type="text"],
+.bharat-2026-challenge .registration-form-wrapper input[type="email"],
+.bharat-2026-challenge .registration-form-wrapper input[type="tel"],
+.bharat-2026-challenge .registration-form-wrapper select,
+.bharat-2026-challenge .registration-form-wrapper textarea {
+  border-color: #c9ad91 !important;
+}
+
+.bharat-2026-challenge .registration-form-wrapper input:focus,
+.bharat-2026-challenge .registration-form-wrapper select:focus,
+.bharat-2026-challenge .registration-form-wrapper textarea:focus {
+  outline: 2px solid rgba(182, 101, 38, 0.3) !important;
+  border-color: #b66526 !important;
+}
+
+.bharat-2026-challenge .registration-form-wrapper .mktoButton:hover {
+  background: #b66526 !important;
+}
+
+.bharat-2026-challenge .registration-form-wrapper .mktoButton:focus-visible {
+  outline: 3px solid rgba(217, 154, 61, 0.46);
+  outline-offset: 3px;
+}
+
 .bharat-2026-challenge .ch-register-button {
   border: 2px solid #292823;
   background: #d99a3d;

From 72460e65de825488444b2f80333f74b53f3e3570 Mon Sep 17 00:00:00 2001
From: Habib-Banwo <habib.ogunbanwo@arm.com>
Date: Mon, 14 Sep 2026 15:30:42 +0100
Subject: [PATCH 3/3] Added FAQ & changed some wording

---
 docs/Bharat_AI_SoC_2026_27.html           | 102 +++++++++++++++++++++-
 docs/_sass/components/_challengePage.scss |  17 ++++
 2 files changed, 115 insertions(+), 4 deletions(-)

diff --git a/docs/Bharat_AI_SoC_2026_27.html b/docs/Bharat_AI_SoC_2026_27.html
index a90ac0fc..973b946a 100644
--- a/docs/Bharat_AI_SoC_2026_27.html
+++ b/docs/Bharat_AI_SoC_2026_27.html
@@ -357,7 +357,7 @@ <h2>Timeline</h2>
             <tr><td>Launch</td><td>19 September 2026</td><td>The Bharat AI-SoC Challenge 2026–27 launches at Semicon India 2026.</td></tr>
             <tr><td>Registration</td><td>19 September – 20 October 2026</td><td>Registration is open for eligible participants to form their teams and prepare for the challenge.</td></tr>
             <tr><td>Phase 1 — Open Qualifying</td><td>21 October – 21 December 2026</td><td>Teams develop their initial solution and submit their report, source code and demo.</td></tr>
-            <tr><td>Phase 1 Evaluation &amp; Shortlist</td><td>Mid-January 2027</td><td>Submissions are evaluated and shortlisted teams advance to the PoC Development phase.</td></tr>
+            <tr><td>Phase 1 Evaluation &amp; Shortlist</td><td>Mid of Jan 2027</td><td>Submissions are evaluated and shortlisted teams advance to the PoC Development phase.</td></tr>
             <tr><td>Phase 2 — PoC Development</td><td>End of January – 5 March 2027</td><td>Shortlisted teams develop and complete their proof of concept.</td></tr>
             <tr><td>Finals &amp; Winners' Announcement</td><td>10–25 March 2027</td><td>Finalists take part in the final evaluation, with one winner selected for each problem statement.</td></tr>
           </tbody>
@@ -372,9 +372,9 @@ <h2>Rewards &amp; Recognition</h2>
           <tr><th>Recognition</th><th>Recipients</th><th>Reward</th></tr>
         </thead>
         <tbody>
-          <tr><td data-label="Recognition">Winner</td><td data-label="Recipients">1 per problem statement (5 winners total)</td><td data-label="Reward">₹1,00,000 per winner</td></tr>
-          <tr><td data-label="Recognition">Certificates</td><td data-label="Recipients">All Phase 1 completions and Phase 2 shortlists</td><td data-label="Reward">Certificate</td></tr>
-          <tr><td data-label="Recognition">Finalist recognition</td><td data-label="Recipients">Finalist team members</td><td data-label="Reward">Branded swag from IIT Delhi</td></tr>
+          <tr><td data-label="Recognition">Winner</td><td data-label="Recipients">₹1,00,000 cahs Awareds by IIT Delhi fopr each Winning Team</td><td data-label="Reward">₹1,00,000 from IIT Delhi</td></tr>
+          <tr><td data-label="Recognition">Certificates</td><td data-label="Recipients">Certificates for all team members submitting completed projects</td><td data-label="Reward">Certificate</td></tr>
+          <tr><td data-label="Recognition">Finalist recognition</td><td data-label="Recipients">Finalist team members</td><td data-label="Reward">Branded swag</td></tr>
         </tbody>
       </table>
     </section>
@@ -414,5 +414,99 @@ <h3>Original Work Requirements</h3>
         </script>
       </div>
     </section>
+
+    <section class="ch-section bharat-faq">
+      <h2>Frequently Asked Questions</h2>
+
+      <details class="ch-accordion">
+        <summary>
+          <span class="ch-acc-title">1. Who can participate?</span>
+        </summary>
+        <div class="ch-acc-body">
+          <p>Indian nationals in the Student or Working Professional / Developer categories described in the eligibility section.</p>
+        </div>
+      </details>
+
+      <details class="ch-accordion">
+        <summary>
+          <span class="ch-acc-title">2. How many people can be in a team?</span>
+        </summary>
+        <div class="ch-acc-body">
+          <p>1 to 3 members.</p>
+        </div>
+      </details>
+
+      <details class="ch-accordion">
+        <summary>
+          <span class="ch-acc-title">3. Can students from different institutions form a team?</span>
+        </summary>
+        <div class="ch-acc-body">
+          <p>No. Student team members must be from the same institution and campus, although they may come from different branches and semesters.</p>
+        </div>
+      </details>
+
+      <details class="ch-accordion">
+        <summary>
+          <span class="ch-acc-title">4. Do student teams need a Faculty Mentor?</span>
+        </summary>
+        <div class="ch-acc-body">
+          <p>Yes. Each student team should have a Faculty Mentor.</p>
+        </div>
+      </details>
+
+      <details class="ch-accordion">
+        <summary>
+          <span class="ch-acc-title">5. Can professionals from different organisations form a team?</span>
+        </summary>
+        <div class="ch-acc-body">
+          <p>No. Professionals from different organisations cannot form a team.</p>
+        </div>
+      </details>
+
+      <details class="ch-accordion">
+        <summary>
+          <span class="ch-acc-title">6. Can a team change its composition or challenge area?</span>
+        </summary>
+        <div class="ch-acc-body">
+          <p>One change of team composition or challenge area is permitted within 15 days of registration.</p>
+        </div>
+      </details>
+
+      <details class="ch-accordion">
+        <summary>
+          <span class="ch-acc-title">7. Can the participants participate in this challenge by applying for more than 1 more problem statement?</span>
+        </summary>
+        <div class="ch-acc-body">
+          <p>No, the participants can apply for only 1 problem statement out of the given 5 problem statements in this challenge.</p>
+        </div>
+      </details>
+
+      <details class="ch-accordion">
+        <summary>
+          <span class="ch-acc-title">8. Who are the organizers of Bharat AI-SoC Student Challenge?</span>
+        </summary>
+        <div class="ch-acc-body">
+          <p>Bharat AI-SoC Student Challenge is jointly organized by Arm(arm.com) through its Arm Education program(Education – Arm®) and Ministry of Electronics & Information Technology(MeitY) through its C2S program(C2S). In addition to this Indian Institute of Technology(IIT),Delhi is the lead mentor to this challenge.</p>
+        </div>
+      </details>
+
+      <details class="ch-accordion">
+        <summary>
+          <span class="ch-acc-title">9. Participant Materials and Intellectual Property</span>
+        </summary>
+        <div class="ch-acc-body">
+          <p>Participants are responsible for ensuring that they have all necessary rights, permissions and authorisations to use and submit any intellectual property, software, code, data, materials or other content used in connection with their participation in the Bharat AI-SOC Challenge ("Participant Materials").</p>
+          <p>Participants must not submit or disclose any confidential, proprietary or third-party information unless they are authorised to do so and such disclosure does not breach any confidentiality, employment, contractual or other obligation owed to any third party.</p>
+          <p>Participation in the Challenge does not require participants to disclose their own confidential information. Any Participant Materials voluntarily submitted or disclosed as part of the Challenge are provided at the participant's own discretion and responsibility.</p>
+          <p>Each participant remains responsible for ensuring that their participation and submission do not infringe, misappropriate or otherwise violate any third-party intellectual property or other rights.</p>
+        </div>
+      </details>
+
+
+
+    </section>
   </main>
 </div>
+
+
+
diff --git a/docs/_sass/components/_challengePage.scss b/docs/_sass/components/_challengePage.scss
index 4c2df18e..837ac8d3 100644
--- a/docs/_sass/components/_challengePage.scss
+++ b/docs/_sass/components/_challengePage.scss
@@ -1543,6 +1543,23 @@ html {
   border-radius: 9px;
 }
 
+.bharat-2026-challenge .bharat-faq .ch-accordion summary {
+  display: flex;
+  flex-direction: row;
+  min-height: 0;
+  padding: 14px 48px 14px 16px;
+}
+
+.bharat-2026-challenge .bharat-faq .ch-acc-title {
+  display: block;
+  min-width: 0;
+  color: var(--text-dark);
+  font-size: 16px;
+  line-height: 1.4;
+  letter-spacing: normal;
+  text-transform: none;
+}
+
 .bharat-2026-challenge .ch-acc-title {
   display: contents;
 }