From 9c4ee28270b76a7624bf2aa4fb4a75e18db25637 Mon Sep 17 00:00:00 2001
From: wwelsh <hello@wwel.sh>
Date: Wed, 5 Nov 2025 12:35:09 -0500
Subject: [PATCH] initial commit

---
 .gitignore                 |   40 ++
 LICENSE                    |   21 +
 README.md                  |  203 ++++++
 SETUP_GUIDE.md             |  198 ++++++
 arxiv_digest_20251101.html |  985 +++++++++++++++++++++++++++
 config.json                |   84 +++
 desktop_demo.png           |  Bin 0 -> 129504 bytes
 generate_index.py          |  205 ++++++
 generate_tiktok_feed.py    |  512 ++++++++++++++
 main.py                    |  724 ++++++++++++++++++++
 mobile_demo.png            |  Bin 0 -> 227938 bytes
 requirements.txt           |    4 +
 reset_seen_papers.py       |   17 +
 run_digest.bat             |   50 ++
 tiktok_feed.html           | 1304 ++++++++++++++++++++++++++++++++++++
 15 files changed, 4347 insertions(+)
 create mode 100644 .gitignore
 create mode 100644 LICENSE
 create mode 100644 README.md
 create mode 100644 SETUP_GUIDE.md
 create mode 100644 arxiv_digest_20251101.html
 create mode 100644 config.json
 create mode 100644 desktop_demo.png
 create mode 100644 generate_index.py
 create mode 100644 generate_tiktok_feed.py
 create mode 100644 main.py
 create mode 100644 mobile_demo.png
 create mode 100644 requirements.txt
 create mode 100644 reset_seen_papers.py
 create mode 100644 run_digest.bat
 create mode 100644 tiktok_feed.html

diff --git a/.gitignore b/.gitignore
new file mode 100644
index 0000000..4136964
--- /dev/null
+++ b/.gitignore
@@ -0,0 +1,40 @@
+# Virtual Environment
+venv/
+env/
+ENV/
+.venv/
+
+# Python
+__pycache__/
+*.py[cod]
+*$py.class
+*.so
+.Python
+
+# ArXiv Downloads
+arxiv_archive/
+*.pdf
+
+# Generated Files
+seen_papers.json
+latest.html
+index.html
+
+# Syncthing
+.stfolder/
+.stignore
+
+# IDE / Editor
+.vscode/
+.idea/
+*.swp
+*.swo
+*~
+
+# OS
+.DS_Store
+Thumbs.db
+desktop.ini
+
+# Logs
+*.log
diff --git a/LICENSE b/LICENSE
new file mode 100644
index 0000000..b77bf2a
--- /dev/null
+++ b/LICENSE
@@ -0,0 +1,21 @@
+MIT License
+
+Copyright (c) 2025
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.
diff --git a/README.md b/README.md
new file mode 100644
index 0000000..1d66199
--- /dev/null
+++ b/README.md
@@ -0,0 +1,203 @@
+![Python](https://img.shields.io/badge/python-3.8+-blue.svg)
+![License](https://img.shields.io/badge/license-MIT-green.svg)
+![arXiv](https://img.shields.io/badge/arXiv-API-red.svg)
+![Platform](https://img.shields.io/badge/platform-windows%20%7C%20linux%20%7C%20macos-lightgrey.svg)
+
+# 📚 Research Digest
+
+**Automated daily research paper digest from arXiv with smart filtering, mobile-friendly interface, and AI-powered summaries.**
+
+Fetch, filter, and browse the latest research papers tailored to your interests. Desktop grid view for deep reading, mobile feed for quick scrolling.
+
+---
+
+## ✨ Features
+
+- **🎯 Smart Filtering** - Keyword-based relevance scoring across custom research interests
+- **📱 Mobile Feed** - Swipeable, full-screen card interface optimized for phones
+- **🖥️ Desktop Grid** - Multi-column layout with rich metadata and difficulty badges
+- **🧠 AI Summaries** - Auto-generated layman explanations using transformers
+- **🔄 Deduplication** - Never see the same paper twice with built-in tracking
+- **⚙️ Configurable** - JSON-based settings for interests, filters, and preferences
+- **📦 Archive** - Auto-saves daily digests with browsable index
+
+---
+
+## 🖼️ Screenshots
+
+### Desktop View
+![Desktop Demo](desktop_demo.png)
+
+### Mobile Feed
+![Mobile Demo](mobile_demo.png)
+
+---
+
+## 🚀 Quick Start
+
+### Windows
+
+1. **Clone & Run**
+   ```bash
+   git clone https://github.com/yourusername/research-digest.git
+   cd research-digest
+   run_digest.bat
+   ```
+
+2. **First run automatically:**
+   - Creates virtual environment
+   - Installs dependencies
+   - Fetches papers
+   - Generates HTML digests
+
+3. **Open in browser:**
+   - `latest.html` - Most recent digest
+   - `index.html` - Browse all archives
+   - `tiktok_feed.html` - Mobile-optimized feed
+
+### Linux/macOS
+
+```bash
+git clone https://github.com/yourusername/research-digest.git
+cd research-digest
+python -m venv venv
+source venv/bin/activate  # On Windows: venv\Scripts\activate
+pip install -r requirements.txt
+python main.py
+python generate_index.py
+```
+
+---
+
+## ⚙️ Configuration
+
+Edit `config.json` to customize:
+
+```json
+{
+  "interests": {
+    "Your Research Area": {
+      "query": "cat:cs.LG OR cat:cs.AI",
+      "keywords": ["keyword1", "keyword2", "keyword3"]
+    }
+  },
+  "settings": {
+    "papers_per_interest": 10,
+    "recent_days": 7,
+    "summary_max_length": 160
+  }
+}
+```
+
+### Available Settings
+
+| Setting | Default | Description |
+|---------|---------|-------------|
+| `papers_per_interest` | 10 | Papers to fetch per category |
+| `recent_days` | 7 | Look back window (0 = all time) |
+| `fallback_days` | 90 | Extended search if few results |
+| `summary_max_length` | 160 | Max characters for summaries |
+| `fetch_multiplier` | 5 | Over-fetch for better filtering |
+
+---
+
+## 📖 arXiv Query Syntax
+
+Use arXiv category codes in queries:
+
+- `cat:cs.LG` - Machine Learning
+- `cat:cs.CV` - Computer Vision
+- `cat:cs.CL` - Computation & Language (NLP)
+- `cat:cs.AI` - Artificial Intelligence
+- `cat:cs.CR` - Cryptography & Security
+- `cat:cs.DC` - Distributed Computing
+
+Combine with `OR`/`AND`: `cat:cs.LG OR cat:cs.AI`
+
+[Full category list](https://arxiv.org/category_taxonomy)
+
+---
+
+## 🔧 Advanced Usage
+
+### Automated Daily Digests
+
+**Windows Task Scheduler:**
+1. Open Task Scheduler
+2. Create Basic Task → Daily → 7:00 AM
+3. Action: Start Program → `C:\path\to\run_digest.bat`
+
+**Linux/macOS Cron:**
+```bash
+0 7 * * * cd /path/to/research-digest && ./venv/bin/python main.py && ./venv/bin/python generate_index.py
+```
+
+### Sync to Mobile (Syncthing)
+
+1. Install [Syncthing](https://syncthing.net/) on PC and phone
+2. Share project folder
+3. Access HTML files directly on phone
+
+### Reset Seen Papers
+
+```bash
+python reset_seen_papers.py
+```
+
+---
+
+## 📂 Project Structure
+
+```
+research-digest/
+├── config.json              # Configuration (edit this!)
+├── main.py                  # Core paper fetcher
+├── generate_index.py        # Archive browser generator
+├── generate_tiktok_feed.py  # Mobile feed generator
+├── run_digest.bat           # Windows launcher
+├── requirements.txt         # Python dependencies
+├── latest.html              # Latest digest (auto-generated)
+├── index.html               # Archive browser (auto-generated)
+├── tiktok_feed.html         # Mobile feed (auto-generated)
+├── seen_papers.json         # Deduplication tracker
+└── arxiv_archive/           # Daily archives
+    ├── arxiv_digest_20251101.html
+    └── ...
+```
+
+---
+
+## 🛠️ Requirements
+
+- **Python 3.8+**
+- **Dependencies:** `transformers`, `torch`, `requests`
+- **Disk Space:** ~2GB for model, ~10MB per digest
+- **Internet:** Required for arXiv API and first-time model download
+
+---
+
+## 📝 License
+
+MIT License - see [LICENSE](LICENSE) file for details
+
+---
+
+## 🤝 Contributing
+
+Contributions welcome! Ideas:
+- Additional paper sources (bioRxiv, SSRN, etc.)
+- Browser extension for direct syncing
+- Custom ML models for better summaries
+- Export to Notion/Obsidian/Roam
+
+---
+
+## 🙏 Acknowledgments
+
+- [arXiv](https://arxiv.org/) for the open research repository
+- [Hugging Face](https://huggingface.co/) for transformer models
+- Inspired by modern feed UIs and research workflows
+
+---
+
+**Built with ❤️ for researchers who want to stay current without drowning in papers**
diff --git a/SETUP_GUIDE.md b/SETUP_GUIDE.md
new file mode 100644
index 0000000..1aa9c8b
--- /dev/null
+++ b/SETUP_GUIDE.md
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+# 📱 Syncthing + Daily arXiv Digest Setup Guide
+
+## 🎯 What This Does
+- Automatically runs your arXiv digest **every morning at 7 AM**
+- Archives each day's report in `arxiv_archive/`
+- Creates `latest.html` for quick access
+- Generates `index.html` to browse all past reports
+- Syncs everything to your phone via Syncthing
+
+---
+
+## ⚙️ Step 1: Set Up Windows Task Scheduler
+
+### Option A: Quick Setup (Copy-Paste This)
+1. Press `Win + R`, type `taskschd.msc`, press Enter
+2. Click **"Create Basic Task"** in the right panel
+3. Fill in:
+   - **Name:** `arXiv Daily Digest`
+   - **Description:** `Fetches daily research papers and syncs to phone`
+4. **Trigger:** Select "Daily"
+   - Start date: Today
+   - Start time: **7:00 AM**
+   - Recur every: **1 days**
+5. **Action:** Select "Start a program"
+   - **Program/script:** `C:\Users\Admin\python\1aResearch\run_digest.bat`
+   - **Start in:** `C:\Users\Admin\python\1aResearch`
+6. Check **"Open the Properties dialog"** at the end
+7. In Properties:
+   - Go to **Conditions** tab
+   - ✅ Check "Start only if the following network connection is available" → Select "Any connection"
+   - ❌ Uncheck "Start the task only if the computer is on AC power"
+8. Click **OK**
+
+### Option B: Advanced Settings
+If you want to run it at startup instead:
+- Change Trigger to **"At log on"**
+- Add a 2-minute delay: In Properties → Triggers → Edit → Delay task for: **2 minutes**
+
+---
+
+## 📂 Step 2: Set Up Syncthing
+
+### On Your PC:
+1. Open Syncthing web UI (usually `http://localhost:8384`)
+2. Click **"Add Folder"**
+   - **Folder Path:** `C:\Users\Admin\python\1aResearch`
+   - **Folder Label:** `arXiv Research`
+   - **Folder ID:** `arxiv-research` (auto-generated)
+3. Go to **"Sharing"** tab
+4. Click **"Add Device"** and enter your phone's Device ID
+
+### On Your Phone:
+1. Install **Syncthing** from Play Store / App Store
+2. Open app → **Add Device** → Scan QR code from PC
+3. Accept the folder share request (`arXiv Research`)
+4. Set sync folder location (e.g., `/storage/emulated/0/arXiv/`)
+
+### What Gets Synced:
+```
+1aResearch/
+├── latest.html          ← Most recent digest (quick access)
+├── index.html           ← Browse all reports
+└── arxiv_archive/
+    ├── arxiv_digest_20251101.html
+    ├── arxiv_digest_20251102.html
+    └── ... (daily backups)
+```
+
+---
+
+## 📱 Step 3: View on Your Phone
+
+### Method 1: Direct File Access
+1. Open your phone's file manager
+2. Navigate to the Syncthing folder (e.g., `arXiv/`)
+3. Open `latest.html` with any browser
+4. Open `index.html` to browse past reports
+
+### Method 2: Use a Local HTML Viewer App
+Install **"HTML Viewer"** or **"WebView Tester"** from the app store:
+- Point it to your Syncthing folder
+- Bookmark `latest.html` for instant access
+
+### Method 3: Create a Home Screen Shortcut (Android)
+1. Open `latest.html` in Chrome
+2. Menu → **"Add to Home screen"**
+3. Name it "arXiv Digest"
+4. Now you have one-tap access!
+
+---
+
+## 🧪 Testing Your Setup
+
+### Test the Batch Script:
+```batch
+# Double-click run_digest.bat or run in Command Prompt:
+cd C:\Users\Admin\python\1aResearch
+run_digest.bat
+```
+
+Expected output:
+```
+Running arXiv digest...
+🔍 Fetching papers for: Efficient ML / Edge AI
+   → Found 5 papers
+...
+✨ HTML digest saved to arxiv_archive\arxiv_digest_20251101.html
+📄 Latest digest saved to latest.html
+
+Generating index page...
+📑 Index page generated with 1 reports
+Done! All files updated.
+```
+
+### Test Syncthing Sync:
+1. Create/edit any file in `C:\Users\Admin\python\1aResearch`
+2. Check your phone's Syncthing folder
+3. File should appear within seconds
+
+### Test Task Scheduler:
+1. Open Task Scheduler
+2. Find "arXiv Daily Digest"
+3. Right-click → **"Run"**
+4. Watch it execute
+
+---
+
+## 🎨 Customization Ideas
+
+### Change Run Time:
+Edit the Task Scheduler trigger to your preferred time (e.g., 6 AM, 9 PM)
+
+### Change Number of Papers:
+Edit `main.py` line 21:
+```python
+PAPERS_PER_INTEREST = 10  # Fetch 10 instead of 5
+```
+
+### Add More Interest Areas:
+Edit `main.py` lines 13-19 and add more queries:
+```python
+INTERESTS = {
+    "Your Topic": 'abs:"your keywords" OR ti:"your topic"',
+    # ... existing topics
+}
+```
+
+### Sync Only HTML Files (Save Space):
+In Syncthing → Folder → **Ignore Patterns**, add:
+```
+!/arxiv_archive/*.html
+!/latest.html
+!/index.html
+*
+```
+
+---
+
+## 🔧 Troubleshooting
+
+### Task Scheduler doesn't run:
+- Check Windows Event Viewer: `Win + X` → Event Viewer → Task Scheduler logs
+- Ensure "Run whether user is logged on or not" is selected
+- Make sure network connection is available
+
+### Syncthing not syncing:
+- Check both devices are connected to the same network (or internet)
+- Verify Device IDs match
+- Check folder status in Syncthing UI (should say "Up to Date")
+
+### Python script fails:
+- Test manually: `cd C:\Users\Admin\python\1aResearch && venv\Scripts\activate && python main.py`
+- Check arXiv API rate limits (3-second delays are built in)
+- Ensure internet connection is active
+
+### Old reports taking up space:
+Create a cleanup script to delete reports older than 30 days:
+```python
+# cleanup_old.py
+import os, glob, time
+for f in glob.glob("arxiv_archive/*.html"):
+    if os.path.getmtime(f) < time.time() - 30*86400:
+        os.remove(f)
+```
+
+---
+
+## 🎉 You're All Set!
+
+Every morning at 7 AM:
+1. ✅ Script fetches latest papers
+2. ✅ Generates beautiful HTML report
+3. ✅ Archives it with date
+4. ✅ Updates index page
+5. ✅ Syncs to your phone
+6. ✅ Read cutting-edge research over coffee!
+
+**Enjoy your automated research digest! 🚀**
diff --git a/arxiv_digest_20251101.html b/arxiv_digest_20251101.html
new file mode 100644
index 0000000..eca5b71
--- /dev/null
+++ b/arxiv_digest_20251101.html
@@ -0,0 +1,985 @@
+<!DOCTYPE html>
+<html lang="en">
+<head>
+  <meta charset="UTF-8" />
+  <meta name="viewport" content="width=device-width, initial-scale=1.0"/>
+  <title>arXiv Digest • 2025-11-01</title>
+  <style>
+    * { box-sizing: border-box; }
+
+    :root {
+      --bg: #0f0f0f;
+      --text: #e8e8e8;
+      --muted: #999;
+      --border: #2a2a2a;
+      --card-bg: #1a1a1a;
+      --link: #6ba3ff;
+      --accent: #ff6b6b;
+      --green: #51cf66;
+      --yellow: #ffd43b;
+      --red: #ff6b6b;
+      --layman-bg: #1f2937;
+      --layman-border: #60a5fa;
+    }
+
+    body {
+      font-family: 'Inter', -apple-system, BlinkMacSystemFont, 'Segoe UI', Roboto, sans-serif;
+      line-height: 1.5;
+      color: var(--text);
+      background: var(--bg);
+      margin: 0;
+      padding: 1rem;
+    }
+
+    .container {
+      max-width: 1600px;
+      margin: 0 auto;
+    }
+
+    header {
+      text-align: center;
+      padding: 2rem 1rem 3rem;
+      border-bottom: 2px solid var(--border);
+      margin-bottom: 2rem;
+    }
+
+    h1 {
+      font-weight: 900;
+      font-size: 2.5rem;
+      margin: 0;
+      background: linear-gradient(135deg, var(--accent), #ffa94d);
+      -webkit-background-clip: text;
+      -webkit-text-fill-color: transparent;
+      background-clip: text;
+    }
+
+    .meta {
+      color: var(--muted);
+      font-size: 0.95rem;
+      margin-top: 0.5rem;
+      letter-spacing: 0.5px;
+    }
+
+    .interest-section {
+      margin-bottom: 3rem;
+    }
+
+    .interest-header {
+      display: flex;
+      align-items: center;
+      gap: 0.8rem;
+      margin-bottom: 1.2rem;
+      padding: 0.8rem 1rem;
+      background: var(--card-bg);
+      border-radius: 12px;
+      border-left: 4px solid var(--accent);
+    }
+
+    .interest-title {
+      font-size: 1.3rem;
+      margin: 0;
+      font-weight: 700;
+      color: var(--text);
+    }
+
+    .papers-grid {
+      display: grid;
+      grid-template-columns: repeat(auto-fill, minmax(380px, 1fr));
+      gap: 1.2rem;
+    }
+
+    .paper {
+      background: var(--card-bg);
+      border: 1px solid var(--border);
+      border-radius: 12px;
+      padding: 1.2rem;
+      transition: all 0.2s ease;
+      position: relative;
+      display: flex;
+      flex-direction: column;
+      height: 100%;
+    }
+
+    .paper:hover {
+      border-color: var(--accent);
+      transform: translateY(-2px);
+      box-shadow: 0 8px 24px rgba(255, 107, 107, 0.15);
+    }
+
+    .paper-header {
+      display: flex;
+      justify-content: space-between;
+      align-items: flex-start;
+      gap: 0.8rem;
+      margin-bottom: 0.8rem;
+    }
+
+    .difficulty-badge {
+      padding: 0.3rem 0.7rem;
+      border-radius: 20px;
+      font-size: 0.7rem;
+      font-weight: 700;
+      white-space: nowrap;
+      flex-shrink: 0;
+    }
+
+    .paper h3 {
+      font-size: 1.05rem;
+      margin: 0 0 0.8rem 0;
+      font-weight: 700;
+      line-height: 1.4;
+      color: var(--text);
+    }
+
+    .layman-box {
+      background: var(--layman-bg);
+      border-left: 3px solid var(--layman-border);
+      padding: 0.7rem 0.9rem;
+      margin-bottom: 0.8rem;
+      border-radius: 6px;
+      font-size: 0.88rem;
+      line-height: 1.5;
+      color: #94a3b8;
+      font-style: italic;
+    }
+
+    .summary {
+      color: var(--muted);
+      margin-bottom: 1rem;
+      font-size: 0.88rem;
+      line-height: 1.6;
+      flex-grow: 1;
+    }
+
+    .paper-footer {
+      display: flex;
+      justify-content: space-between;
+      align-items: center;
+      padding-top: 0.8rem;
+      border-top: 1px solid var(--border);
+      margin-top: auto;
+    }
+
+    .category-tag {
+      background: #1e3a5f;
+      color: #60a5fa;
+      padding: 0.25rem 0.65rem;
+      border-radius: 15px;
+      font-size: 0.75rem;
+      font-weight: 600;
+    }
+
+    .date {
+      color: var(--muted);
+      font-size: 0.75rem;
+    }
+
+    .links {
+      display: flex;
+      gap: 1rem;
+      margin-top: 0.8rem;
+    }
+
+    .links a {
+      color: var(--link);
+      text-decoration: none;
+      font-size: 0.85rem;
+      font-weight: 600;
+      transition: color 0.2s;
+    }
+
+    .links a:hover {
+      color: var(--accent);
+    }
+
+    .footer {
+      text-align: center;
+      margin-top: 4rem;
+      padding: 2rem;
+      color: var(--muted);
+      font-size: 0.85rem;
+      border-top: 1px solid var(--border);
+    }
+
+    @media (max-width: 768px) {
+      .papers-grid {
+        grid-template-columns: 1fr;
+      }
+      h1 {
+        font-size: 2rem;
+      }
+    }
+  </style>
+</head>
+<body>
+  <div class="container">
+    <header>
+      <h1>arXiv Research Digest</h1>
+      <div class="meta">November 01, 2025 • 45 papers across 5 interests</div>
+    </header>
+<section class="interest-section">
+  <div class="interest-header">
+    <span>🔬</span>
+    <h2 class="interest-title">Efficient ML / Edge AI</h2>
+  </div>
+  <div class="papers-grid">
+    <article class="paper">
+      <div class="paper-header">
+        <span class="difficulty-badge">🟢 Applied</span>
+      </div>
+      <h3>Inference-Cost-Aware Dynamic Tree Construction for Efficient Inference in Large Language Models</h3>
+      <div class="layman-box">💡 This research tackles the problem of language AI.</div>
+      <div class="summary"> Large Language Models (LLMs) face significant inference latency challenges stemming from their autoregressive design and large size . To address this, speculative decoding emerges as a solution, enabling the simultaneous generation and validation of multiple tokens .</div>
+      <div class="paper-footer">
+        <span class="category-tag">cs.CL</span>
+        <span class="date">2025-10-30</span>
+      </div>
+      <div class="links">
+        <a href="http://arxiv.org/abs/2510.26577v1" target="_blank">Abstract ↗</a>
+        <a href="https://arxiv.org/pdf/2510.26577.pdf" target="_blank">PDF ↗</a>
+      </div>
+    </article>
+    <article class="paper">
+      <div class="paper-header">
+        <span class="difficulty-badge">🟢 Applied</span>
+      </div>
+      <h3>Distilling Multilingual Vision-Language Models: When Smaller Models Stay Multilingual</h3>
+      <div class="layman-box">💡 This research reduces language AI.</div>
+      <div class="summary"> Knowledge distillation (KD) demonstrates promising results in transferring knowledge from larger to smaller VLMs . applying KD in multilingualism is an underexplored area . We study five distillation formulations across CLIP and SigLIP2 .</div>
+      <div class="paper-footer">
+        <span class="category-tag">cs.CL</span>
+        <span class="date">2025-10-30</span>
+      </div>
+      <div class="links">
+        <a href="http://arxiv.org/abs/2510.26271v1" target="_blank">Abstract ↗</a>
+        <a href="https://arxiv.org/pdf/2510.26271.pdf" target="_blank">PDF ↗</a>
+      </div>
+    </article>
+    <article class="paper">
+      <div class="paper-header">
+        <span class="difficulty-badge">🟢 Applied</span>
+      </div>
+      <h3>STAR: A Privacy-Preserving, Energy-Efficient Edge AI Framework for Human Activity Recognition via Wi-Fi CSI in Mobile and Pervasive Computing Environments</h3>
+      <div class="layman-box">💡 This research presents techniques for privacy-preserving AI.</div>
+      <div class="summary"> Human Activity Recognition (HAR) via Wi-Fi Channel State Information (CSI) presents a privacy-preserving, contactless sensing approach suitable for smart homes, healthcare monitoring, and mobile IoT systems .</div>
+      <div class="paper-footer">
+        <span class="category-tag">cs.LG</span>
+        <span class="date">2025-10-30</span>
+      </div>
+      <div class="links">
+        <a href="http://arxiv.org/abs/2510.26148v1" target="_blank">Abstract ↗</a>
+        <a href="https://arxiv.org/pdf/2510.26148.pdf" target="_blank">PDF ↗</a>
+      </div>
+    </article>
+    <article class="paper">
+      <div class="paper-header">
+        <span class="difficulty-badge">🟢 Applied</span>
+      </div>
+      <h3>Do Students Debias Like Teachers? On the Distillability of Bias Mitigation Methods</h3>
+      <div class="layman-box">💡 This research running AI locally on devices for computer vision.</div>
+      <div class="summary"> Knowledge distillation (KD) is an effective method for model compression and transferring knowledge between models . However, its effect on model's robustness against spurious correlations that degrade performance on out-of-distribution data remains underexplored . This study investigates the effect of knowledge distillation on the transferability of ``debiasing'' capabilities from teacher models to student models .</div>
+      <div class="paper-footer">
+        <span class="category-tag">cs.LG</span>
+        <span class="date">2025-10-30</span>
+      </div>
+      <div class="links">
+        <a href="http://arxiv.org/abs/2510.26038v1" target="_blank">Abstract ↗</a>
+        <a href="https://arxiv.org/pdf/2510.26038.pdf" target="_blank">PDF ↗</a>
+      </div>
+    </article>
+    <article class="paper">
+      <div class="paper-header">
+        <span class="difficulty-badge">🟢 Applied</span>
+      </div>
+      <h3>An Agentic Framework for Rapid Deployment of Edge AI Solutions in Industry 5.0</h3>
+      <div class="layman-box">💡 This research reduces edge computing.</div>
+      <div class="summary"> We present a novel framework for Industry 5.0 that simplifies the deployment of AI models on edge devices in various industrial settings . The design reduces latency and avoids external data transfer by enabling local inference and real-time processing .</div>
+      <div class="paper-footer">
+        <span class="category-tag">cs.AI</span>
+        <span class="date">2025-10-29</span>
+      </div>
+      <div class="links">
+        <a href="http://arxiv.org/abs/2510.25813v1" target="_blank">Abstract ↗</a>
+        <a href="https://arxiv.org/pdf/2510.25813.pdf" target="_blank">PDF ↗</a>
+      </div>
+    </article>
+    <article class="paper">
+      <div class="paper-header">
+        <span class="difficulty-badge">🟢 Applied</span>
+      </div>
+      <h3>Energy-Efficient Autonomous Driving with Adaptive Perception and Robust Decision</h3>
+      <div class="layman-box">💡 This research explores techniques in machine learning.</div>
+      <div class="summary"> Autonomous driving is an emerging technology that is expected to bring significant social, economic, and environmental benefits . However, these benefits come with rising energy consumption by computation engines limiting the driving range of vehicles, especially electric ones . Perception computing is typically the most power-intensive component, as it relies on deep learning models to extract environmental features . To address these challenges, we propose an energy-efficient autonomous driving framework, called EneAD .</div>
+      <div class="paper-footer">
+        <span class="category-tag">cs.AI</span>
+        <span class="date">2025-10-29</span>
+      </div>
+      <div class="links">
+        <a href="http://arxiv.org/abs/2510.25205v1" target="_blank">Abstract ↗</a>
+        <a href="https://arxiv.org/pdf/2510.25205.pdf" target="_blank">PDF ↗</a>
+      </div>
+    </article>
+    <article class="paper">
+      <div class="paper-header">
+        <span class="difficulty-badge">🟢 Applied</span>
+      </div>
+      <h3>Resource-Efficient and Robust Inference of Deep and Bayesian Neural Networks on Embedded and Analog Computing Platforms</h3>
+      <div class="layman-box">💡 This research makes more efficient edge computing.</div>
+      <div class="summary"> While machine learning has transformed numerous application domains, its growing computational demands increasingly constrain scalability and efficiency . In practice, neural networks must not only operate efficiently but also provide reliable predictions under distributional shifts or unseen data . This work advances resource-efficient and robust inference for both conventional and Bayesian neural networks .</div>
+      <div class="paper-footer">
+        <span class="category-tag">cs.LG</span>
+        <span class="date">2025-10-28</span>
+      </div>
+      <div class="links">
+        <a href="http://arxiv.org/abs/2510.24951v1" target="_blank">Abstract ↗</a>
+        <a href="https://arxiv.org/pdf/2510.24951.pdf" target="_blank">PDF ↗</a>
+      </div>
+    </article>
+    <article class="paper">
+      <div class="paper-header">
+        <span class="difficulty-badge">🟢 Applied</span>
+      </div>
+      <h3>UHKD: A Unified Framework for Heterogeneous Knowledge Distillation via Frequency-Domain Representations</h3>
+      <div class="layman-box">💡 This research reduces computer vision.</div>
+      <div class="summary"> Knowledge distillation (KD) is an effective model compression technique that transfers knowledge from a high-performance teacher to a lightweight student, reducing cost while maintaining accuracy . In visual applications, where large-scale image models are widely used, KD enables efficient deployment .</div>
+      <div class="paper-footer">
+        <span class="category-tag">cs.CV</span>
+        <span class="date">2025-10-28</span>
+      </div>
+      <div class="links">
+        <a href="http://arxiv.org/abs/2510.24116v1" target="_blank">Abstract ↗</a>
+        <a href="https://arxiv.org/pdf/2510.24116.pdf" target="_blank">PDF ↗</a>
+      </div>
+    </article>
+    <article class="paper">
+      <div class="paper-header">
+        <span class="difficulty-badge">🟢 Applied</span>
+      </div>
+      <h3>A Survey on Efficient Vision-Language-Action Models</h3>
+      <div class="layman-box">💡 This research presents techniques for computer vision.</div>
+      <div class="summary"> Vision-Language-Action models (VLAs) represent a significant frontier in embodied intelligence, aiming to bridge digital knowledge with physical-world interaction . While these models have demonstrated remarkable generalist capabilities, deployment is severely hampered by the substantial computational and data requirements .</div>
+      <div class="paper-footer">
+        <span class="category-tag">cs.CV</span>
+        <span class="date">2025-10-27</span>
+      </div>
+      <div class="links">
+        <a href="http://arxiv.org/abs/2510.24795v1" target="_blank">Abstract ↗</a>
+        <a href="https://arxiv.org/pdf/2510.24795.pdf" target="_blank">PDF ↗</a>
+      </div>
+    </article>
+    <article class="paper">
+      <div class="paper-header">
+        <span class="difficulty-badge">🟢 Applied</span>
+      </div>
+      <h3>Rethinking Inference Placement for Deep Learning across Edge and Cloud Platforms: A Multi-Objective Optimization Perspective and Future Directions</h3>
+      <div class="layman-box">💡 This research running AI locally on devices for language AI.</div>
+      <div class="summary"> Edge intelligent applications like VR/AR and language model based chatbots have become widespread with the rapid expansion of IoT and mobile devices . But constrained edge devices often cannot serve the increasingly large and complex deep learning (DL) models . Research aims to balance accuracy, computation delay, transmission delay, and privacy concerns .</div>
+      <div class="paper-footer">
+        <span class="category-tag">cs.DC</span>
+        <span class="date">2025-10-27</span>
+      </div>
+      <div class="links">
+        <a href="http://arxiv.org/abs/2510.22909v1" target="_blank">Abstract ↗</a>
+        <a href="https://arxiv.org/pdf/2510.22909.pdf" target="_blank">PDF ↗</a>
+      </div>
+    </article>
+  </div>
+</section>
+<section class="interest-section">
+  <div class="interest-header">
+    <span>🔬</span>
+    <h2 class="interest-title">Privacy-Preserving ML</h2>
+  </div>
+  <div class="papers-grid">
+    <article class="paper">
+      <div class="paper-header">
+        <span class="difficulty-badge">🟢 Applied</span>
+      </div>
+      <h3>Non-Convex Over-the-Air Heterogeneous Federated Learning: A Bias-Variance Trade-off</h3>
+      <div class="layman-box">💡 This research distributed machine learning across computer vision.</div>
+      <div class="summary"> Over-the-air (OTA) federated learning (FL) has been well recognized as a scalable paradigm that exploits the waveform superposition of the wireless multiple-access channel . We develop novel OTA-FL SGD updates that allow a structured, time-invariant model bias while facilitating reduced variance updates .</div>
+      <div class="paper-footer">
+        <span class="category-tag">cs.LG</span>
+        <span class="date">2025-10-30</span>
+      </div>
+      <div class="links">
+        <a href="http://arxiv.org/abs/2510.26722v1" target="_blank">Abstract ↗</a>
+        <a href="https://arxiv.org/pdf/2510.26722.pdf" target="_blank">PDF ↗</a>
+      </div>
+    </article>
+    <article class="paper">
+      <div class="paper-header">
+        <span class="difficulty-badge">🟡 Advanced</span>
+      </div>
+      <h3>On Purely Private Covariance Estimation</h3>
+      <div class="layman-box">💡 This research presents techniques for privacy-preserving AI.</div>
+      <div class="summary"> We present a simple perturbation mechanism for the release of $d-dimensional covariance matrices under pure differential privacy . For large datasets with at least $n\geq d^2/\varepsilon elements, our mechanism recovers the provably optimal Frobenius norm error guarantees of \cite{nikolov2023private}.</div>
+      <div class="paper-footer">
+        <span class="category-tag">cs.LG</span>
+        <span class="date">2025-10-30</span>
+      </div>
+      <div class="links">
+        <a href="http://arxiv.org/abs/2510.26717v1" target="_blank">Abstract ↗</a>
+        <a href="https://arxiv.org/pdf/2510.26717.pdf" target="_blank">PDF ↗</a>
+      </div>
+    </article>
+    <article class="paper">
+      <div class="paper-header">
+        <span class="difficulty-badge">🟢 Applied</span>
+      </div>
+      <h3>Tight Differentially Private PCA via Matrix Coherence</h3>
+      <div class="layman-box">💡 This research makes more efficient privacy-preserving AI.</div>
+      <div class="summary"> We revisit the task of computing the span of the top $r$ singular vectors $u_1, \ldots, u_r$ of a matrix under differential privacy . We show that a simple and efficient algorithm -- based on singular value decomposition and standard perturbation mechanisms -- returns a private rank-$r$ approximation whose error depends only on the coherence of the input .</div>
+      <div class="paper-footer">
+        <span class="category-tag">cs.LG</span>
+        <span class="date">2025-10-30</span>
+      </div>
+      <div class="links">
+        <a href="http://arxiv.org/abs/2510.26679v1" target="_blank">Abstract ↗</a>
+        <a href="https://arxiv.org/pdf/2510.26679.pdf" target="_blank">PDF ↗</a>
+      </div>
+    </article>
+    <article class="paper">
+      <div class="paper-header">
+        <span class="difficulty-badge">🟢 Applied</span>
+      </div>
+      <h3>UnifiedFL: A Dynamic Unified Learning Framework for Equitable Federation</h3>
+      <div class="layman-box">💡 This research protecting data privacy in privacy-preserving AI.</div>
+      <div class="summary"> Federated learning (FL) has emerged as a key paradigm for collaborative model training across multiple clients without sharing raw data . We propose UnifiedFL, a dynamic federated learning framework that represents heterogeneous local networks as nodes and edges in a directed model graph optimized by a shared graph neural network .</div>
+      <div class="paper-footer">
+        <span class="category-tag">cs.LG</span>
+        <span class="date">2025-10-30</span>
+      </div>
+      <div class="links">
+        <a href="http://arxiv.org/abs/2510.26350v1" target="_blank">Abstract ↗</a>
+        <a href="https://arxiv.org/pdf/2510.26350.pdf" target="_blank">PDF ↗</a>
+      </div>
+    </article>
+    <article class="paper">
+      <div class="paper-header">
+        <span class="difficulty-badge">🟢 Applied</span>
+      </div>
+      <h3>PEEL: A Poisoning-Exposing Encoding Theoretical Framework for Local Differential Privacy</h3>
+      <div class="layman-box">💡 This research protecting data privacy in privacy-preserving AI.</div>
+      <div class="summary"> Local Differential Privacy (LDP) is a widely adopted privacy-protection model in the Internet of Things . However, existing defenses either incur prohibitive resource overheads or rely on domain-specific prior knowledge . We propose PEEL, a Poisoning-Exposing Encoding theoretical framework for LDP, which departs from resource- or prior-dependent countermeasures . PEEL amplifies stealthy poisoning effects by re-encoding LDP-perturbed data via sparsification, normalization, and low-rank projection .</div>
+      <div class="paper-footer">
+        <span class="category-tag">cs.CR</span>
+        <span class="date">2025-10-30</span>
+      </div>
+      <div class="links">
+        <a href="http://arxiv.org/abs/2510.26102v1" target="_blank">Abstract ↗</a>
+        <a href="https://arxiv.org/pdf/2510.26102.pdf" target="_blank">PDF ↗</a>
+      </div>
+    </article>
+  </div>
+</section>
+<section class="interest-section">
+  <div class="interest-header">
+    <span>🔬</span>
+    <h2 class="interest-title">Creative AI / Emotion</h2>
+  </div>
+  <div class="papers-grid">
+    <article class="paper">
+      <div class="paper-header">
+        <span class="difficulty-badge">🟢 Applied</span>
+      </div>
+      <h3>Contribution-Guided Asymmetric Learning for Robust Multimodal Fusion under Imbalance and Noise</h3>
+      <div class="layman-box">💡 This research achieves better emotion AI.</div>
+      <div class="summary"> Contribution-Guided Asymmetric Learning (CAL) aims to enhance the contribution of high-contribution modalities while compressing weak modalities to increase their contribution . CAL has shown outstanding performance in imbalanced fusion tasks and noise robustness tests . CAL is based on a modality contribution metric W^m combining the information quantity I(m) and confidence D(m).</div>
+      <div class="paper-footer">
+        <span class="category-tag">cs.MM</span>
+        <span class="date">2025-10-30</span>
+      </div>
+      <div class="links">
+        <a href="http://arxiv.org/abs/2510.26289v1" target="_blank">Abstract ↗</a>
+        <a href="https://arxiv.org/pdf/2510.26289.pdf" target="_blank">PDF ↗</a>
+      </div>
+    </article>
+    <article class="paper">
+      <div class="paper-header">
+        <span class="difficulty-badge">🟢 Applied</span>
+      </div>
+      <h3>Lost in Phonation: Voice Quality Variation as an Evaluation Dimension for Speech Foundation Models</h3>
+      <div class="layman-box">💡 This research presents techniques for speech processing.</div>
+      <div class="summary"> Recent advances in speech foundation models (SFMs) have enabled the direct processing of spoken language from raw audio . This capability allows SFMs to be exposed to rich paralinguistic variations embedded in the input speech signal . One under-explored dimension of this variation is voice quality, encompassing phonation types such as creaky and breathy voice .</div>
+      <div class="paper-footer">
+        <span class="category-tag">eess.AS</span>
+        <span class="date">2025-10-29</span>
+      </div>
+      <div class="links">
+        <a href="http://arxiv.org/abs/2510.25577v1" target="_blank">Abstract ↗</a>
+        <a href="https://arxiv.org/pdf/2510.25577.pdf" target="_blank">PDF ↗</a>
+      </div>
+    </article>
+    <article class="paper">
+      <div class="paper-header">
+        <span class="difficulty-badge">🟢 Applied</span>
+      </div>
+      <h3>Evaluating Emotion Recognition in Spoken Language Models on Emotionally Incongruent Speech</h3>
+      <div class="layman-box">💡 This research achieves better language AI.</div>
+      <div class="summary"> Advances in spoken language processing have driven the development of spoken language models . We evaluate four SLMs on the task of speech emotion recognition using a dataset of emotionally incongruent speech samples . Results indicate that SLMs rely predominantly on textual semantics rather than speech emotion to perform the task .</div>
+      <div class="paper-footer">
+        <span class="category-tag">cs.CL</span>
+        <span class="date">2025-10-29</span>
+      </div>
+      <div class="links">
+        <a href="http://arxiv.org/abs/2510.25054v2" target="_blank">Abstract ↗</a>
+        <a href="https://arxiv.org/pdf/2510.25054.pdf" target="_blank">PDF ↗</a>
+      </div>
+    </article>
+    <article class="paper">
+      <div class="paper-header">
+        <span class="difficulty-badge">🟢 Applied</span>
+      </div>
+      <h3>MCIHN: A Hybrid Network Model Based on Multi-path Cross-modal Interaction for Multimodal Emotion Recognition</h3>
+      <div class="layman-box">💡 This research understanding emotions in emotion AI.</div>
+      <div class="summary"> Multimodal emotion recognition is crucial for future human-computer interaction . However accurate emotion recognition still faces significant challenges due to differences between different modalities and the difficulty of characterizing unimodal emotional information . A hybrid network model based on multipath cross-modal interaction (MCIHN) is proposed .</div>
+      <div class="paper-footer">
+        <span class="category-tag">cs.CV</span>
+        <span class="date">2025-10-28</span>
+      </div>
+      <div class="links">
+        <a href="http://arxiv.org/abs/2510.24827v1" target="_blank">Abstract ↗</a>
+        <a href="https://arxiv.org/pdf/2510.24827.pdf" target="_blank">PDF ↗</a>
+      </div>
+    </article>
+    <article class="paper">
+      <div class="paper-header">
+        <span class="difficulty-badge">🟢 Applied</span>
+      </div>
+      <h3>Emotion-Coherent Reasoning for Multimodal LLMs via Emotional Rationale Verifier</h3>
+      <div class="layman-box">💡 This research understanding emotions in language AI.</div>
+      <div class="summary"> Emotional Rationale Verifier (ERV) and an Explanation Reward are novel approaches to predicting emotions . Authors propose a novel approach: the ERV and an explanation reward . Their method significantly improves explanation-prediction consistency and explanation emotion accuracy .</div>
+      <div class="paper-footer">
+        <span class="category-tag">cs.AI</span>
+        <span class="date">2025-10-27</span>
+      </div>
+      <div class="links">
+        <a href="http://arxiv.org/abs/2510.23506v1" target="_blank">Abstract ↗</a>
+        <a href="https://arxiv.org/pdf/2510.23506.pdf" target="_blank">PDF ↗</a>
+      </div>
+    </article>
+    <article class="paper">
+      <div class="paper-header">
+        <span class="difficulty-badge">🟢 Applied</span>
+      </div>
+      <h3>Emotion Recognition with Minimal Wearable Sensing: Multi-domain Feature, Hybrid Feature Selection, and Personalized vs. Generalized Ensemble Model Analysis</h3>
+      <div class="layman-box">💡 This research proposes a method for edge computing.</div>
+      <div class="summary"> Negative emotions are linked to the onset of neurodegenerative diseases and dementia . Physiological signals from wearable devices offer a promising noninvasive method for continuous emotion monitoring . The method is designed for deployment in resource-constrained systems, such as Internet of Things .</div>
+      <div class="paper-footer">
+        <span class="category-tag">cs.HC</span>
+        <span class="date">2025-10-26</span>
+      </div>
+      <div class="links">
+        <a href="http://arxiv.org/abs/2510.22498v1" target="_blank">Abstract ↗</a>
+        <a href="https://arxiv.org/pdf/2510.22498.pdf" target="_blank">PDF ↗</a>
+      </div>
+    </article>
+    <article class="paper">
+      <div class="paper-header">
+        <span class="difficulty-badge">🟢 Applied</span>
+      </div>
+      <h3>LUNA: Efficient and Topology-Agnostic Foundation Model for EEG Signal Analysis</h3>
+      <div class="layman-box">💡 This research explores techniques in emotion AI.</div>
+      <div class="summary"> LUNA (Latent Unified Network Architecture) is a self-supervised foundation model that reconciles disparate electrode geometries while scaling linearly -- not quadratically -- with channel count . LUNA compresses multi-channel EEG into a fixed-size, topology-agnostic latent space via learned queries and cross-attention . It demonstrates highly competitive performance across several benchmarks .</div>
+      <div class="paper-footer">
+        <span class="category-tag">cs.LG</span>
+        <span class="date">2025-10-25</span>
+      </div>
+      <div class="links">
+        <a href="http://arxiv.org/abs/2510.22257v1" target="_blank">Abstract ↗</a>
+        <a href="https://arxiv.org/pdf/2510.22257.pdf" target="_blank">PDF ↗</a>
+      </div>
+    </article>
+    <article class="paper">
+      <div class="paper-header">
+        <span class="difficulty-badge">🟢 Applied</span>
+      </div>
+      <h3>Multi-dataset Joint Pre-training of Emotional EEG Enables Generalizable Affective Computing</h3>
+      <div class="layman-box">💡 This research presents techniques for emotion AI.</div>
+      <div class="summary"> The method outperforms state-of-the-art large-scale EEG models by an average of 4.57% in AUROC for few-shot emotion recognition and 11.92% in accuracy for zero-shot generalization to a new dataset .</div>
+      <div class="paper-footer">
+        <span class="category-tag">cs.LG</span>
+        <span class="date">2025-10-25</span>
+      </div>
+      <div class="links">
+        <a href="http://arxiv.org/abs/2510.22197v1" target="_blank">Abstract ↗</a>
+        <a href="https://arxiv.org/pdf/2510.22197.pdf" target="_blank">PDF ↗</a>
+      </div>
+    </article>
+    <article class="paper">
+      <div class="paper-header">
+        <span class="difficulty-badge">🟢 Applied</span>
+      </div>
+      <h3>SentiMaithili: A Benchmark Dataset for Sentiment and Reason Generation for the Low-Resource Maithili Language</h3>
+      <div class="layman-box">💡 This research presents techniques for language AI.</div>
+      <div class="summary"> Maithili is an Indo-Aryan language spoken by more than 13 million people in the Purvanchal region of India . It is valued for its rich linguistic structure and cultural significance .</div>
+      <div class="paper-footer">
+        <span class="category-tag">cs.CL</span>
+        <span class="date">2025-10-25</span>
+      </div>
+      <div class="links">
+        <a href="http://arxiv.org/abs/2510.22160v1" target="_blank">Abstract ↗</a>
+        <a href="https://arxiv.org/pdf/2510.22160.pdf" target="_blank">PDF ↗</a>
+      </div>
+    </article>
+    <article class="paper">
+      <div class="paper-header">
+        <span class="difficulty-badge">🟢 Applied</span>
+      </div>
+      <h3>REVE: A Foundation Model for EEG -- Adapting to Any Setup with Large-Scale Pretraining on 25,000 Subjects</h3>
+      <div class="layman-box">💡 This research reduces computer vision.</div>
+      <div class="summary"> Foundation models have transformed AI by reducing reliance on task-specific data through large-scale pretraining . While successful in language and vision, their adoption in EEG has lagged due to the heterogeneity of public datasets . Existing EEG foundation models struggle to generalize across these variations, often restricting pretraining to a single setup . We present REVE (Representation for EEG with Versatile Embeddings) a pretrained model .</div>
+      <div class="paper-footer">
+        <span class="category-tag">cs.LG</span>
+        <span class="date">2025-10-24</span>
+      </div>
+      <div class="links">
+        <a href="http://arxiv.org/abs/2510.21585v1" target="_blank">Abstract ↗</a>
+        <a href="https://arxiv.org/pdf/2510.21585.pdf" target="_blank">PDF ↗</a>
+      </div>
+    </article>
+  </div>
+</section>
+<section class="interest-section">
+  <div class="interest-header">
+    <span>🔬</span>
+    <h2 class="interest-title">Lightweight Systems</h2>
+  </div>
+  <div class="papers-grid">
+    <article class="paper">
+      <div class="paper-header">
+        <span class="difficulty-badge">🟢 Applied</span>
+      </div>
+      <h3>Vectorized Context-Aware Embeddings for GAT-Based Collaborative Filtering</h3>
+      <div class="layman-box">💡 This research enhances language AI.</div>
+      <div class="summary"> Recommender systems often struggle with data sparsity and cold-start scenarios . This paper presents a Graph Attention Network (GAT) based Collaborative Filtering (CF) framework enhanced with context aware embeddings .</div>
+      <div class="paper-footer">
+        <span class="category-tag">cs.IR</span>
+        <span class="date">2025-10-30</span>
+      </div>
+      <div class="links">
+        <a href="http://arxiv.org/abs/2510.26461v1" target="_blank">Abstract ↗</a>
+        <a href="https://arxiv.org/pdf/2510.26461.pdf" target="_blank">PDF ↗</a>
+      </div>
+    </article>
+    <article class="paper">
+      <div class="paper-header">
+        <span class="difficulty-badge">🟡 Advanced</span>
+      </div>
+      <h3>On neighborhoods of embedded toroidal and Hopf manifolds and their foliations</h3>
+      <div class="layman-box">💡 This research running AI on low-power devices for edge computing.</div>
+      <div class="summary"> In this article, we give completely new examples of embedded complex manifolds the germ of neighborhood of which is holomorphically equivalent to the zero section in its normal bundle . The first set of examples is composed of connected abelian complex Lie groups, embedded in some complex manifold $M$. The second set is $n$-dimensional Hopf manifolds, embedded as hypersurfaces .</div>
+      <div class="paper-footer">
+        <span class="category-tag">math.CV</span>
+        <span class="date">2025-10-30</span>
+      </div>
+      <div class="links">
+        <a href="http://arxiv.org/abs/2510.26454v1" target="_blank">Abstract ↗</a>
+        <a href="https://arxiv.org/pdf/2510.26454.pdf" target="_blank">PDF ↗</a>
+      </div>
+    </article>
+    <article class="paper">
+      <div class="paper-header">
+        <span class="difficulty-badge">🟢 Applied</span>
+      </div>
+      <h3>Scales++: Compute Efficient Evaluation Subset Selection with Cognitive Scales Embeddings</h3>
+      <div class="layman-box">💡 This research makes more efficient language AI.</div>
+      <div class="summary"> The prohibitive cost of evaluating large language models (LLMs) on comprehensive benchmarks necessitates the creation of small yet representative data subsets that enable efficient assessment while retaining predictive fidelity . Current methods for this task operate under a model-centric paradigm, selecting benchmarking items based on the collective performance of existing models . Such approaches are limited by large upfront costs, an inability to immediately handle new benchmarks (`cold-start'), and the fragile assumption that future models will share the failure patterns of their predecessors .</div>
+      <div class="paper-footer">
+        <span class="category-tag">cs.AI</span>
+        <span class="date">2025-10-30</span>
+      </div>
+      <div class="links">
+        <a href="http://arxiv.org/abs/2510.26384v1" target="_blank">Abstract ↗</a>
+        <a href="https://arxiv.org/pdf/2510.26384.pdf" target="_blank">PDF ↗</a>
+      </div>
+    </article>
+    <article class="paper">
+      <div class="paper-header">
+        <span class="difficulty-badge">🟡 Advanced</span>
+      </div>
+      <h3>From Embedding to Control: Representations for Stochastic Multi-Object Systems</h3>
+      <div class="layman-box">💡 This research achieves better machine learning.</div>
+      <div class="summary"> This paper studies how to achieve accurate modeling and effective control in stochastic nonlinear dynamics with multiple interacting objects . Non-uniform interactions and random topologies make this task challenging .</div>
+      <div class="paper-footer">
+        <span class="category-tag">eess.SY</span>
+        <span class="date">2025-10-30</span>
+      </div>
+      <div class="links">
+        <a href="http://arxiv.org/abs/2510.26344v1" target="_blank">Abstract ↗</a>
+        <a href="https://arxiv.org/pdf/2510.26344.pdf" target="_blank">PDF ↗</a>
+      </div>
+    </article>
+    <article class="paper">
+      <div class="paper-header">
+        <span class="difficulty-badge">🔴 Theory-Heavy</span>
+      </div>
+      <h3>Sharp embeddings and existence results for Logarithmic $p$-Laplacian equations with critical growth</h3>
+      <div class="layman-box">💡 This research explores techniques in machine learning.</div>
+      <div class="summary"> In this paper, we derive a new $p$-Logarithmic Sobolev inequality and optimal continuous and compact embeddings into Orlicz-type spaces of the function space associated with the logarathmic $p$.-Laplacian . By employing the method of the Nehari manifold, we prove the existence of a nontrivial weak solution . We conduct an asymptotic analysis of a weighted nonlocal, nonlinear problem governed by the fractional</div>
+      <div class="paper-footer">
+        <span class="category-tag">math.AP</span>
+        <span class="date">2025-10-30</span>
+      </div>
+      <div class="links">
+        <a href="http://arxiv.org/abs/2510.26286v1" target="_blank">Abstract ↗</a>
+        <a href="https://arxiv.org/pdf/2510.26286.pdf" target="_blank">PDF ↗</a>
+      </div>
+    </article>
+    <article class="paper">
+      <div class="paper-header">
+        <span class="difficulty-badge">🟢 Applied</span>
+      </div>
+      <h3>Accretion rates of stellar-mass compact objects embedded in AGN discs</h3>
+      <div class="layman-box">💡 This research running AI on low-power devices for edge computing.</div>
+      <div class="summary"> Stellar-mass compact objects (COs) embedded in active galactic nucleus (AGN) discs are commonly assumed to accrete via Bondi or Bondi-Hoyle-Lyttleton prescriptions . We show that differential rotation in AGN discs can impart non-negligible angular momentum, in which case accretion proceeds through a viscous disc rather than Bondi/BHL flow .</div>
+      <div class="paper-footer">
+        <span class="category-tag">astro-ph.HE</span>
+        <span class="date">2025-10-30</span>
+      </div>
+      <div class="links">
+        <a href="http://arxiv.org/abs/2510.26111v1" target="_blank">Abstract ↗</a>
+        <a href="https://arxiv.org/pdf/2510.26111.pdf" target="_blank">PDF ↗</a>
+      </div>
+    </article>
+    <article class="paper">
+      <div class="paper-header">
+        <span class="difficulty-badge">🟡 Advanced</span>
+      </div>
+      <h3>An explicit formula of the limit of the heat kernel measures on the spheres embedded in $\R^\infty$</h3>
+      <div class="layman-box">💡 This research explores techniques in machine learning.</div>
+      <div class="summary"> We show that the heat kernel measures based at the north pole of the spheres converges to a Gaussian measure in $R^\infty$ We also find an explicit formula for this measure .</div>
+      <div class="paper-footer">
+        <span class="category-tag">math.PR</span>
+        <span class="date">2025-10-29</span>
+      </div>
+      <div class="links">
+        <a href="http://arxiv.org/abs/2510.25855v1" target="_blank">Abstract ↗</a>
+        <a href="https://arxiv.org/pdf/2510.25855.pdf" target="_blank">PDF ↗</a>
+      </div>
+    </article>
+    <article class="paper">
+      <div class="paper-header">
+        <span class="difficulty-badge">🟡 Advanced</span>
+      </div>
+      <h3>Tight Spherical Embeddings (Updated Version)</h3>
+      <div class="layman-box">💡 This research explores techniques in machine learning.</div>
+      <div class="summary"> This is an updated version of a paper which appeared in the proceedings of the 1979 Berlin Colloquium on Global Differential Geometry . The main result of this paper is that every compact isoparametric hypersurface $M^n \subset S^{n+1} is tight .</div>
+      <div class="paper-footer">
+        <span class="category-tag">math.DG</span>
+        <span class="date">2025-10-29</span>
+      </div>
+      <div class="links">
+        <a href="http://arxiv.org/abs/2510.25611v1" target="_blank">Abstract ↗</a>
+        <a href="https://arxiv.org/pdf/2510.25611.pdf" target="_blank">PDF ↗</a>
+      </div>
+    </article>
+    <article class="paper">
+      <div class="paper-header">
+        <span class="difficulty-badge">🟢 Applied</span>
+      </div>
+      <h3>Enhanced quality factors at resonance in acoustofluidic cavities embedded in matched elastic metamaterials</h3>
+      <div class="layman-box">💡 This research enhances machine learning.</div>
+      <div class="summary"> We show that by embedding liquid-filled acoustofluidic cavities in a metamaterial, the quality factor of the cavity at selected acoustic resonance modes can be enhanced by 2 to 3 orders of magnitude .</div>
+      <div class="paper-footer">
+        <span class="category-tag">physics.flu-dyn</span>
+        <span class="date">2025-10-29</span>
+      </div>
+      <div class="links">
+        <a href="http://arxiv.org/abs/2510.25527v1" target="_blank">Abstract ↗</a>
+        <a href="https://arxiv.org/pdf/2510.25527.pdf" target="_blank">PDF ↗</a>
+      </div>
+    </article>
+    <article class="paper">
+      <div class="paper-header">
+        <span class="difficulty-badge">🟢 Applied</span>
+      </div>
+      <h3>Hierarchical Physics-Embedded Learning for Spatiotemporal Dynamical Systems</h3>
+      <div class="layman-box">💡 This research explores techniques in edge computing.</div>
+      <div class="summary"> Modeling complex spatiotemporal dynamics, particularly in far-from-equilibrium systems, remains a challenge in science . The governing partial differential equations (PDEs) for these systems are often intractable to derive from first principles .</div>
+      <div class="paper-footer">
+        <span class="category-tag">cs.LG</span>
+        <span class="date">2025-10-29</span>
+      </div>
+      <div class="links">
+        <a href="http://arxiv.org/abs/2510.25306v1" target="_blank">Abstract ↗</a>
+        <a href="https://arxiv.org/pdf/2510.25306.pdf" target="_blank">PDF ↗</a>
+      </div>
+    </article>
+  </div>
+</section>
+<section class="interest-section">
+  <div class="interest-header">
+    <span>🔬</span>
+    <h2 class="interest-title">Offline-First / Local AI</h2>
+  </div>
+  <div class="papers-grid">
+    <article class="paper">
+      <div class="paper-header">
+        <span class="difficulty-badge">🟢 Applied</span>
+      </div>
+      <h3>SBASH: a Framework for Designing and Evaluating RAG vs. Prompt-Tuned LLM Honeypots</h3>
+      <div class="layman-box">💡 This research explores techniques in language AI.</div>
+      <div class="summary"> Honeypots are decoy systems used for gathering valuable threat intelligence . Maximising attacker engagement is essential to their utility . Research has highlighted that context-awareness is necessary to increase engagement . Large Language Models (LLMs) have been shown as one approach to increase context awareness .</div>
+      <div class="paper-footer">
+        <span class="category-tag">cs.CR</span>
+        <span class="date">2025-10-24</span>
+      </div>
+      <div class="links">
+        <a href="http://arxiv.org/abs/2510.21459v1" target="_blank">Abstract ↗</a>
+        <a href="https://arxiv.org/pdf/2510.21459.pdf" target="_blank">PDF ↗</a>
+      </div>
+    </article>
+    <article class="paper">
+      <div class="paper-header">
+        <span class="difficulty-badge">🟢 Applied</span>
+      </div>
+      <h3>REx86: A Local Large Language Model for Assisting in x86 Assembly Reverse Engineering</h3>
+      <div class="layman-box">💡 This research improves language AI.</div>
+      <div class="summary"> Large Language Models offer potential for improving RE efficiency through automated comprehension and commenting . Cloud-hosted, closed-weight models pose privacy and security risks and cannot be used in closed-network facilities . REx86 reduces test-set cross-entropy loss by 64.2% and improves semantic cosine similarity against ground truth by 20.3\% over its base model .</div>
+      <div class="paper-footer">
+        <span class="category-tag">cs.CR</span>
+        <span class="date">2025-10-23</span>
+      </div>
+      <div class="links">
+        <a href="http://arxiv.org/abs/2510.20975v1" target="_blank">Abstract ↗</a>
+        <a href="https://arxiv.org/pdf/2510.20975.pdf" target="_blank">PDF ↗</a>
+      </div>
+    </article>
+    <article class="paper">
+      <div class="paper-header">
+        <span class="difficulty-badge">🟢 Applied</span>
+      </div>
+      <h3>CORE: Reducing UI Exposure in Mobile Agents via Collaboration Between Cloud and Local LLMs</h3>
+      <div class="layman-box">💡 This research achieves better language AI.</div>
+      <div class="summary"> Mobile agents rely on Large Language Models (LLMs) to plan and execute tasks on smartphone user interfaces . While cloud-based LLMs achieve high task accuracy, they require uploading the full UI state at every step . In contrast, local LLMs avoid UI uploads but suffer from limited capacity, resulting in lower task success rates . CORE comprises three key components: (1) layout-aware block partitioning, (2) Co-planning) and Co-decision-making .</div>
+      <div class="paper-footer">
+        <span class="category-tag">cs.CL</span>
+        <span class="date">2025-10-17</span>
+      </div>
+      <div class="links">
+        <a href="http://arxiv.org/abs/2510.15455v1" target="_blank">Abstract ↗</a>
+        <a href="https://arxiv.org/pdf/2510.15455.pdf" target="_blank">PDF ↗</a>
+      </div>
+    </article>
+    <article class="paper">
+      <div class="paper-header">
+        <span class="difficulty-badge">🟢 Applied</span>
+      </div>
+      <h3>LLM-guided Hierarchical Retrieval</h3>
+      <div class="layman-box">💡 This research explores techniques in language AI.</div>
+      <div class="summary"> Modern IR systems are increasingly tasked with answering complex, multi-faceted queries that require deep reasoning . We introduce LATTICE, a hierarchical retrieval framework that enables an LLM to reason over and navigate large corpora with logarithmic search complexity .</div>
+      <div class="paper-footer">
+        <span class="category-tag">cs.IR</span>
+        <span class="date">2025-10-15</span>
+      </div>
+      <div class="links">
+        <a href="http://arxiv.org/abs/2510.13217v1" target="_blank">Abstract ↗</a>
+        <a href="https://arxiv.org/pdf/2510.13217.pdf" target="_blank">PDF ↗</a>
+      </div>
+    </article>
+    <article class="paper">
+      <div class="paper-header">
+        <span class="difficulty-badge">🟢 Applied</span>
+      </div>
+      <h3>COSTAR-A: A prompting framework for enhancing Large Language Model performance on Point-of-View questions</h3>
+      <div class="layman-box">💡 This research enhances language AI.</div>
+      <div class="summary"> COSTAR-A is a novel prompt engineering framework that enhances the existing COSTAR method . COSTAR stands for Context, Objective, Style, Tone, Audience, and Response, by adding the 'Answer' component at the end .</div>
+      <div class="paper-footer">
+        <span class="category-tag">cs.CL</span>
+        <span class="date">2025-10-14</span>
+      </div>
+      <div class="links">
+        <a href="http://arxiv.org/abs/2510.12637v1" target="_blank">Abstract ↗</a>
+        <a href="https://arxiv.org/pdf/2510.12637.pdf" target="_blank">PDF ↗</a>
+      </div>
+    </article>
+    <article class="paper">
+      <div class="paper-header">
+        <span class="difficulty-badge">🟢 Applied</span>
+      </div>
+      <h3>Bridging Semantics & Structure for Software Vulnerability Detection using Hybrid Network Models</h3>
+      <div class="layman-box">💡 This research explores techniques in language AI.</div>
+      <div class="summary"> Software vulnerabilities remain a persistent risk, yet static and dynamic analyses often overlook structural dependencies that shape insecure behaviors . Viewing programs as heterogeneous graphs, we capture control- and data-flow relations as complex interaction networks . Our hybrid framework combines these graph representations with light-weight (<4B) local LLMs .</div>
+      <div class="paper-footer">
+        <span class="category-tag">cs.SE</span>
+        <span class="date">2025-10-11</span>
+      </div>
+      <div class="links">
+        <a href="http://arxiv.org/abs/2510.10321v1" target="_blank">Abstract ↗</a>
+        <a href="https://arxiv.org/pdf/2510.10321.pdf" target="_blank">PDF ↗</a>
+      </div>
+    </article>
+    <article class="paper">
+      <div class="paper-header">
+        <span class="difficulty-badge">🟢 Applied</span>
+      </div>
+      <h3>Open WebUI: An Open, Extensible, and Usable Interface for AI Interaction</h3>
+      <div class="layman-box">💡 This research presents techniques for language AI.</div>
+      <div class="summary"> The toolkit is designed to be open (open-source and local), extensible ( plugin support and users can interact with multiple models) The extensibility is enabled through a two-pronged plugin architecture and a community platform for sharing, importing, and adapting extensions .</div>
+      <div class="paper-footer">
+        <span class="category-tag">cs.HC</span>
+        <span class="date">2025-10-02</span>
+      </div>
+      <div class="links">
+        <a href="http://arxiv.org/abs/2510.02546v1" target="_blank">Abstract ↗</a>
+        <a href="https://arxiv.org/pdf/2510.02546.pdf" target="_blank">PDF ↗</a>
+      </div>
+    </article>
+    <article class="paper">
+      <div class="paper-header">
+        <span class="difficulty-badge">🟢 Applied</span>
+      </div>
+      <h3>DualTune: Decoupled Fine-Tuning for On-Device Agentic Systems</h3>
+      <div class="layman-box">💡 This research protecting data privacy in language AI.</div>
+      <div class="summary"> Large Language Models (LLMs) consistently underperform compared to frontier models in tool calling scenarios . We propose "decoupled fine-tuning" to create dedicated LoRA adapters for tool selection and tool-specific argument generation using separate loss masking for each of the subtasks . DualTune is an inference framework that leverages the LRA adapters created using decoupled fines-tune to perform efficient agent orchestration with the help of local models .</div>
+      <div class="paper-footer">
+        <span class="category-tag">cs.AI</span>
+        <span class="date">2025-09-30</span>
+      </div>
+      <div class="links">
+        <a href="http://arxiv.org/abs/2510.00229v2" target="_blank">Abstract ↗</a>
+        <a href="https://arxiv.org/pdf/2510.00229.pdf" target="_blank">PDF ↗</a>
+      </div>
+    </article>
+    <article class="paper">
+      <div class="paper-header">
+        <span class="difficulty-badge">🟢 Applied</span>
+      </div>
+      <h3>SecureFixAgent: A Hybrid LLM Agent for Automated Python Static Vulnerability Repair</h3>
+      <div class="layman-box">💡 This research automatically finding language AI.</div>
+      <div class="summary"> Static analysis tools like Bandit are effective at vulnerability detection but suffer from high false positives and lack repair capabilities . Large Language Models (LLMs) can suggest fixes but often hallucinate changes and lack self-validation . We present SecureFixAgent, a hybrid repair framework integrating Bandit with lightweight local LLMs in an iterative detect-repair-validate loop .</div>
+      <div class="paper-footer">
+        <span class="category-tag">cs.CR</span>
+        <span class="date">2025-09-18</span>
+      </div>
+      <div class="links">
+        <a href="http://arxiv.org/abs/2509.16275v1" target="_blank">Abstract ↗</a>
+        <a href="https://arxiv.org/pdf/2509.16275.pdf" target="_blank">PDF ↗</a>
+      </div>
+    </article>
+    <article class="paper">
+      <div class="paper-header">
+        <span class="difficulty-badge">🟢 Applied</span>
+      </div>
+      <h3>PrivWeb: Unobtrusive and Content-aware Privacy Protection For Web Agents</h3>
+      <div class="layman-box">💡 This research protecting data privacy in language AI.</div>
+      <div class="summary"> PrivWeb is a trusted add-on on web agents that anonymizes private information on interfaces according to user preferences . It features privacy categorization and adaptive notifications that selectively pauses tasks for user control over information collection for highly sensitive information . PrivWeb reduces perceived privacy risks with no associated increase in cognitive effort, and resulted in higher overall satisfaction .</div>
+      <div class="paper-footer">
+        <span class="category-tag">cs.HC</span>
+        <span class="date">2025-09-15</span>
+      </div>
+      <div class="links">
+        <a href="http://arxiv.org/abs/2509.11939v1" target="_blank">Abstract ↗</a>
+        <a href="https://arxiv.org/pdf/2509.11939.pdf" target="_blank">PDF ↗</a>
+      </div>
+    </article>
+  </div>
+</section>
+    <div class="footer">
+      ✨ Generated automatically • Powered by arXiv API
+    </div>
+  </div>
+</body>
+</html>
diff --git a/config.json b/config.json
new file mode 100644
index 0000000..d193f9b
--- /dev/null
+++ b/config.json
@@ -0,0 +1,84 @@
+{
+  "interests": {
+    "Efficient ML / Edge AI": {
+      "query": "cat:cs.LG OR cat:cs.CV OR cat:cs.CL",
+      "keywords": [
+        "efficient",
+        "edge",
+        "compression",
+        "quantization",
+        "pruning",
+        "distillation",
+        "inference",
+        "lightweight",
+        "mobile",
+        "accelerat"
+      ]
+    },
+    "Privacy-Preserving ML": {
+      "query": "cat:cs.CR OR cat:cs.LG",
+      "keywords": [
+        "privacy",
+        "federated",
+        "differential",
+        "secure",
+        "encrypted",
+        "confidential",
+        "private",
+        "anonymi"
+      ]
+    },
+    "Creative AI / Emotion": {
+      "query": "cat:cs.AI OR cat:cs.SD OR cat:cs.HC",
+      "keywords": [
+        "emotion",
+        "generative",
+        "creative",
+        "music",
+        "affective",
+        "sentiment",
+        "art",
+        "design",
+        "audio",
+        "synthesis"
+      ]
+    },
+    "Lightweight Systems": {
+      "query": "cat:cs.DC OR cat:cs.AR",
+      "keywords": [
+        "embedded",
+        "iot",
+        "edge",
+        "resource",
+        "constrained",
+        "microcontroller",
+        "low-power",
+        "sensor",
+        "device"
+      ]
+    },
+    "Offline-First / Local AI": {
+      "query": "cat:cs.LG",
+      "keywords": [
+        "local",
+        "device",
+        "mobile",
+        "offline",
+        "on-device",
+        "edge",
+        "browser",
+        "client-side",
+        "standalone"
+      ]
+    }
+  },
+  "settings": {
+    "papers_per_interest": 10,
+    "summary_max_length": 160,
+    "recent_days": 7,
+    "fallback_days": 90,
+    "min_papers_threshold": 5,
+    "fetch_multiplier": 5,
+    "user_agent": "ResearchDigestBot/1.0 (github.com/wedsmoker)"
+  }
+}
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zBGq3TpRUu=vsa#G{?xh-cVntSn3wQuYJ@xGllHj>_rw~imvd?E^rnS|cctk-iMe1B
z)<x}@L7`9moVAquWY)j4QR)+|=h*&kRvVaog{1ECQj4KGP(ZFDC+miNSoxRm{UyfD
l$)x;2g!heT^3&cM;sY^ln)fmYA`jttl45f2%S1l<{C~?aE$ILN

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HcmV?d00001

diff --git a/generate_index.py b/generate_index.py
new file mode 100644
index 0000000..865a6c5
--- /dev/null
+++ b/generate_index.py
@@ -0,0 +1,205 @@
+"""Generate an index.html page to browse all archived digests."""
+import os
+from datetime import datetime
+import glob
+
+def generate_index():
+    archive_dir = "arxiv_archive"
+
+    # Get all digest files
+    if os.path.exists(archive_dir):
+        digest_files = sorted(glob.glob(os.path.join(archive_dir, "arxiv_digest_*.html")), reverse=True)
+    else:
+        digest_files = []
+
+    # Parse dates and create entries
+    entries = []
+    for filepath in digest_files:
+        filename = os.path.basename(filepath)
+        # Extract date from filename: arxiv_digest_20251101.html
+        date_str = filename.replace("arxiv_digest_", "").replace(".html", "")
+        try:
+            date_obj = datetime.strptime(date_str, "%Y%m%d")
+            formatted_date = date_obj.strftime("%B %d, %Y")
+            day_of_week = date_obj.strftime("%A")
+            entries.append({
+                'filename': filename,
+                'date': formatted_date,
+                'day': day_of_week,
+                'date_obj': date_obj
+            })
+        except ValueError:
+            continue
+
+    html = f"""<!DOCTYPE html>
+<html lang="en">
+<head>
+  <meta charset="UTF-8">
+  <meta name="viewport" content="width=device-width, initial-scale=1.0">
+  <title>arXiv Digest Archive</title>
+  <style>
+    * {{ box-sizing: border-box; }}
+
+    body {{
+      font-family: 'Inter', -apple-system, BlinkMacSystemFont, 'Segoe UI', Roboto, sans-serif;
+      background: #0f0f0f;
+      color: #e8e8e8;
+      margin: 0;
+      padding: 2rem;
+    }}
+
+    .container {{
+      max-width: 900px;
+      margin: 0 auto;
+    }}
+
+    header {{
+      text-align: center;
+      margin-bottom: 3rem;
+      padding-bottom: 2rem;
+      border-bottom: 2px solid #2a2a2a;
+    }}
+
+    h1 {{
+      font-weight: 900;
+      font-size: 2.5rem;
+      margin: 0 0 0.5rem 0;
+      background: linear-gradient(135deg, #ff6b6b, #ffa94d);
+      -webkit-background-clip: text;
+      -webkit-text-fill-color: transparent;
+      background-clip: text;
+    }}
+
+    .subtitle {{
+      color: #999;
+      font-size: 1rem;
+    }}
+
+    .latest-link {{
+      display: inline-block;
+      background: #ff6b6b;
+      color: white;
+      padding: 1rem 2rem;
+      border-radius: 8px;
+      text-decoration: none;
+      font-weight: 700;
+      margin-bottom: 3rem;
+      transition: transform 0.2s, box-shadow 0.2s;
+    }}
+
+    .latest-link:hover {{
+      transform: translateY(-2px);
+      box-shadow: 0 8px 24px rgba(255, 107, 107, 0.3);
+    }}
+
+    .archive-list {{
+      list-style: none;
+      padding: 0;
+    }}
+
+    .archive-item {{
+      background: #1a1a1a;
+      border: 1px solid #2a2a2a;
+      border-radius: 10px;
+      padding: 1.5rem;
+      margin-bottom: 1rem;
+      transition: all 0.2s;
+    }}
+
+    .archive-item:hover {{
+      border-color: #ff6b6b;
+      transform: translateX(5px);
+    }}
+
+    .archive-item a {{
+      text-decoration: none;
+      color: #e8e8e8;
+      display: flex;
+      justify-content: space-between;
+      align-items: center;
+    }}
+
+    .date-info {{
+      display: flex;
+      flex-direction: column;
+      gap: 0.3rem;
+    }}
+
+    .date-main {{
+      font-size: 1.2rem;
+      font-weight: 700;
+      color: #6ba3ff;
+    }}
+
+    .date-day {{
+      font-size: 0.9rem;
+      color: #999;
+    }}
+
+    .arrow {{
+      font-size: 1.5rem;
+      color: #ff6b6b;
+    }}
+
+    .no-reports {{
+      text-align: center;
+      color: #999;
+      padding: 3rem;
+    }}
+
+    .stats {{
+      text-align: center;
+      margin-top: 3rem;
+      padding-top: 2rem;
+      border-top: 1px solid #2a2a2a;
+      color: #999;
+      font-size: 0.9rem;
+    }}
+  </style>
+</head>
+<body>
+  <div class="container">
+    <header>
+      <h1>📚 arXiv Digest Archive</h1>
+      <p class="subtitle">Browse your daily research digests</p>
+    </header>
+
+    <div style="text-align: center;">
+      <a href="latest.html" class="latest-link">📰 View Latest Digest</a>
+    </div>
+
+    <h2 style="margin-bottom: 1.5rem; color: #e8e8e8;">Past Reports</h2>
+"""
+
+    if entries:
+        html += '    <ul class="archive-list">\n'
+        for entry in entries:
+            html += f"""      <li class="archive-item">
+        <a href="arxiv_archive/{entry['filename']}">
+          <div class="date-info">
+            <div class="date-main">{entry['date']}</div>
+            <div class="date-day">{entry['day']}</div>
+          </div>
+          <div class="arrow">→</div>
+        </a>
+      </li>
+"""
+        html += '    </ul>\n'
+    else:
+        html += '    <div class="no-reports">No archived reports yet. Run the digest script to generate your first report!</div>\n'
+
+    html += f"""
+    <div class="stats">
+      {len(entries)} report{"s" if len(entries) != 1 else ""} archived • Updated {datetime.now().strftime("%B %d, %Y at %I:%M %p")}
+    </div>
+  </div>
+</body>
+</html>
+"""
+
+    with open("index.html", 'w', encoding='utf-8') as f:
+        f.write(html)
+    print(f"📑 Index page generated with {len(entries)} reports")
+
+if __name__ == "__main__":
+    generate_index()
diff --git a/generate_tiktok_feed.py b/generate_tiktok_feed.py
new file mode 100644
index 0000000..646e9ed
--- /dev/null
+++ b/generate_tiktok_feed.py
@@ -0,0 +1,512 @@
+import json
+import random
+from datetime import datetime
+
+def interleave_papers_by_interest(all_papers_by_interest):
+    """
+    Interleave papers round-robin style across interests.
+    Returns a flat list cycling through: Interest1[0], Interest2[0], ..., Interest1[1], Interest2[1], ...
+    """
+    # Shuffle papers within each interest category
+    for interest_name in all_papers_by_interest:
+        random.shuffle(all_papers_by_interest[interest_name])
+
+    # Interleave round-robin
+    interleaved = []
+    interest_names = list(all_papers_by_interest.keys())
+    max_papers = max(len(papers) for papers in all_papers_by_interest.values()) if all_papers_by_interest else 0
+
+    for i in range(max_papers):
+        for interest_name in interest_names:
+            papers = all_papers_by_interest[interest_name]
+            if i < len(papers):
+                # Add interest category to paper data
+                papers[i]['interest_category'] = interest_name
+                interleaved.append(papers[i])
+
+    return interleaved
+
+def generate_tiktok_html(interleaved_papers):
+    """Generate self-contained TikTok-style feed HTML with embedded data."""
+
+    papers_json = json.dumps(interleaved_papers, indent=2, ensure_ascii=False)
+    date_str = datetime.now().strftime('%B %d, %Y')
+
+    html = f"""<!DOCTYPE html>
+<html lang="en">
+<head>
+  <meta charset="UTF-8" />
+  <meta name="viewport" content="width=device-width, initial-scale=1.0, user-scalable=no"/>
+  <title>Research Feed • {date_str}</title>
+  <style>
+    * {{
+      box-sizing: border-box;
+      margin: 0;
+      padding: 0;
+    }}
+
+    :root {{
+      --bg: #000000;
+      --text: #ffffff;
+      --muted: #a0a0a0;
+      --card-bg: #1a1a1a;
+      --border: #2a2a2a;
+      --accent: #ff6b6b;
+      --heart-red: #ff4458;
+      --layman-bg: #1f2937;
+      --layman-border: #60a5fa;
+    }}
+
+    body {{
+      font-family: -apple-system, BlinkMacSystemFont, 'Segoe UI', Roboto, sans-serif;
+      background: var(--bg);
+      color: var(--text);
+      overflow-x: hidden;
+      -webkit-font-smoothing: antialiased;
+    }}
+
+    #feed-container {{
+      height: 100vh;
+      overflow-y: scroll;
+      scroll-snap-type: y mandatory;
+      -webkit-overflow-scrolling: touch;
+      padding-top: 60px; /* Space for fixed header */
+    }}
+
+    .paper-card {{
+      min-height: 100vh;
+      scroll-snap-align: start;
+      scroll-snap-stop: always;
+      display: flex;
+      flex-direction: column;
+      justify-content: center;
+      padding: 2rem 1.5rem;
+      position: relative;
+      border-bottom: 1px solid var(--border);
+    }}
+
+    .interest-badge {{
+      display: inline-block;
+      background: var(--accent);
+      color: white;
+      padding: 0.4rem 0.9rem;
+      border-radius: 20px;
+      font-size: 0.7rem;
+      font-weight: 700;
+      text-transform: uppercase;
+      letter-spacing: 0.5px;
+      margin-bottom: 1rem;
+    }}
+
+    .difficulty-badge {{
+      display: inline-block;
+      padding: 0.3rem 0.7rem;
+      border-radius: 15px;
+      font-size: 0.7rem;
+      font-weight: 600;
+      margin-left: 0.5rem;
+    }}
+
+    .paper-title {{
+      font-size: 1.5rem;
+      font-weight: 800;
+      line-height: 1.3;
+      margin-bottom: 1rem;
+      color: var(--text);
+    }}
+
+    .layman-box {{
+      background: var(--layman-bg);
+      border-left: 3px solid var(--layman-border);
+      padding: 1rem;
+      margin-bottom: 1rem;
+      border-radius: 8px;
+      font-size: 0.95rem;
+      line-height: 1.6;
+      color: #94a3b8;
+      font-style: italic;
+    }}
+
+    .summary {{
+      color: var(--muted);
+      font-size: 0.95rem;
+      line-height: 1.6;
+      margin-bottom: 1.5rem;
+    }}
+
+    .paper-meta {{
+      display: flex;
+      justify-content: space-between;
+      align-items: center;
+      margin-bottom: 1rem;
+      color: var(--muted);
+      font-size: 0.8rem;
+    }}
+
+    .category-tag {{
+      background: #1e3a5f;
+      color: #60a5fa;
+      padding: 0.3rem 0.7rem;
+      border-radius: 15px;
+      font-size: 0.75rem;
+      font-weight: 600;
+    }}
+
+    .links {{
+      display: flex;
+      gap: 1.5rem;
+      margin-bottom: 2rem;
+    }}
+
+    .links a {{
+      color: #6ba3ff;
+      text-decoration: none;
+      font-size: 0.9rem;
+      font-weight: 600;
+      display: flex;
+      align-items: center;
+      gap: 0.3rem;
+    }}
+
+    .links a:active {{
+      color: var(--accent);
+    }}
+
+    /* Fixed header with export button */
+    .fixed-header {{
+      position: fixed;
+      top: 0;
+      left: 0;
+      right: 0;
+      background: rgba(0, 0, 0, 0.95);
+      backdrop-filter: blur(10px);
+      padding: 0.8rem 1.5rem;
+      display: flex;
+      align-items: center;
+      justify-content: space-between;
+      border-bottom: 1px solid var(--border);
+      z-index: 200;
+    }}
+
+    .like-counter {{
+      display: flex;
+      align-items: center;
+      gap: 0.5rem;
+      color: var(--muted);
+      font-size: 0.9rem;
+    }}
+
+    .export-button {{
+      background: linear-gradient(135deg, var(--accent), #ffa94d);
+      color: white;
+      border: none;
+      padding: 0.6rem 1.2rem;
+      border-radius: 20px;
+      font-weight: 700;
+      font-size: 0.85rem;
+      cursor: pointer;
+      transition: all 0.2s ease;
+      -webkit-tap-highlight-color: transparent;
+      opacity: 0.5;
+      pointer-events: none;
+    }}
+
+    .export-button.active {{
+      opacity: 1;
+      pointer-events: auto;
+    }}
+
+    .export-button.active:active {{
+      transform: scale(0.95);
+    }}
+
+    .like-button {{
+      position: fixed;
+      bottom: 2rem;
+      right: 1.5rem;
+      width: 60px;
+      height: 60px;
+      border-radius: 50%;
+      background: rgba(26, 26, 26, 0.9);
+      border: 2px solid var(--border);
+      display: flex;
+      align-items: center;
+      justify-content: center;
+      font-size: 1.8rem;
+      cursor: pointer;
+      transition: all 0.2s ease;
+      z-index: 100;
+      -webkit-tap-highlight-color: transparent;
+    }}
+
+    .like-button:active {{
+      transform: scale(0.9);
+    }}
+
+    .like-button.liked {{
+      background: var(--heart-red);
+      border-color: var(--heart-red);
+      animation: heartbeat 0.3s ease;
+    }}
+
+    @keyframes heartbeat {{
+      0%, 100% {{ transform: scale(1); }}
+      50% {{ transform: scale(1.2); }}
+    }}
+
+    .scroll-indicator {{
+      position: fixed;
+      bottom: 1rem;
+      left: 50%;
+      transform: translateX(-50%);
+      color: var(--muted);
+      font-size: 0.8rem;
+      animation: bounce 2s infinite;
+      z-index: 50;
+    }}
+
+    @keyframes bounce {{
+      0%, 100% {{ transform: translateX(-50%) translateY(0); }}
+      50% {{ transform: translateX(-50%) translateY(-10px); }}
+    }}
+
+    .hide-indicator {{
+      display: none;
+    }}
+  </style>
+</head>
+<body>
+  <!-- Fixed Header with Export Button -->
+  <div class="fixed-header">
+    <div class="like-counter">
+      <span>♥</span>
+      <span><span id="likeCount">0</span> liked</span>
+    </div>
+    <button class="export-button" id="exportButton">Export Likes</button>
+  </div>
+
+  <div id="feed-container"></div>
+
+  <div class="like-button" id="likeButton">
+    <span id="heartIcon">♡</span>
+  </div>
+
+  <div class="scroll-indicator" id="scrollIndicator">
+    ↓ Scroll to explore
+  </div>
+
+  <script>
+    // ============================================
+    // EMBEDDED PAPERS DATA
+    // ============================================
+    const papers = {papers_json};
+
+    // ============================================
+    // STATE MANAGEMENT
+    // ============================================
+    let likes = JSON.parse(localStorage.getItem('tiktok_likes') || '{{}}');
+    let currentPaperIndex = 0;
+
+    // ============================================
+    // RENDER FEED
+    // ============================================
+    function renderFeed() {{
+      const container = document.getElementById('feed-container');
+
+      papers.forEach((paper, index) => {{
+        const card = document.createElement('div');
+        card.className = 'paper-card';
+        card.dataset.index = index;
+
+        card.innerHTML = `
+          <div class="interest-badge">${{paper.interest_category}}</div>
+          <div class="difficulty-badge">${{paper.difficulty}}</div>
+
+          <h1 class="paper-title">${{paper.title}}</h1>
+
+          <div class="layman-box">💡 ${{paper.layman}}</div>
+
+          <div class="summary">${{paper.summary}}</div>
+
+          <div class="paper-meta">
+            <span class="category-tag">${{paper.category}}</span>
+            <span class="date">${{paper.published}}</span>
+          </div>
+
+          <div class="links">
+            <a href="${{paper.link}}" target="_blank">Abstract ↗</a>
+            <a href="${{paper.pdf_link}}" target="_blank">PDF ↗</a>
+          </div>
+        `;
+
+        container.appendChild(card);
+      }});
+    }}
+
+    // ============================================
+    // LIKE SYSTEM
+    // ============================================
+    function getCurrentPaper() {{
+      const container = document.getElementById('feed-container');
+      const scrollPos = container.scrollTop;
+      const windowHeight = window.innerHeight;
+
+      // Find which paper is currently in view
+      const cards = document.querySelectorAll('.paper-card');
+      for (let i = 0; i < cards.length; i++) {{
+        const rect = cards[i].getBoundingClientRect();
+        if (rect.top >= -windowHeight/2 && rect.top < windowHeight/2) {{
+          return i;
+        }}
+      }}
+      return 0;
+    }}
+
+    function toggleLike() {{
+      const paperIndex = getCurrentPaper();
+      const paper = papers[paperIndex];
+      const arxivId = paper.arxiv_id;
+
+      const heartIcon = document.getElementById('heartIcon');
+      const likeButton = document.getElementById('likeButton');
+
+      if (likes[arxivId]) {{
+        // Unlike
+        delete likes[arxivId];
+        heartIcon.textContent = '♡';
+        likeButton.classList.remove('liked');
+      }} else {{
+        // Like
+        likes[arxivId] = {{
+          arxiv_id: arxivId,
+          title: paper.title,
+          abstract_url: paper.link,
+          category: paper.category,
+          interest_category: paper.interest_category,
+          liked_date: new Date().toISOString(),
+          difficulty: paper.difficulty
+        }};
+        heartIcon.textContent = '♥';
+        likeButton.classList.add('liked');
+      }}
+
+      // Save to localStorage
+      localStorage.setItem('tiktok_likes', JSON.stringify(likes));
+
+      // Update counter and export button
+      updateCounter();
+      updateExportButton();
+    }}
+
+    function updateLikeButton() {{
+      const paperIndex = getCurrentPaper();
+      const paper = papers[paperIndex];
+      const heartIcon = document.getElementById('heartIcon');
+      const likeButton = document.getElementById('likeButton');
+
+      if (likes[paper.arxiv_id]) {{
+        heartIcon.textContent = '♥';
+        likeButton.classList.add('liked');
+      }} else {{
+        heartIcon.textContent = '♡';
+        likeButton.classList.remove('liked');
+      }}
+    }}
+
+    function updateCounter() {{
+      const count = Object.keys(likes).length;
+      document.getElementById('likeCount').textContent = count;
+    }}
+
+    function updateExportButton() {{
+      const exportButton = document.getElementById('exportButton');
+      if (Object.keys(likes).length > 0) {{
+        exportButton.classList.add('active');
+      }} else {{
+        exportButton.classList.remove('active');
+      }}
+    }}
+
+    // ============================================
+    // EXPORT LIKES
+    // ============================================
+    function exportLikes() {{
+      const likedPapers = Object.values(likes);
+
+      // Calculate category preferences
+      const preferences = {{}};
+      likedPapers.forEach(paper => {{
+        const cat = paper.interest_category;
+        preferences[cat] = (preferences[cat] || 0) + 1;
+      }});
+
+      const exportData = {{
+        liked_papers: likedPapers,
+        preferences: preferences,
+        export_date: new Date().toISOString(),
+        total_likes: likedPapers.length
+      }};
+
+      const blob = new Blob([JSON.stringify(exportData, null, 2)], {{
+        type: 'application/json'
+      }});
+
+      const url = URL.createObjectURL(blob);
+      const a = document.createElement('a');
+      a.href = url;
+      a.download = `arxiv_likes_${{new Date().toISOString().split('T')[0]}}.json`;
+      document.body.appendChild(a);
+      a.click();
+      document.body.removeChild(a);
+      URL.revokeObjectURL(url);
+    }}
+
+    // ============================================
+    // EVENT LISTENERS
+    // ============================================
+    document.getElementById('likeButton').addEventListener('click', toggleLike);
+    document.getElementById('exportButton').addEventListener('click', exportLikes);
+
+    // Update like button when scrolling
+    document.getElementById('feed-container').addEventListener('scroll', () => {{
+      updateLikeButton();
+
+      // Hide scroll indicator after first scroll
+      const scrollIndicator = document.getElementById('scrollIndicator');
+      if (document.getElementById('feed-container').scrollTop > 50) {{
+        scrollIndicator.classList.add('hide-indicator');
+      }}
+    }});
+
+    // ============================================
+    // INITIALIZATION
+    // ============================================
+    renderFeed();
+    updateLikeButton();
+    updateCounter();
+    updateExportButton();
+  </script>
+</body>
+</html>
+"""
+
+    return html
+
+def save_tiktok_feed(all_papers_by_interest, filename='tiktok_feed.html'):
+    """
+    Generate and save TikTok-style feed from papers data.
+    Called by main.py after fetching papers.
+    """
+    # Interleave papers round-robin
+    interleaved = interleave_papers_by_interest(all_papers_by_interest)
+    print(f"\n🔄 Interleaved {len(interleaved)} papers across {len(all_papers_by_interest)} interests")
+
+    # Generate HTML
+    html = generate_tiktok_html(interleaved)
+
+    # Save file
+    with open(filename, 'w', encoding='utf-8') as f:
+        f.write(html)
+
+    print(f"✨ TikTok feed saved to {filename}")
+    print("📱 Sync with your phone and open in browser!")
diff --git a/main.py b/main.py
new file mode 100644
index 0000000..ac6348d
--- /dev/null
+++ b/main.py
@@ -0,0 +1,724 @@
+import os
+import time
+import json
+import xml.etree.ElementTree as ET
+import requests
+from transformers import pipeline
+from datetime import datetime, timedelta
+from generate_tiktok_feed import save_tiktok_feed
+
+# ======================
+# CONFIGURATION
+# ======================
+
+def load_config():
+    """Load configuration from config.json file."""
+    config_file = "config.json"
+
+    # Default configuration (fallback)
+    default_config = {
+        "interests": {
+            "Efficient ML / Edge AI": {
+                "query": 'cat:cs.LG OR cat:cs.CV OR cat:cs.CL',
+                "keywords": ['efficient', 'edge', 'compression', 'quantization', 'pruning', 'distillation', 'inference', 'lightweight', 'mobile', 'accelerat']
+            }
+        },
+        "settings": {
+            "papers_per_interest": 10,
+            "summary_max_length": 160,
+            "recent_days": 7,
+            "fallback_days": 90,
+            "min_papers_threshold": 5,
+            "fetch_multiplier": 5,
+            "user_agent": "ResearchDigestBot/1.0 (github.com/wedsmoker)"
+        }
+    }
+
+    if os.path.exists(config_file):
+        try:
+            with open(config_file, 'r', encoding='utf-8') as f:
+                config = json.load(f)
+                print(f"✅ Loaded configuration from {config_file}")
+                return config
+        except Exception as e:
+            print(f"⚠️ Error loading config file: {e}. Using defaults.")
+            return default_config
+    else:
+        print(f"⚠️ {config_file} not found. Using default configuration.")
+        return default_config
+
+# Load configuration
+config = load_config()
+INTERESTS = config.get('interests', {})
+settings = config.get('settings', {})
+
+PAPERS_PER_INTEREST = settings.get('papers_per_interest', 10)
+SUMMARY_MAX_LENGTH = settings.get('summary_max_length', 160)
+USER_AGENT = settings.get('user_agent', 'ResearchDigestBot/1.0')
+
+# Date filtering: Only fetch papers from the last N days (set to 0 to disable)
+RECENT_DAYS = settings.get('recent_days', 7)
+FALLBACK_DAYS = settings.get('fallback_days', 90)
+MIN_PAPERS_THRESHOLD = settings.get('min_papers_threshold', 5)
+FETCH_MULTIPLIER = settings.get('fetch_multiplier', 5)
+
+# Deduplication: Track papers we've already shown
+SEEN_PAPERS_FILE = "seen_papers.json"
+
+# Initialize summarizer (optional)
+try:
+    summarizer = pipeline(
+        "summarization",
+        model="sshleifer/distilbart-cnn-12-6",
+        device=-1
+    )
+except Exception as e:
+    print(f"⚠️ Summarizer unavailable ({e}). Using raw abstracts.")
+    summarizer = None
+
+# ======================
+# DEDUPLICATION HELPERS
+# ======================
+
+def load_seen_papers():
+    """Load the set of previously seen paper IDs."""
+    if os.path.exists(SEEN_PAPERS_FILE):
+        try:
+            with open(SEEN_PAPERS_FILE, 'r') as f:
+                data = json.load(f)
+                return set(data.get('seen_ids', []))
+        except Exception as e:
+            print(f"⚠️ Error loading seen papers: {e}")
+    return set()
+
+def save_seen_papers(seen_ids):
+    """Save the set of seen paper IDs."""
+    try:
+        with open(SEEN_PAPERS_FILE, 'w') as f:
+            json.dump({
+                'seen_ids': list(seen_ids),
+                'last_updated': datetime.now().isoformat()
+            }, f, indent=2)
+    except Exception as e:
+        print(f"⚠️ Error saving seen papers: {e}")
+
+def get_date_filter(days=None):
+    """Generate date filter for arXiv query (last N days)."""
+    if days is None:
+        days = RECENT_DAYS
+
+    if days <= 0:
+        return ""
+
+    end_date = datetime.now()
+    start_date = end_date - timedelta(days=days)
+
+    # arXiv date format: YYYYMMDD0000 to YYYYMMDD2359
+    date_filter = f"submittedDate:[{start_date.strftime('%Y%m%d')}0000 TO {end_date.strftime('%Y%m%d')}2359]"
+    return date_filter
+
+# ======================
+# ARXIV FETCH & PARSE
+# ======================
+
+def fetch_arxiv_papers(query, max_results=5, days_back=None):
+    url = "http://export.arxiv.org/api/query"
+
+    # Add date filter if configured
+    date_filter = get_date_filter(days_back)
+    if date_filter:
+        # Combine user query with date filter using AND
+        query = f"({query}) AND {date_filter}"
+
+    params = {
+        "search_query": query,
+        "start": 0,
+        "max_results": max_results,
+        "sortBy": "submittedDate",
+        "sortOrder": "descending"
+    }
+    headers = {"User-Agent": USER_AGENT}
+    try:
+        response = requests.get(url, params=params, headers=headers, timeout=20)
+        response.raise_for_status()
+        return response.text
+    except Exception as e:
+        print(f"❌ Error fetching query '{query}': {e}")
+        return None
+
+def parse_papers(xml_data):
+    if not xml_data:
+        return []
+    try:
+        root = ET.fromstring(xml_data)
+    except ET.ParseError:
+        return []
+
+    namespace = {'atom': 'http://www.w3.org/2005/Atom'}
+    papers = []
+
+    for entry in root.findall('atom:entry', namespace):
+        title_elem = entry.find('atom:title', namespace)
+        summary_elem = entry.find('atom:summary', namespace)
+        id_elem = entry.find('atom:id', namespace)
+        published_elem = entry.find('atom:published', namespace)
+
+        if None in (title_elem, summary_elem, id_elem):
+            continue
+
+        title = ' '.join(title_elem.text.strip().split())
+        summary = ' '.join(summary_elem.text.strip().split())
+        link = id_elem.text
+        published = published_elem.text.split('T')[0] if published_elem is not None else "Unknown"
+
+        # Extract arXiv ID
+        arxiv_id = link.split('/abs/')[-1].split('v')[0]
+
+        # Get primary category
+        primary_cat_elem = entry.find('.//{http://arxiv.org/schemas/atom}primary_category')
+        category = primary_cat_elem.get('term') if primary_cat_elem is not None else "unknown"
+
+        papers.append({
+            'title': title,
+            'summary': summary,
+            'link': link,
+            'pdf_link': f"https://arxiv.org/pdf/{arxiv_id}.pdf",
+            'arxiv_id': arxiv_id,
+            'category': category,
+            'published': published
+        })
+    return papers
+
+def summarize_abstract(abstract):
+    if summarizer is None:
+        return abstract[:SUMMARY_MAX_LENGTH] + ("..." if len(abstract) > SUMMARY_MAX_LENGTH else "")
+    try:
+        if len(abstract.split()) < 15:
+            return abstract
+        result = summarizer(
+            abstract,
+            max_length=min(SUMMARY_MAX_LENGTH, 142),
+            min_length=30,
+            truncation=True
+        )
+        return result[0]['summary_text']
+    except Exception as e:
+        return abstract[:SUMMARY_MAX_LENGTH] + "..."
+
+def calculate_relevance_score(paper, keywords):
+    """Calculate relevance score based on keyword matches in title and abstract."""
+    title_lower = paper['title'].lower()
+    abstract_lower = paper['summary'].lower()
+
+    score = 0
+    matched_keywords = []
+
+    for keyword in keywords:
+        keyword_lower = keyword.lower()
+        # Title matches are worth more
+        if keyword_lower in title_lower:
+            score += 3
+            matched_keywords.append(keyword)
+        # Abstract matches
+        elif keyword_lower in abstract_lower:
+            score += 1
+            matched_keywords.append(keyword)
+
+    # Bonus for multiple keyword matches
+    if len(matched_keywords) > 2:
+        score += len(matched_keywords) - 2
+
+    paper['relevance_score'] = score
+    paper['matched_keywords'] = matched_keywords
+    return score
+
+def estimate_difficulty(abstract, category):
+    """Estimate paper difficulty using heuristic keyword analysis."""
+    abstract_lower = abstract.lower()
+
+    # Theory-heavy indicators
+    complexity_words = ['theoretical', 'proof', 'theorem', 'convergence', 'optimal',
+                        'asymptotic', 'lemma', 'proposition', 'rigorous', 'formalism']
+
+    # Applied/practical indicators
+    applied_words = ['system', 'framework', 'application', 'dataset', 'benchmark',
+                     'implementation', 'experiment', 'empirical', 'practical']
+
+    # Math-heavy categories
+    math_categories = ['math.', 'stat.', 'quant-ph']
+
+    # Calculate score
+    score = sum(1 for w in complexity_words if w in abstract_lower)
+    score -= sum(0.5 for w in applied_words if w in abstract_lower)
+
+    # Category bonus
+    if any(cat in category for cat in math_categories):
+        score += 1
+
+    # Determine difficulty level
+    if score > 2:
+        return "🔴 Theory-Heavy"
+    elif score > 0.5:
+        return "🟡 Advanced"
+    else:
+        return "🟢 Applied"
+
+def generate_layman_context(title, abstract):
+    """Generate simple layman explanation using keyword extraction and templates."""
+    abstract_lower = abstract.lower()
+
+    # Extract key action words and concepts
+    action_map = {
+        'improv': 'improves',
+        'reduc': 'reduces',
+        'enhanc': 'enhances',
+        'optimi': 'optimizes',
+        'acceler': 'speeds up',
+        'efficient': 'makes more efficient',
+        'novel': 'introduces a new approach to',
+        'outperform': 'works better than existing methods for',
+        'achiev': 'achieves better',
+        'propose': 'proposes a method for',
+        'present': 'presents techniques for',
+        'address': 'tackles the problem of',
+        'privacy': 'protecting data privacy in',
+        'federated': 'distributed machine learning across',
+        'emotion': 'understanding emotions in',
+        'embedded': 'running AI on low-power devices for',
+        'edge': 'running AI locally on devices for',
+        'compression': 'making models smaller for',
+        'inference': 'faster predictions in',
+        'generative': 'creating new content with',
+        'detection': 'automatically finding',
+        'classification': 'categorizing',
+        'prediction': 'forecasting'
+    }
+
+    # Find first matching action
+    action = "explores techniques in"
+    for keyword, phrase in action_map.items():
+        if keyword in abstract_lower[:300]:  # Check first part of abstract
+            action = phrase
+            break
+
+    # Extract domain
+    domain = "machine learning"
+    if "language model" in abstract_lower or "llm" in abstract_lower or "nlp" in abstract_lower:
+        domain = "language AI"
+    elif "vision" in abstract_lower or "image" in abstract_lower or "visual" in abstract_lower:
+        domain = "computer vision"
+    elif "speech" in abstract_lower or "audio" in abstract_lower:
+        domain = "speech processing"
+    elif "privacy" in abstract_lower or "federated" in abstract_lower:
+        domain = "privacy-preserving AI"
+    elif "edge" in abstract_lower or "embedded" in abstract_lower or "device" in abstract_lower:
+        domain = "edge computing"
+    elif "emotion" in abstract_lower or "affective" in abstract_lower:
+        domain = "emotion AI"
+
+    return f"This research {action} {domain}."
+
+# ======================
+# HTML OUTPUT
+# ======================
+
+def save_html_digest(all_papers_by_interest, filename=None):
+    # Create archive directory if it doesn't exist
+    archive_dir = "arxiv_archive"
+    if not os.path.exists(archive_dir):
+        os.makedirs(archive_dir)
+
+    if filename is None:
+        date_str = datetime.now().strftime('%Y%m%d')
+        filename = os.path.join(archive_dir, f"arxiv_digest_{date_str}.html")
+
+    # Also save as latest.html for easy syncing
+    latest_file = "latest.html"
+
+    html = f"""<!DOCTYPE html>
+<html lang="en">
+<head>
+  <meta charset="UTF-8" />
+  <meta name="viewport" content="width=device-width, initial-scale=1.0"/>
+  <title>arXiv Digest • {datetime.now().strftime('%Y-%m-%d')}</title>
+  <style>
+    * {{ box-sizing: border-box; }}
+
+    :root {{
+      --bg: #0f0f0f;
+      --text: #e8e8e8;
+      --muted: #999;
+      --border: #2a2a2a;
+      --card-bg: #1a1a1a;
+      --link: #6ba3ff;
+      --accent: #ff6b6b;
+      --green: #51cf66;
+      --yellow: #ffd43b;
+      --red: #ff6b6b;
+      --layman-bg: #1f2937;
+      --layman-border: #60a5fa;
+    }}
+
+    body {{
+      font-family: 'Inter', -apple-system, BlinkMacSystemFont, 'Segoe UI', Roboto, sans-serif;
+      line-height: 1.5;
+      color: var(--text);
+      background: var(--bg);
+      margin: 0;
+      padding: 1rem;
+    }}
+
+    .container {{
+      max-width: 1600px;
+      margin: 0 auto;
+    }}
+
+    header {{
+      text-align: center;
+      padding: 2rem 1rem 3rem;
+      border-bottom: 2px solid var(--border);
+      margin-bottom: 2rem;
+    }}
+
+    h1 {{
+      font-weight: 900;
+      font-size: 2.5rem;
+      margin: 0;
+      background: linear-gradient(135deg, var(--accent), #ffa94d);
+      -webkit-background-clip: text;
+      -webkit-text-fill-color: transparent;
+      background-clip: text;
+    }}
+
+    .meta {{
+      color: var(--muted);
+      font-size: 0.95rem;
+      margin-top: 0.5rem;
+      letter-spacing: 0.5px;
+    }}
+
+    .interest-section {{
+      margin-bottom: 3rem;
+    }}
+
+    .interest-header {{
+      display: flex;
+      align-items: center;
+      gap: 0.8rem;
+      margin-bottom: 1.2rem;
+      padding: 0.8rem 1rem;
+      background: var(--card-bg);
+      border-radius: 12px;
+      border-left: 4px solid var(--accent);
+    }}
+
+    .interest-title {{
+      font-size: 1.3rem;
+      margin: 0;
+      font-weight: 700;
+      color: var(--text);
+    }}
+
+    .papers-grid {{
+      display: grid;
+      grid-template-columns: repeat(auto-fill, minmax(380px, 1fr));
+      gap: 1.2rem;
+    }}
+
+    .paper {{
+      background: var(--card-bg);
+      border: 1px solid var(--border);
+      border-radius: 12px;
+      padding: 1.2rem;
+      transition: all 0.2s ease;
+      position: relative;
+      display: flex;
+      flex-direction: column;
+      height: 100%;
+    }}
+
+    .paper:hover {{
+      border-color: var(--accent);
+      transform: translateY(-2px);
+      box-shadow: 0 8px 24px rgba(255, 107, 107, 0.15);
+    }}
+
+    .paper-header {{
+      display: flex;
+      justify-content: space-between;
+      align-items: flex-start;
+      gap: 0.8rem;
+      margin-bottom: 0.8rem;
+    }}
+
+    .difficulty-badge {{
+      padding: 0.3rem 0.7rem;
+      border-radius: 20px;
+      font-size: 0.7rem;
+      font-weight: 700;
+      white-space: nowrap;
+      flex-shrink: 0;
+    }}
+
+    .paper h3 {{
+      font-size: 1.05rem;
+      margin: 0 0 0.8rem 0;
+      font-weight: 700;
+      line-height: 1.4;
+      color: var(--text);
+    }}
+
+    .layman-box {{
+      background: var(--layman-bg);
+      border-left: 3px solid var(--layman-border);
+      padding: 0.7rem 0.9rem;
+      margin-bottom: 0.8rem;
+      border-radius: 6px;
+      font-size: 0.88rem;
+      line-height: 1.5;
+      color: #94a3b8;
+      font-style: italic;
+    }}
+
+    .summary {{
+      color: var(--muted);
+      margin-bottom: 1rem;
+      font-size: 0.88rem;
+      line-height: 1.6;
+      flex-grow: 1;
+    }}
+
+    .paper-footer {{
+      display: flex;
+      justify-content: space-between;
+      align-items: center;
+      padding-top: 0.8rem;
+      border-top: 1px solid var(--border);
+      margin-top: auto;
+    }}
+
+    .category-tag {{
+      background: #1e3a5f;
+      color: #60a5fa;
+      padding: 0.25rem 0.65rem;
+      border-radius: 15px;
+      font-size: 0.75rem;
+      font-weight: 600;
+    }}
+
+    .date {{
+      color: var(--muted);
+      font-size: 0.75rem;
+    }}
+
+    .links {{
+      display: flex;
+      gap: 1rem;
+      margin-top: 0.8rem;
+    }}
+
+    .links a {{
+      color: var(--link);
+      text-decoration: none;
+      font-size: 0.85rem;
+      font-weight: 600;
+      transition: color 0.2s;
+    }}
+
+    .links a:hover {{
+      color: var(--accent);
+    }}
+
+    .footer {{
+      text-align: center;
+      margin-top: 4rem;
+      padding: 2rem;
+      color: var(--muted);
+      font-size: 0.85rem;
+      border-top: 1px solid var(--border);
+    }}
+
+    @media (max-width: 768px) {{
+      .papers-grid {{
+        grid-template-columns: 1fr;
+      }}
+      h1 {{
+        font-size: 2rem;
+      }}
+    }}
+  </style>
+</head>
+<body>
+  <div class="container">
+    <header>
+      <h1>arXiv Research Digest</h1>
+      <div class="meta">{datetime.now().strftime('%B %d, %Y')} • {sum(len(p) for p in all_papers_by_interest.values())} papers across {len(all_papers_by_interest)} interests</div>
+    </header>
+"""
+
+    for interest_name, papers in all_papers_by_interest.items():
+        html += f"""<section class="interest-section">
+  <div class="interest-header">
+    <span>🔬</span>
+    <h2 class="interest-title">{interest_name}</h2>
+  </div>
+"""
+        if not papers:
+            html += '  <p>No recent papers found.</p>\n'
+        else:
+            html += '  <div class="papers-grid">\n'
+            for paper in papers:
+                html += f"""    <article class="paper">
+      <div class="paper-header">
+        <span class="difficulty-badge">{paper['difficulty']}</span>
+      </div>
+      <h3>{paper['title']}</h3>
+      <div class="layman-box">💡 {paper['layman']}</div>
+      <div class="summary">{paper['summary']}</div>
+      <div class="paper-footer">
+        <span class="category-tag">{paper['category']}</span>
+        <span class="date">{paper['published']}</span>
+      </div>
+      <div class="links">
+        <a href="{paper['link']}" target="_blank">Abstract ↗</a>
+        <a href="{paper['pdf_link']}" target="_blank">PDF ↗</a>
+      </div>
+    </article>
+"""
+            html += '  </div>\n'
+        html += "</section>\n"
+
+    html += """    <div class="footer">
+      ✨ Generated automatically • Powered by arXiv API
+    </div>
+  </div>
+</body>
+</html>
+"""
+    # Save archived version
+    with open(filename, 'w', encoding='utf-8') as f:
+        f.write(html)
+    print(f"✨ HTML digest saved to {filename}")
+
+    # Also save as latest.html for quick access
+    with open(latest_file, 'w', encoding='utf-8') as f:
+        f.write(html)
+    print(f"📄 Latest digest saved to {latest_file}")
+
+# ======================
+# MAIN EXECUTION
+# ======================
+
+if __name__ == "__main__":
+    # Load previously seen papers
+    seen_papers = load_seen_papers()
+    print(f"📋 Loaded {len(seen_papers)} previously seen papers")
+
+    if RECENT_DAYS > 0:
+        print(f"📅 Fetching papers from last {RECENT_DAYS} days")
+    else:
+        print("📅 Fetching all available papers (no date filter)")
+
+    all_papers = {}
+    new_papers_count = 0
+    duplicate_count = 0
+
+    for interest_name, interest_config in INTERESTS.items():
+        query = interest_config['query']
+        keywords = interest_config['keywords']
+
+        print(f"\n🔍 Fetching papers for: {interest_name}")
+        xml_data = fetch_arxiv_papers(query, PAPERS_PER_INTEREST * FETCH_MULTIPLIER)  # Fetch more to filter
+        papers = parse_papers(xml_data) if xml_data else []
+
+        print(f"   → Found {len(papers)} papers")
+
+        # Filter out duplicates and calculate relevance
+        fresh_papers = []
+        for p in papers:
+            if p['arxiv_id'] not in seen_papers:
+                # Store original abstract for analysis
+                original_abstract = p['summary']
+
+                # Calculate relevance score FIRST (before summarization)
+                calculate_relevance_score(p, keywords)
+
+                # Estimate difficulty level (use ORIGINAL abstract before summarization)
+                p['difficulty'] = estimate_difficulty(original_abstract, p['category'])
+
+                # Generate layman context (use ORIGINAL abstract for better keyword extraction)
+                p['layman'] = generate_layman_context(p['title'], original_abstract)
+
+                # Generate summary (do this last to avoid losing original abstract)
+                p['summary'] = summarize_abstract(original_abstract)
+
+                fresh_papers.append(p)
+            else:
+                duplicate_count += 1
+
+        # Sort by relevance score (highest first)
+        fresh_papers.sort(key=lambda x: x['relevance_score'], reverse=True)
+
+        # Take top N papers
+        top_papers = fresh_papers[:PAPERS_PER_INTEREST]
+
+        # Mark these papers as seen
+        for p in top_papers:
+            seen_papers.add(p['arxiv_id'])
+            new_papers_count += 1
+
+        all_papers[interest_name] = top_papers
+        print(f"   ✨ {len(top_papers)} new papers (from {len(fresh_papers)} candidates, skipped {len(papers) - len(fresh_papers)} duplicates)")
+        if top_papers:
+            print(f"   📊 Relevance scores: {[p['relevance_score'] for p in top_papers]}")
+
+        # FALLBACK: If we didn't get enough papers, try wider date range (only 1 extra request)
+        if len(top_papers) < MIN_PAPERS_THRESHOLD and FALLBACK_DAYS > RECENT_DAYS:
+            print(f"   🔄 Low yield, trying fallback search (last {FALLBACK_DAYS} days)...")
+            time.sleep(3)  # Respect rate limit before fallback request
+
+            xml_data_fallback = fetch_arxiv_papers(query, PAPERS_PER_INTEREST * FETCH_MULTIPLIER, days_back=FALLBACK_DAYS)
+            papers_fallback = parse_papers(xml_data_fallback) if xml_data_fallback else []
+
+            print(f"   → Found {len(papers_fallback)} papers in fallback")
+
+            # Process fallback papers
+            fallback_fresh = []
+            for p in papers_fallback:
+                if p['arxiv_id'] not in seen_papers:
+                    original_abstract = p['summary']
+                    calculate_relevance_score(p, keywords)
+                    p['difficulty'] = estimate_difficulty(original_abstract, p['category'])
+                    p['layman'] = generate_layman_context(p['title'], original_abstract)
+                    p['summary'] = summarize_abstract(original_abstract)
+                    fallback_fresh.append(p)
+
+            # Sort fallback papers by relevance
+            fallback_fresh.sort(key=lambda x: x['relevance_score'], reverse=True)
+
+            # Add top fallback papers to fill quota
+            needed = PAPERS_PER_INTEREST - len(top_papers)
+            additional_papers = fallback_fresh[:needed]
+
+            for p in additional_papers:
+                seen_papers.add(p['arxiv_id'])
+                new_papers_count += 1
+
+            top_papers.extend(additional_papers)
+            all_papers[interest_name] = top_papers
+            print(f"   ✨ After fallback: {len(top_papers)} total papers")
+
+        # Be kind: 3-second delay between queries (arXiv recommendation)
+        time.sleep(3)
+
+    # Save updated seen papers
+    save_seen_papers(seen_papers)
+
+    print(f"\n📊 Summary:")
+    print(f"   • Total new papers: {new_papers_count}")
+    print(f"   • Total duplicates skipped: {duplicate_count}")
+    print(f"   • Total tracked papers: {len(seen_papers)}")
+
+    save_html_digest(all_papers)
+    save_tiktok_feed(all_papers)
+    print("\n✅ Done! Open the HTML files in your browser.")
\ No newline at end of file
diff --git a/mobile_demo.png b/mobile_demo.png
new file mode 100644
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diff --git a/requirements.txt b/requirements.txt
new file mode 100644
index 0000000..590e429
--- /dev/null
+++ b/requirements.txt
@@ -0,0 +1,4 @@
+transformers==4.46.2
+torch==2.5.1
+torchvision==0.20.1
+requests==2.32.3
\ No newline at end of file
diff --git a/reset_seen_papers.py b/reset_seen_papers.py
new file mode 100644
index 0000000..f7b96e9
--- /dev/null
+++ b/reset_seen_papers.py
@@ -0,0 +1,17 @@
+"""
+Reset the seen_papers.json file to start fresh.
+Run this if you want to see papers again that were previously shown.
+"""
+import os
+import json
+
+SEEN_PAPERS_FILE = "seen_papers.json"
+
+if os.path.exists(SEEN_PAPERS_FILE):
+    # Backup old file
+    backup_file = SEEN_PAPERS_FILE.replace('.json', '_backup.json')
+    os.rename(SEEN_PAPERS_FILE, backup_file)
+    print(f"✅ Backed up old file to {backup_file}")
+    print(f"✅ Reset complete! Next run will show all papers as fresh.")
+else:
+    print("ℹ️ No seen_papers.json file found. Nothing to reset.")
diff --git a/run_digest.bat b/run_digest.bat
new file mode 100644
index 0000000..68097b5
--- /dev/null
+++ b/run_digest.bat
@@ -0,0 +1,50 @@
+@echo off
+REM ArXiv Digest Runner - Sets up environment and runs the script
+cd /d "%~dp0"
+
+REM Check if virtual environment exists
+if not exist "venv\" (
+    echo Virtual environment not found. Creating one...
+    python -m venv venv
+    if errorlevel 1 (
+        echo Error creating virtual environment!
+        echo Make sure Python is installed and available in PATH.
+        pause
+        exit /b 1
+    )
+    echo Virtual environment created successfully.
+
+    echo Installing dependencies...
+    call venv\Scripts\activate.bat
+    python -m pip install --upgrade pip
+    pip install -r requirements.txt
+    if errorlevel 1 (
+        echo Error installing dependencies!
+        pause
+        exit /b 1
+    )
+    echo Dependencies installed successfully.
+) else (
+    call venv\Scripts\activate.bat
+)
+
+echo Running arXiv digest...
+python main.py
+if errorlevel 1 (
+    echo Error running main script!
+    pause
+    goto :end
+)
+
+echo Generating index page...
+python generate_index.py
+if errorlevel 1 (
+    echo Error generating index!
+    pause
+    goto :end
+)
+
+echo Done! All files updated.
+pause
+:end
+deactivate
diff --git a/tiktok_feed.html b/tiktok_feed.html
new file mode 100644
index 0000000..fccb3fa
--- /dev/null
+++ b/tiktok_feed.html
@@ -0,0 +1,1304 @@
+<!DOCTYPE html>
+<html lang="en">
+<head>
+  <meta charset="UTF-8" />
+  <meta name="viewport" content="width=device-width, initial-scale=1.0, user-scalable=no"/>
+  <title>Research Feed • November 05, 2025</title>
+  <style>
+    * {
+      box-sizing: border-box;
+      margin: 0;
+      padding: 0;
+    }
+
+    :root {
+      --bg: #000000;
+      --text: #ffffff;
+      --muted: #a0a0a0;
+      --card-bg: #1a1a1a;
+      --border: #2a2a2a;
+      --accent: #ff6b6b;
+      --heart-red: #ff4458;
+      --layman-bg: #1f2937;
+      --layman-border: #60a5fa;
+    }
+
+    body {
+      font-family: -apple-system, BlinkMacSystemFont, 'Segoe UI', Roboto, sans-serif;
+      background: var(--bg);
+      color: var(--text);
+      overflow-x: hidden;
+      -webkit-font-smoothing: antialiased;
+    }
+
+    #feed-container {
+      height: 100vh;
+      overflow-y: scroll;
+      scroll-snap-type: y mandatory;
+      -webkit-overflow-scrolling: touch;
+      padding-top: 60px; /* Space for fixed header */
+    }
+
+    .paper-card {
+      min-height: 100vh;
+      scroll-snap-align: start;
+      scroll-snap-stop: always;
+      display: flex;
+      flex-direction: column;
+      justify-content: center;
+      padding: 2rem 1.5rem;
+      position: relative;
+      border-bottom: 1px solid var(--border);
+    }
+
+    .interest-badge {
+      display: inline-block;
+      background: var(--accent);
+      color: white;
+      padding: 0.4rem 0.9rem;
+      border-radius: 20px;
+      font-size: 0.7rem;
+      font-weight: 700;
+      text-transform: uppercase;
+      letter-spacing: 0.5px;
+      margin-bottom: 1rem;
+    }
+
+    .difficulty-badge {
+      display: inline-block;
+      padding: 0.3rem 0.7rem;
+      border-radius: 15px;
+      font-size: 0.7rem;
+      font-weight: 600;
+      margin-left: 0.5rem;
+    }
+
+    .paper-title {
+      font-size: 1.5rem;
+      font-weight: 800;
+      line-height: 1.3;
+      margin-bottom: 1rem;
+      color: var(--text);
+    }
+
+    .layman-box {
+      background: var(--layman-bg);
+      border-left: 3px solid var(--layman-border);
+      padding: 1rem;
+      margin-bottom: 1rem;
+      border-radius: 8px;
+      font-size: 0.95rem;
+      line-height: 1.6;
+      color: #94a3b8;
+      font-style: italic;
+    }
+
+    .summary {
+      color: var(--muted);
+      font-size: 0.95rem;
+      line-height: 1.6;
+      margin-bottom: 1.5rem;
+    }
+
+    .paper-meta {
+      display: flex;
+      justify-content: space-between;
+      align-items: center;
+      margin-bottom: 1rem;
+      color: var(--muted);
+      font-size: 0.8rem;
+    }
+
+    .category-tag {
+      background: #1e3a5f;
+      color: #60a5fa;
+      padding: 0.3rem 0.7rem;
+      border-radius: 15px;
+      font-size: 0.75rem;
+      font-weight: 600;
+    }
+
+    .links {
+      display: flex;
+      gap: 1.5rem;
+      margin-bottom: 2rem;
+    }
+
+    .links a {
+      color: #6ba3ff;
+      text-decoration: none;
+      font-size: 0.9rem;
+      font-weight: 600;
+      display: flex;
+      align-items: center;
+      gap: 0.3rem;
+    }
+
+    .links a:active {
+      color: var(--accent);
+    }
+
+    /* Fixed header with export button */
+    .fixed-header {
+      position: fixed;
+      top: 0;
+      left: 0;
+      right: 0;
+      background: rgba(0, 0, 0, 0.95);
+      backdrop-filter: blur(10px);
+      padding: 0.8rem 1.5rem;
+      display: flex;
+      align-items: center;
+      justify-content: space-between;
+      border-bottom: 1px solid var(--border);
+      z-index: 200;
+    }
+
+    .like-counter {
+      display: flex;
+      align-items: center;
+      gap: 0.5rem;
+      color: var(--muted);
+      font-size: 0.9rem;
+    }
+
+    .export-button {
+      background: linear-gradient(135deg, var(--accent), #ffa94d);
+      color: white;
+      border: none;
+      padding: 0.6rem 1.2rem;
+      border-radius: 20px;
+      font-weight: 700;
+      font-size: 0.85rem;
+      cursor: pointer;
+      transition: all 0.2s ease;
+      -webkit-tap-highlight-color: transparent;
+      opacity: 0.5;
+      pointer-events: none;
+    }
+
+    .export-button.active {
+      opacity: 1;
+      pointer-events: auto;
+    }
+
+    .export-button.active:active {
+      transform: scale(0.95);
+    }
+
+    .like-button {
+      position: fixed;
+      bottom: 2rem;
+      right: 1.5rem;
+      width: 60px;
+      height: 60px;
+      border-radius: 50%;
+      background: rgba(26, 26, 26, 0.9);
+      border: 2px solid var(--border);
+      display: flex;
+      align-items: center;
+      justify-content: center;
+      font-size: 1.8rem;
+      cursor: pointer;
+      transition: all 0.2s ease;
+      z-index: 100;
+      -webkit-tap-highlight-color: transparent;
+    }
+
+    .like-button:active {
+      transform: scale(0.9);
+    }
+
+    .like-button.liked {
+      background: var(--heart-red);
+      border-color: var(--heart-red);
+      animation: heartbeat 0.3s ease;
+    }
+
+    @keyframes heartbeat {
+      0%, 100% { transform: scale(1); }
+      50% { transform: scale(1.2); }
+    }
+
+    .scroll-indicator {
+      position: fixed;
+      bottom: 1rem;
+      left: 50%;
+      transform: translateX(-50%);
+      color: var(--muted);
+      font-size: 0.8rem;
+      animation: bounce 2s infinite;
+      z-index: 50;
+    }
+
+    @keyframes bounce {
+      0%, 100% { transform: translateX(-50%) translateY(0); }
+      50% { transform: translateX(-50%) translateY(-10px); }
+    }
+
+    .hide-indicator {
+      display: none;
+    }
+  </style>
+</head>
+<body>
+  <!-- Fixed Header with Export Button -->
+  <div class="fixed-header">
+    <div class="like-counter">
+      <span>♥</span>
+      <span><span id="likeCount">0</span> liked</span>
+    </div>
+    <button class="export-button" id="exportButton">Export Likes</button>
+  </div>
+
+  <div id="feed-container"></div>
+
+  <div class="like-button" id="likeButton">
+    <span id="heartIcon">♡</span>
+  </div>
+
+  <div class="scroll-indicator" id="scrollIndicator">
+    ↓ Scroll to explore
+  </div>
+
+  <script>
+    // ============================================
+    // EMBEDDED PAPERS DATA
+    // ============================================
+    const papers = [
+  {
+    "title": "Nesterov-Accelerated Robust Federated Learning Over Byzantine Adversaries",
+    "summary": " We investigate robust federated learning, where a group of workers collaboratively train a shared model under the orchestration of a central server in the presence of Byzantine adversaries . We propose a Byzantine-resilient Nesterov-Accelerated Federated Learning (Byrd-NAFL) algorithm .",
+    "link": "http://arxiv.org/abs/2511.02657v1",
+    "pdf_link": "https://arxiv.org/pdf/2511.02657.pdf",
+    "arxiv_id": "2511.02657",
+    "category": "cs.LG",
+    "published": "2025-11-04",
+    "relevance_score": 3,
+    "matched_keywords": [
+      "accelerat"
+    ],
+    "difficulty": "🟢 Applied",
+    "layman": "This research enhances privacy-preserving AI.",
+    "interest_category": "Efficient ML / Edge AI"
+  },
+  {
+    "title": "DANIEL: A Distributed and Scalable Approach for Global Representation Learning with EHR Applications",
+    "summary": " Classical probabilistic graphical models face challenges in modern data environments . We revisit the Ising model and develop a distributed framework that enables scalable representation learning from large-scale binary data . We evaluate our algorithm on multi-institutional electronic health record (EHR) datasets from 58,248 patients .",
+    "link": "http://arxiv.org/abs/2511.02754v1",
+    "pdf_link": "https://arxiv.org/pdf/2511.02754.pdf",
+    "arxiv_id": "2511.02754",
+    "category": "stat.ME",
+    "published": "2025-11-04",
+    "relevance_score": 2,
+    "matched_keywords": [
+      "privacy",
+      "federated"
+    ],
+    "difficulty": "🟢 Applied",
+    "layman": "This research explores techniques in privacy-preserving AI.",
+    "interest_category": "Privacy-Preserving ML"
+  },
+  {
+    "title": "Adaptive Neighborhood-Constrained Q Learning for Offline Reinforcement Learning",
+    "summary": " Offline reinforcement learning (RL) suffers from extrapolation errors induced by out-of-distribution (OOD) actions . To overcome these limitations, we propose a new neighborhood constraint that restricts action selection in the Bellman target to the union of neighborhoods of dataset actions . Theoretically, the constraint bounds extrapolation error and distribution shift under certain conditions, but also approximates the support constraint without requiring behavior policy modeling .",
+    "link": "http://arxiv.org/abs/2511.02567v1",
+    "pdf_link": "https://arxiv.org/pdf/2511.02567.pdf",
+    "arxiv_id": "2511.02567",
+    "category": "cs.LG",
+    "published": "2025-11-04",
+    "relevance_score": 2,
+    "matched_keywords": [
+      "art",
+      "design"
+    ],
+    "difficulty": "🟢 Applied",
+    "layman": "This research tackles the problem of language AI.",
+    "interest_category": "Creative AI / Emotion"
+  },
+  {
+    "title": "EPARA: Parallelizing Categorized AI Inference in Edge Clouds",
+    "summary": " EPARA is an end-to-end AI parallel inference framework aimed at enhancing the edge AI serving capability . EPARA consists of a task-categorized parallelism allocator that decides the parallel mode of each task, a distributed request handler that performs the calculation for the specific request, and a state-aware scheduler that periodically updates service placement in edge clouds .",
+    "link": "http://arxiv.org/abs/2511.00603v1",
+    "pdf_link": "https://arxiv.org/pdf/2511.00603.pdf",
+    "arxiv_id": "2511.00603",
+    "category": "cs.DC",
+    "published": "2025-11-01",
+    "relevance_score": 8,
+    "matched_keywords": [
+      "embedded",
+      "edge",
+      "resource",
+      "device"
+    ],
+    "difficulty": "🟢 Applied",
+    "layman": "This research enhances language AI.",
+    "interest_category": "Lightweight Systems"
+  },
+  {
+    "title": "Forecasting Future Anatomies: Longitudianl Brain Mri-to-Mri Prediction",
+    "summary": " Neuroimaging has important implications for studying neurodegenerative diseases such as Alzheimer's disease . Most existing approaches predict future cognitive scores or clinical outcomes . Here we investigate longitudinal MRI image-to-image prediction that forecasts a participant's entire brain MRI several years into the future .",
+    "link": "http://arxiv.org/abs/2511.02558v1",
+    "pdf_link": "https://arxiv.org/pdf/2511.02558.pdf",
+    "arxiv_id": "2511.02558",
+    "category": "cs.CV",
+    "published": "2025-11-04",
+    "relevance_score": 1,
+    "matched_keywords": [
+      "local"
+    ],
+    "difficulty": "🟢 Applied",
+    "layman": "This research creating new content with computer vision.",
+    "interest_category": "Offline-First / Local AI"
+  },
+  {
+    "title": "Curriculum Design for Trajectory-Constrained Agent: Compressing Chain-of-Thought Tokens in LLMs",
+    "summary": " Training agents to operate under strict constraints during deployment presents significant challenges . In this work, we propose a curriculum learning strategy that gradually tightens constraints during training, enabling the agent to incrementally master the deployment requirements . We provide theoretical analysis using an RL agent in a binary-tree Markov Decision Process .",
+    "link": "http://arxiv.org/abs/2511.02690v1",
+    "pdf_link": "https://arxiv.org/pdf/2511.02690.pdf",
+    "arxiv_id": "2511.02690",
+    "category": "cs.LG",
+    "published": "2025-11-04",
+    "relevance_score": 4,
+    "matched_keywords": [
+      "compression",
+      "inference",
+      "accelerat"
+    ],
+    "difficulty": "🟢 Applied",
+    "layman": "This research proposes a method for language AI.",
+    "interest_category": "Efficient ML / Edge AI"
+  },
+  {
+    "title": "Orion-MSP: Multi-Scale Sparse Attention for Tabular In-Context Learning",
+    "summary": " Tabular data remain the predominant format for real-world applications . Developing effective neural models for tabular data remains challenging due to heterogeneous feature types and complex interactions occurring at multiple scales . Orion-MSP is a tabular ICL architecture featuring three key innovations: multi-scale processing to capture hierarchical feature interactions .",
+    "link": "http://arxiv.org/abs/2511.02818v1",
+    "pdf_link": "https://arxiv.org/pdf/2511.02818.pdf",
+    "arxiv_id": "2511.02818",
+    "category": "cs.AI",
+    "published": "2025-11-04",
+    "relevance_score": 0,
+    "matched_keywords": [],
+    "difficulty": "🟢 Applied",
+    "layman": "This research explores techniques in machine learning.",
+    "interest_category": "Privacy-Preserving ML"
+  },
+  {
+    "title": "STAR-VAE: Latent Variable Transformers for Scalable and Controllable Molecular Generation",
+    "summary": " STAR-VAE (Selfies-encoded, Transformer-based, AutoRegressive Variational Auto Encoder) is a scalable latent-variable framework . It is trained on 79 million drug-like molecules from PubChem, using SELFIES to guarantee syntactic validity .",
+    "link": "http://arxiv.org/abs/2511.02769v1",
+    "pdf_link": "https://arxiv.org/pdf/2511.02769.pdf",
+    "arxiv_id": "2511.02769",
+    "category": "cs.LG",
+    "published": "2025-11-04",
+    "relevance_score": 2,
+    "matched_keywords": [
+      "generative",
+      "art"
+    ],
+    "difficulty": "🟢 Applied",
+    "layman": "This research presents techniques for machine learning.",
+    "interest_category": "Creative AI / Emotion"
+  },
+  {
+    "title": "Real-time Continual Learning on Intel Loihi 2",
+    "summary": " AI systems on edge devices face a critical challenge in open-world environments . Online continual learning (OCL) remains challenging in power-constrained settings . We present a neuromorphic solution called CLP-SNN: a spiking neural network architecture for Continually Learning Prototypes .",
+    "link": "http://arxiv.org/abs/2511.01553v1",
+    "pdf_link": "https://arxiv.org/pdf/2511.01553.pdf",
+    "arxiv_id": "2511.01553",
+    "category": "cs.LG",
+    "published": "2025-11-03",
+    "relevance_score": 6,
+    "matched_keywords": [
+      "iot",
+      "edge",
+      "constrained",
+      "device"
+    ],
+    "difficulty": "🟢 Applied",
+    "layman": "This research introduces a new approach to edge computing.",
+    "interest_category": "Lightweight Systems"
+  },
+  {
+    "title": "In Situ Training of Implicit Neural Compressors for Scientific Simulations via Sketch-Based Regularization",
+    "summary": " Focusing on implicit neural representations, we present a novel in situ training protocol that employs limited memory buffers of full and sketched data samples . We evaluate our method on a variety of complex simulation data in two and three dimensions .",
+    "link": "http://arxiv.org/abs/2511.02659v1",
+    "pdf_link": "https://arxiv.org/pdf/2511.02659.pdf",
+    "arxiv_id": "2511.02659",
+    "category": "cs.LG",
+    "published": "2025-11-04",
+    "relevance_score": 1,
+    "matched_keywords": [
+      "offline"
+    ],
+    "difficulty": "🟡 Advanced",
+    "layman": "This research introduces a new approach to machine learning.",
+    "interest_category": "Offline-First / Local AI"
+  },
+  {
+    "title": "Optimal Singular Damage: Efficient LLM Inference in Low Storage Regimes",
+    "summary": " Large language models (LLMs) are increasingly prevalent across diverse applications . However, their enormous size limits storage and processing capabilities to a few well-resourced stakeholders . This paper focuses on efficient storage of parameter updates in pre-trained models after fine-tuning .",
+    "link": "http://arxiv.org/abs/2511.02681v1",
+    "pdf_link": "https://arxiv.org/pdf/2511.02681.pdf",
+    "arxiv_id": "2511.02681",
+    "category": "cs.CL",
+    "published": "2025-11-04",
+    "relevance_score": 6,
+    "matched_keywords": [
+      "efficient",
+      "inference"
+    ],
+    "difficulty": "🟢 Applied",
+    "layman": "This research explores techniques in language AI.",
+    "interest_category": "Efficient ML / Edge AI"
+  },
+  {
+    "title": "Agent-Omni: Test-Time Multimodal Reasoning via Model Coordination for Understanding Anything",
+    "summary": " Multimodal large language models have shown strong capabilities but remain limited to fixed modality pairs . Building fully omni-capable models that can integrate text, images, audio, and video remains impractical and lacks robust reasoning support . We propose an Agent-Omni framework that coordinates existing foundation models through a master-agent system .",
+    "link": "http://arxiv.org/abs/2511.02834v1",
+    "pdf_link": "https://arxiv.org/pdf/2511.02834.pdf",
+    "arxiv_id": "2511.02834",
+    "category": "cs.AI",
+    "published": "2025-11-04",
+    "relevance_score": 0,
+    "matched_keywords": [],
+    "difficulty": "🟢 Applied",
+    "layman": "This research explores techniques in language AI.",
+    "interest_category": "Privacy-Preserving ML"
+  },
+  {
+    "title": "When One Modality Sabotages the Others: A Diagnostic Lens on Multimodal Reasoning",
+    "summary": " In this paper, we introduce modality sabotage, a diagnostic failure mode in which a high-confidence unimodal error overrides other evidence and misleads the fused result . Applying our diagnostic layer in a case study on multimodal emotion recognition benchmarks with foundation models revealed systematic reliability profiles .",
+    "link": "http://arxiv.org/abs/2511.02794v1",
+    "pdf_link": "https://arxiv.org/pdf/2511.02794.pdf",
+    "arxiv_id": "2511.02794",
+    "category": "cs.AI",
+    "published": "2025-11-04",
+    "relevance_score": 2,
+    "matched_keywords": [
+      "emotion",
+      "art"
+    ],
+    "difficulty": "🟢 Applied",
+    "layman": "This research forecasting language AI.",
+    "interest_category": "Creative AI / Emotion"
+  },
+  {
+    "title": "Boosting performance of computer vision applications through embedded GPUs on the edge",
+    "summary": " Edge computing can be used to offload certain high intensive tasks . Edge computing is usually composed of devices with limited capacity, which may impact in users quality of experience . This work proposes the use of embedded devices with graphics processing units (GPUs) to overcome such limitation .",
+    "link": "http://arxiv.org/abs/2511.01129v1",
+    "pdf_link": "https://arxiv.org/pdf/2511.01129.pdf",
+    "arxiv_id": "2511.01129",
+    "category": "cs.CV",
+    "published": "2025-11-03",
+    "relevance_score": 10,
+    "matched_keywords": [
+      "embedded",
+      "edge",
+      "resource",
+      "device"
+    ],
+    "difficulty": "🟢 Applied",
+    "layman": "This research presents techniques for computer vision.",
+    "interest_category": "Lightweight Systems"
+  },
+  {
+    "title": "Agentic AI for Mobile Network RAN Management and Optimization",
+    "summary": " Agentic AI represents a new paradigm for automating complex systems by using Large AI Models (LAMs) to provide human-level cognitive abilities with multimodal perception, planning, memory, and reasoning capabilities . The complexity of 5G and upcoming 6G networks renders manual optimization ineffective . Despite its rapid advances, there is no established framework outlining the foundational components and operational principles .",
+    "link": "http://arxiv.org/abs/2511.02532v1",
+    "pdf_link": "https://arxiv.org/pdf/2511.02532.pdf",
+    "arxiv_id": "2511.02532",
+    "category": "cs.AI",
+    "published": "2025-11-04",
+    "relevance_score": 3,
+    "matched_keywords": [
+      "mobile"
+    ],
+    "difficulty": "🟢 Applied",
+    "layman": "This research presents techniques for machine learning.",
+    "interest_category": "Offline-First / Local AI"
+  },
+  {
+    "title": "Accelerated Frank-Wolfe Algorithms: Complementarity Conditions and Sparsity",
+    "summary": " We develop new algorithms in Frank-Wolfe family for minimizing smooth convex functions over compact convex sets . A key technical ingredient is a complementarity condition that captures solution sparsity -- face dimension for polytopes and rank for matrices . Our results close a gap on how to accelerate recent advancements in linearly-converging FW algorithms .",
+    "link": "http://arxiv.org/abs/2511.02821v1",
+    "pdf_link": "https://arxiv.org/pdf/2511.02821.pdf",
+    "arxiv_id": "2511.02821",
+    "category": "math.OC",
+    "published": "2025-11-04",
+    "relevance_score": 3,
+    "matched_keywords": [
+      "accelerat"
+    ],
+    "difficulty": "🟡 Advanced",
+    "layman": "This research speeds up machine learning.",
+    "interest_category": "Efficient ML / Edge AI"
+  },
+  {
+    "title": "Bringing Private Reads to Hyperledger Fabric via Private Information Retrieval",
+    "summary": " The prototype achieves an average end-to-end latency of 113 ms and a peer-side execution time below 42 ms . Storage profiling across three channel configurations shows near-linear growth .",
+    "link": "http://arxiv.org/abs/2511.02656v1",
+    "pdf_link": "https://arxiv.org/pdf/2511.02656.pdf",
+    "arxiv_id": "2511.02656",
+    "category": "cs.CR",
+    "published": "2025-11-04",
+    "relevance_score": 6,
+    "matched_keywords": [
+      "privacy",
+      "encrypted",
+      "private"
+    ],
+    "difficulty": "🟢 Applied",
+    "layman": "This research proposes a method for privacy-preserving AI.",
+    "interest_category": "Privacy-Preserving ML"
+  },
+  {
+    "title": "Emotional Contagion in Code: How GitHub Emoji Reactions Shape Developer Collaboration",
+    "summary": " Developer communities increasingly rely on emoji reactions to communicate, but we know little about how these signals spread and influence technical discussions . We analyzed 2,098 GitHub issues and pull requests across 50 popular repositories to understand emotional contagion in software development . Our findings reveal a surprisingly positive emotional landscape: 57.4% of discussions carry positive sentiment .",
+    "link": "http://arxiv.org/abs/2511.02515v1",
+    "pdf_link": "https://arxiv.org/pdf/2511.02515.pdf",
+    "arxiv_id": "2511.02515",
+    "category": "cs.HC",
+    "published": "2025-11-04",
+    "relevance_score": 4,
+    "matched_keywords": [
+      "emotion",
+      "sentiment"
+    ],
+    "difficulty": "🟢 Applied",
+    "layman": "This research understanding emotions in emotion AI.",
+    "interest_category": "Creative AI / Emotion"
+  },
+  {
+    "title": "Neuro-Inspired Task Offloading in Edge-IoT Networks Using Spiking Neural Networks",
+    "summary": " Traditional task offloading strategies in edge computing often rely on static heuristics or data-intensive machine learning models . We propose a novel task-offloading framework based on Spiking Neural Networks inspired by the efficiency and adaptability of biological neural systems .",
+    "link": "http://arxiv.org/abs/2511.01127v1",
+    "pdf_link": "https://arxiv.org/pdf/2511.01127.pdf",
+    "arxiv_id": "2511.01127",
+    "category": "cs.DC",
+    "published": "2025-11-03",
+    "relevance_score": 10,
+    "matched_keywords": [
+      "iot",
+      "edge",
+      "resource",
+      "constrained"
+    ],
+    "difficulty": "🟢 Applied",
+    "layman": "This research introduces a new approach to edge computing.",
+    "interest_category": "Lightweight Systems"
+  },
+  {
+    "title": "Agentic World Modeling for 6G: Near-Real-Time Generative State-Space Reasoning",
+    "summary": " We reframe open radio access network (O-RAN) near-real-time (Near-RT) control via counterfactual dynamics and a world modeling (WM) paradigm . This enables quantitative \"what-if\" forecasting beyond large language models as the primary modeling primitive . Actions such as physical resource blocks are treated as first-class control inputs in a causal world model .",
+    "link": "http://arxiv.org/abs/2511.02748v1",
+    "pdf_link": "https://arxiv.org/pdf/2511.02748.pdf",
+    "arxiv_id": "2511.02748",
+    "category": "cs.NI",
+    "published": "2025-11-04",
+    "relevance_score": 1,
+    "matched_keywords": [
+      "offline"
+    ],
+    "difficulty": "🟢 Applied",
+    "layman": "This research forecasting language AI.",
+    "interest_category": "Offline-First / Local AI"
+  },
+  {
+    "title": "In Good GRACEs: Principled Teacher Selection for Knowledge Distillation",
+    "summary": " Knowledge distillation is an efficient strategy to use data generated by large \"teacher\" language models to train smaller capable \"student\" models . But selecting the optimal teacher for a specific student-task combination requires expensive trial-and-error . We propose a lightweight score called GRACE to quantify how effective a teacher will be for post-training a student model .",
+    "link": "http://arxiv.org/abs/2511.02833v1",
+    "pdf_link": "https://arxiv.org/pdf/2511.02833.pdf",
+    "arxiv_id": "2511.02833",
+    "category": "cs.LG",
+    "published": "2025-11-04",
+    "relevance_score": 10,
+    "matched_keywords": [
+      "efficient",
+      "edge",
+      "distillation",
+      "lightweight"
+    ],
+    "difficulty": "🟡 Advanced",
+    "layman": "This research makes more efficient language AI.",
+    "interest_category": "Efficient ML / Edge AI"
+  },
+  {
+    "title": "Fast, Private, and Protected: Safeguarding Data Privacy and Defending Against Model Poisoning Attacks in Federated Learning",
+    "summary": " Federated Learning (FL) is a distributed training paradigm wherein participants collaborate to build a global model while ensuring the privacy of the involved data . FPP employs a reputation-based mechanism to mitigate the participation of attackers .",
+    "link": "http://arxiv.org/abs/2511.02797v1",
+    "pdf_link": "https://arxiv.org/pdf/2511.02797.pdf",
+    "arxiv_id": "2511.02797",
+    "category": "cs.LG",
+    "published": "2025-11-04",
+    "relevance_score": 12,
+    "matched_keywords": [
+      "privacy",
+      "federated",
+      "secure",
+      "private"
+    ],
+    "difficulty": "🟢 Applied",
+    "layman": "This research protecting data privacy in privacy-preserving AI.",
+    "interest_category": "Privacy-Preserving ML"
+  },
+  {
+    "title": "Audience Amplified: Virtual Audiences in Asynchronously Performed AR Theater",
+    "summary": " Users can wander around naturally and engage in AR theater with virtual audiences trained from real audiences using imitation learning . Virtual dancers emerge from the surroundings, accompanied by a digitally simulated audience .",
+    "link": "http://arxiv.org/abs/2511.02807v1",
+    "pdf_link": "https://arxiv.org/pdf/2511.02807.pdf",
+    "arxiv_id": "2511.02807",
+    "category": "cs.HC",
+    "published": "2025-11-04",
+    "relevance_score": 4,
+    "matched_keywords": [
+      "sentiment",
+      "art",
+      "audio"
+    ],
+    "difficulty": "🟢 Applied",
+    "layman": "This research enhances speech processing.",
+    "interest_category": "Creative AI / Emotion"
+  },
+  {
+    "title": "Split Learning-Enabled Framework for Secure and Light-weight Internet of Medical Things Systems",
+    "summary": " Conventional deep learning methods are impractical due to resource limitations . Federated Learning (FL) suffers from high communication overhead and vulnerability to non-IID data . Split learning (SL) based framework for IoT malware detection through image-based classification .",
+    "link": "http://arxiv.org/abs/2511.00336v1",
+    "pdf_link": "https://arxiv.org/pdf/2511.00336.pdf",
+    "arxiv_id": "2511.00336",
+    "category": "cs.CR",
+    "published": "2025-11-01",
+    "relevance_score": 8,
+    "matched_keywords": [
+      "iot",
+      "edge",
+      "resource",
+      "constrained",
+      "device"
+    ],
+    "difficulty": "🟢 Applied",
+    "layman": "This research distributed machine learning across computer vision.",
+    "interest_category": "Lightweight Systems"
+  },
+  {
+    "title": "RL-Aided Cognitive ISAC: Robust Detection and Sensing-Communication Trade-offs",
+    "summary": " This paper proposes a reinforcement learning (RL)-aided cognitive framework for massive MIMO-based integrated sensing and communication (ISAC) systems . The focus is on enhancing radar sensing performance in environments with unknown and dynamic disturbance characteristics .",
+    "link": "http://arxiv.org/abs/2511.02672v1",
+    "pdf_link": "https://arxiv.org/pdf/2511.02672.pdf",
+    "arxiv_id": "2511.02672",
+    "category": "eess.SP",
+    "published": "2025-11-04",
+    "relevance_score": 1,
+    "matched_keywords": [
+      "edge"
+    ],
+    "difficulty": "🟢 Applied",
+    "layman": "This research enhances edge computing.",
+    "interest_category": "Offline-First / Local AI"
+  },
+  {
+    "title": "Apriel-H1: Towards Efficient Enterprise Reasoning Models",
+    "summary": " Hybrid LLMs combine transformer attention and SSM sequence mixers for efficient reasoning at 15B model size . High inference throughput is critical for agentic tasks, long-context reasoning, efficient deployment under high request loads, and more efficient test-time compute scaling .",
+    "link": "http://arxiv.org/abs/2511.02651v1",
+    "pdf_link": "https://arxiv.org/pdf/2511.02651.pdf",
+    "arxiv_id": "2511.02651",
+    "category": "cs.LG",
+    "published": "2025-11-04",
+    "relevance_score": 6,
+    "matched_keywords": [
+      "efficient",
+      "distillation",
+      "inference"
+    ],
+    "difficulty": "🟢 Applied",
+    "layman": "This research achieves better language AI.",
+    "interest_category": "Efficient ML / Edge AI"
+  },
+  {
+    "title": "Enhancing Federated Learning Privacy with QUBO",
+    "summary": " Federated learning (FL) is a widely used method for training machine learning (ML) models in a scalable way while preserving privacy . The risk of exposing sensitive data increases cumulatively as the number of iterations where a client's updates are included in the aggregated model increases . Attackers can launch membership inference attacks (MIA), property inference attacks, and model inversion attacks .",
+    "link": "http://arxiv.org/abs/2511.02785v1",
+    "pdf_link": "https://arxiv.org/pdf/2511.02785.pdf",
+    "arxiv_id": "2511.02785",
+    "category": "cs.LG",
+    "published": "2025-11-04",
+    "relevance_score": 6,
+    "matched_keywords": [
+      "privacy",
+      "federated"
+    ],
+    "difficulty": "🟢 Applied",
+    "layman": "This research protecting data privacy in privacy-preserving AI.",
+    "interest_category": "Privacy-Preserving ML"
+  },
+  {
+    "title": "SigmaCollab: An Application-Driven Dataset for Physically Situated Collaboration",
+    "summary": " We introduce SigmaCollab, a dataset enabling research on physically situated human-AI collaboration . The dataset consists of a set of 85 sessions in which untrained participants were guided by a mixed-reality assistive AI agent in performing procedural tasks in the physical world .",
+    "link": "http://arxiv.org/abs/2511.02560v1",
+    "pdf_link": "https://arxiv.org/pdf/2511.02560.pdf",
+    "arxiv_id": "2511.02560",
+    "category": "cs.HC",
+    "published": "2025-11-04",
+    "relevance_score": 2,
+    "matched_keywords": [
+      "art",
+      "audio"
+    ],
+    "difficulty": "🟢 Applied",
+    "layman": "This research explores techniques in speech processing.",
+    "interest_category": "Creative AI / Emotion"
+  },
+  {
+    "title": "Tetris: An SLA-aware Application Placement Strategy in the Edge-Cloud Continuum",
+    "summary": " An Edge-Cloud Continuum integrates edge and cloud resources to provide a flexible and scalable infrastructure . This paradigm can minimize latency by processing data closer to the source at the edge while leveraging the vast computational power of the cloud for more intensive tasks .",
+    "link": "http://arxiv.org/abs/2511.00294v1",
+    "pdf_link": "https://arxiv.org/pdf/2511.00294.pdf",
+    "arxiv_id": "2511.00294",
+    "category": "cs.DC",
+    "published": "2025-10-31",
+    "relevance_score": 4,
+    "matched_keywords": [
+      "edge",
+      "resource"
+    ],
+    "difficulty": "🟢 Applied",
+    "layman": "This research running AI locally on devices for edge computing.",
+    "interest_category": "Lightweight Systems"
+  },
+  {
+    "title": "VecComp: Vector Computing via MIMO Digital Over-the-Air Computation",
+    "summary": " ChannelComp framework has proposed digital over-the-air computation by designing digital modulations that enable the computation of arbitrary functions . This framework is intended for applications that favor local information processing over the mere acquisition of data .",
+    "link": "http://arxiv.org/abs/2511.02765v1",
+    "pdf_link": "https://arxiv.org/pdf/2511.02765.pdf",
+    "arxiv_id": "2511.02765",
+    "category": "cs.LG",
+    "published": "2025-11-04",
+    "relevance_score": 1,
+    "matched_keywords": [
+      "local"
+    ],
+    "difficulty": "🟢 Applied",
+    "layman": "This research proposes a method for machine learning.",
+    "interest_category": "Offline-First / Local AI"
+  },
+  {
+    "title": "Can Visual Input Be Compressed? A Visual Token Compression Benchmark for Large Multimodal Models",
+    "summary": " Large multimodal models (LMMs) often suffer from severe inference inefficiency due to the large number of visual tokens introduced by image encoders . Recent token compression methods, such as pruning and merging, have shown promise in reducing redundancy . In this work, we present UniPruneBench, a unified and extensible benchmark for visual token pruning .",
+    "link": "http://arxiv.org/abs/2511.02650v1",
+    "pdf_link": "https://arxiv.org/pdf/2511.02650.pdf",
+    "arxiv_id": "2511.02650",
+    "category": "cs.CV",
+    "published": "2025-11-04",
+    "relevance_score": 8,
+    "matched_keywords": [
+      "efficient",
+      "compression",
+      "pruning",
+      "inference"
+    ],
+    "difficulty": "🟢 Applied",
+    "layman": "This research reduces language AI.",
+    "interest_category": "Efficient ML / Edge AI"
+  },
+  {
+    "title": "GeoCrossBench: Cross-Band Generalization for Remote Sensing",
+    "summary": " GeoCrossBench is an extension of the popular GeoBench benchmark with a new evaluation protocol . It tests the in-distribution performance; generalization to satellites with no band overlap . ChiViT significantly outperforms the runner-up DINOv3 . Fine-tuning just the last linear layer of these models can get relatively consistent performance across all satellites .",
+    "link": "http://arxiv.org/abs/2511.02831v1",
+    "pdf_link": "https://arxiv.org/pdf/2511.02831.pdf",
+    "arxiv_id": "2511.02831",
+    "category": "cs.LG",
+    "published": "2025-11-04",
+    "relevance_score": 0,
+    "matched_keywords": [],
+    "difficulty": "🟢 Applied",
+    "layman": "This research explores techniques in machine learning.",
+    "interest_category": "Privacy-Preserving ML"
+  },
+  {
+    "title": "1 PoCo: Agentic Proof-of-Concept Exploit Generation for Smart Contracts",
+    "summary": " Smart contracts operate in a highly adversarial environment, where vulnerabilities can lead to substantial financial losses . POCO autonomously generates PoC exploits in an agentic manner by interacting with a set of code-execution tools in a Reason-Act-Observe loop .",
+    "link": "http://arxiv.org/abs/2511.02780v1",
+    "pdf_link": "https://arxiv.org/pdf/2511.02780.pdf",
+    "arxiv_id": "2511.02780",
+    "category": "cs.CR",
+    "published": "2025-11-04",
+    "relevance_score": 3,
+    "matched_keywords": [
+      "art"
+    ],
+    "difficulty": "🟢 Applied",
+    "layman": "This research explores techniques in edge computing.",
+    "interest_category": "Creative AI / Emotion"
+  },
+  {
+    "title": "Edge AI in Highly Volatile Environments: Is Fairness Worth the Accuracy Trade-off?",
+    "summary": " Federated learning (FL) has emerged as a transformative paradigm for edge intelligence, enabling collaborative model training while preserving data privacy across distributed personal devices . The inherent volatility of edge environments poses significant challenges for achieving high accuracy and fairness in client participation .",
+    "link": "http://arxiv.org/abs/2511.01737v1",
+    "pdf_link": "https://arxiv.org/pdf/2511.01737.pdf",
+    "arxiv_id": "2511.01737",
+    "category": "cs.LG",
+    "published": "2025-11-03",
+    "relevance_score": 6,
+    "matched_keywords": [
+      "edge",
+      "resource",
+      "device"
+    ],
+    "difficulty": "🟢 Applied",
+    "layman": "This research protecting data privacy in privacy-preserving AI.",
+    "interest_category": "Lightweight Systems"
+  },
+  {
+    "title": "Dynamic Priors in Bayesian Optimization for Hyperparameter Optimization",
+    "summary": " Hyperparameter optimization (HPO) based on Bayesian optimization (BO) supports users in designing models well-suited for a given dataset . HPO has proven its effectiveness on several applications, ranging from classical machine learning for tabular data to deep neural networks for computer vision and transformers for natural language processing .",
+    "link": "http://arxiv.org/abs/2511.02570v1",
+    "pdf_link": "https://arxiv.org/pdf/2511.02570.pdf",
+    "arxiv_id": "2511.02570",
+    "category": "cs.LG",
+    "published": "2025-11-04",
+    "relevance_score": 1,
+    "matched_keywords": [
+      "edge"
+    ],
+    "difficulty": "🟢 Applied",
+    "layman": "This research optimizes computer vision.",
+    "interest_category": "Offline-First / Local AI"
+  },
+  {
+    "title": "Federated Attention: A Distributed Paradigm for Collaborative LLM Inference over Edge Networks",
+    "summary": " Large language models (LLMs) are proliferating rapidly at the edge, delivering intelligent capabilities across diverse application scenarios . However, their practical deployment in collaborative scenarios confronts challenges: privacy vulnerabilities, communication overhead, and computational bottlenecks . To address these, we propose Federated Attention (FedAttn), which integrates the federated paradigm into the self-attention mechanism .",
+    "link": "http://arxiv.org/abs/2511.02647v1",
+    "pdf_link": "https://arxiv.org/pdf/2511.02647.pdf",
+    "arxiv_id": "2511.02647",
+    "category": "cs.DC",
+    "published": "2025-11-04",
+    "relevance_score": 6,
+    "matched_keywords": [
+      "edge",
+      "inference"
+    ],
+    "difficulty": "🟢 Applied",
+    "layman": "This research protecting data privacy in language AI.",
+    "interest_category": "Efficient ML / Edge AI"
+  },
+  {
+    "title": "Adam Reduces a Unique Form of Sharpness: Theoretical Insights Near the Minimizer Manifold",
+    "summary": " Adam implicitly reduces a unique form of sharpness measure shaped by its adaptive updates, leading to qualitatively different solutions from SGD . In solving sparse linear regression with diagonal linear networks, this distinction enables Adam to achieve better sparsity and generalization .",
+    "link": "http://arxiv.org/abs/2511.02773v1",
+    "pdf_link": "https://arxiv.org/pdf/2511.02773.pdf",
+    "arxiv_id": "2511.02773",
+    "category": "cs.LG",
+    "published": "2025-11-04",
+    "relevance_score": 1,
+    "matched_keywords": [
+      "differential"
+    ],
+    "difficulty": "🟡 Advanced",
+    "layman": "This research reduces machine learning.",
+    "interest_category": "Privacy-Preserving ML"
+  },
+  {
+    "title": "The Collaboration Gap",
+    "summary": " The trajectory of AI development suggests we will increasingly rely on agent-based systems composed of independently developed agents . Success of these systems will critically depend on effective collaboration among these heterogeneous agents . Few empirical studies have evaluated such agent-agent collaboration at scale . We propose a collaborative maze-solving benchmark that isolates collaborative capabilities .",
+    "link": "http://arxiv.org/abs/2511.02687v1",
+    "pdf_link": "https://arxiv.org/pdf/2511.02687.pdf",
+    "arxiv_id": "2511.02687",
+    "category": "cs.AI",
+    "published": "2025-11-04",
+    "relevance_score": 2,
+    "matched_keywords": [
+      "art",
+      "design"
+    ],
+    "difficulty": "🟢 Applied",
+    "layman": "This research explores techniques in machine learning.",
+    "interest_category": "Creative AI / Emotion"
+  },
+  {
+    "title": "Energy-Efficient Hardware Acceleration of Whisper ASR on a CGLA",
+    "summary": " Whisper's core computational kernel was evaluated on a general-purpose Coarse-Grained Linear Arrays (CGLAs) accelerator . Whisper is 1.90x more efficient than the NVIDIA Jetson AGX Orin .",
+    "link": "http://arxiv.org/abs/2511.02269v1",
+    "pdf_link": "https://arxiv.org/pdf/2511.02269.pdf",
+    "arxiv_id": "2511.02269",
+    "category": "cs.AR",
+    "published": "2025-11-04",
+    "relevance_score": 4,
+    "matched_keywords": [
+      "edge",
+      "constrained",
+      "device"
+    ],
+    "difficulty": "🟢 Applied",
+    "layman": "This research tackles the problem of speech processing.",
+    "interest_category": "Lightweight Systems"
+  },
+  {
+    "title": "Redundancy Maximization as a Principle of Associative Memory Learning",
+    "summary": " Associative memory, traditionally modeled by Hopfield networks, enables the retrieval of previously stored patterns from partial or noisy cues . We use redundancy as an information-theoretic learning goal, which is directly optimized for each neuron .",
+    "link": "http://arxiv.org/abs/2511.02584v1",
+    "pdf_link": "https://arxiv.org/pdf/2511.02584.pdf",
+    "arxiv_id": "2511.02584",
+    "category": "cs.IT",
+    "published": "2025-11-04",
+    "relevance_score": 1,
+    "matched_keywords": [
+      "local"
+    ],
+    "difficulty": "🟢 Applied",
+    "layman": "This research explores techniques in machine learning.",
+    "interest_category": "Offline-First / Local AI"
+  },
+  {
+    "title": "TabTune: A Unified Library for Inference and Fine-Tuning Tabular Foundation Models",
+    "summary": " TabTune provides consistent access to seven state-of-the-art models supporting multiple adaptation strategies . The framework automates model-aware preprocessing, manages architectural heterogeneity internally, and integrates evaluation modules for performance, calibration, and fairness . The library is open source and available at https://github.com/Lexsi-Labs/TabTune .",
+    "link": "http://arxiv.org/abs/2511.02802v1",
+    "pdf_link": "https://arxiv.org/pdf/2511.02802.pdf",
+    "arxiv_id": "2511.02802",
+    "category": "cs.LG",
+    "published": "2025-11-04",
+    "relevance_score": 4,
+    "matched_keywords": [
+      "efficient",
+      "inference"
+    ],
+    "difficulty": "🟢 Applied",
+    "layman": "This research presents techniques for machine learning.",
+    "interest_category": "Efficient ML / Edge AI"
+  },
+  {
+    "title": "Assessing win strength in MLB win prediction models",
+    "summary": " In Major League Baseball, strategy and planning are major factors in determining the outcome of a game . Previous studies have aided this by building machine learning models for predicting the winning team of any given game . We extend this work by training a comprehensive set of models using a common dataset .",
+    "link": "http://arxiv.org/abs/2511.02815v1",
+    "pdf_link": "https://arxiv.org/pdf/2511.02815.pdf",
+    "arxiv_id": "2511.02815",
+    "category": "cs.LG",
+    "published": "2025-11-04",
+    "relevance_score": 1,
+    "matched_keywords": [
+      "differential"
+    ],
+    "difficulty": "🟢 Applied",
+    "layman": "This research explores techniques in machine learning.",
+    "interest_category": "Privacy-Preserving ML"
+  },
+  {
+    "title": "Neurosymbolic Deep Learning Semantics",
+    "summary": " Artificial Intelligence (AI) is a powerful new language of science as evidenced by recent Nobel Prizes in chemistry and physics . Yet, this new language lacks semantics, which makes AI's scientific discoveries unsatisfactory at best . In this paper, we argue that logic offers an adequate framework to offer a much needed semantics for deep learning .",
+    "link": "http://arxiv.org/abs/2511.02825v1",
+    "pdf_link": "https://arxiv.org/pdf/2511.02825.pdf",
+    "arxiv_id": "2511.02825",
+    "category": "cs.AI",
+    "published": "2025-11-04",
+    "relevance_score": 2,
+    "matched_keywords": [
+      "art",
+      "design"
+    ],
+    "difficulty": "🟢 Applied",
+    "layman": "This research explores techniques in edge computing.",
+    "interest_category": "Creative AI / Emotion"
+  },
+  {
+    "title": "3D Point Cloud Object Detection on Edge Devices for Split Computing",
+    "summary": " Split Computing aims to lessen the computational burden on edge devices, thereby reducing processing time and power consumption . Split Computing reduces the inference time by 70.8% and the edge device execution time by 90.0% .",
+    "link": "http://arxiv.org/abs/2511.02293v1",
+    "pdf_link": "https://arxiv.org/pdf/2511.02293.pdf",
+    "arxiv_id": "2511.02293",
+    "category": "cs.DC",
+    "published": "2025-11-04",
+    "relevance_score": 6,
+    "matched_keywords": [
+      "edge",
+      "device"
+    ],
+    "difficulty": "🟢 Applied",
+    "layman": "This research automatically finding edge computing.",
+    "interest_category": "Lightweight Systems"
+  },
+  {
+    "title": "From Solo to Symphony: Orchestrating Multi-Agent Collaboration with Single-Agent Demos",
+    "summary": " Solo-to-Collaborative RL (SoCo) is a framework that transfers solo knowledge into cooperative learning . SoCo pretrains a shared solo policy from solo demonstrations, then adapts it for cooperation during multi-agent training . It significantly boosts training efficiency and performance of backbone algorithms .",
+    "link": "http://arxiv.org/abs/2511.02762v1",
+    "pdf_link": "https://arxiv.org/pdf/2511.02762.pdf",
+    "arxiv_id": "2511.02762",
+    "category": "cs.LG",
+    "published": "2025-11-04",
+    "relevance_score": 2,
+    "matched_keywords": [
+      "offline",
+      "edge"
+    ],
+    "difficulty": "🟢 Applied",
+    "layman": "This research makes more efficient edge computing.",
+    "interest_category": "Offline-First / Local AI"
+  },
+  {
+    "title": "Does Interpretability of Knowledge Tracing Models Support Teacher Decision Making?",
+    "summary": " Knowledge tracing (KT) models are a crucial basis for pedagogical decision-making . No study to date has investigated whether the interpretability of KT models actually helps human teachers to make teaching decisions .",
+    "link": "http://arxiv.org/abs/2511.02718v1",
+    "pdf_link": "https://arxiv.org/pdf/2511.02718.pdf",
+    "arxiv_id": "2511.02718",
+    "category": "cs.LG",
+    "published": "2025-11-04",
+    "relevance_score": 3,
+    "matched_keywords": [
+      "edge"
+    ],
+    "difficulty": "🟢 Applied",
+    "layman": "This research running AI locally on devices for edge computing.",
+    "interest_category": "Efficient ML / Edge AI"
+  },
+  {
+    "title": "TWIST2: Scalable, Portable, and Holistic Humanoid Data Collection System",
+    "summary": " TWIST2 is a portable, mocap-free humanoid teleoperation and data collection system . System leverages PICO4U VR for obtaining real-time whole-body human motions . System is fully reproducible and open-sourced at https://yanjieze.com/TWIST2 .",
+    "link": "http://arxiv.org/abs/2511.02832v1",
+    "pdf_link": "https://arxiv.org/pdf/2511.02832.pdf",
+    "arxiv_id": "2511.02832",
+    "category": "cs.RO",
+    "published": "2025-11-04",
+    "relevance_score": 0,
+    "matched_keywords": [],
+    "difficulty": "🟢 Applied",
+    "layman": "This research explores techniques in language AI.",
+    "interest_category": "Privacy-Preserving ML"
+  },
+  {
+    "title": "Perceived Femininity in Singing Voice: Analysis and Prediction",
+    "summary": " This paper focuses on the often-overlooked aspect of perceived voice femininity in singing voices . The analysis of gender bias in music content could benefit from such study .",
+    "link": "http://arxiv.org/abs/2511.02726v1",
+    "pdf_link": "https://arxiv.org/pdf/2511.02726.pdf",
+    "arxiv_id": "2511.02726",
+    "category": "cs.SD",
+    "published": "2025-11-04",
+    "relevance_score": 4,
+    "matched_keywords": [
+      "music",
+      "art",
+      "design"
+    ],
+    "difficulty": "🟢 Applied",
+    "layman": "This research explores techniques in speech processing.",
+    "interest_category": "Creative AI / Emotion"
+  },
+  {
+    "title": "FREESH: Fair, Resource- and Energy-Efficient Scheduling for LLM Serving on Heterogeneous GPUs",
+    "summary": " The ever-increasing computation and energy demand for LLM and AI agents call for holistic and efficient optimization of LLM serving systems . FREESH identifies optimal configurations of balanced load serving by matching distinct GPU instance's power-throughput characteristics with predictable LLM query length and workloads . During the 1-hour serving on production workloads, FREESH reduces energy by 28.6% and emissions by 45.45% .",
+    "link": "http://arxiv.org/abs/2511.00807v1",
+    "pdf_link": "https://arxiv.org/pdf/2511.00807.pdf",
+    "arxiv_id": "2511.00807",
+    "category": "cs.DC",
+    "published": "2025-11-02",
+    "relevance_score": 4,
+    "matched_keywords": [
+      "iot",
+      "resource"
+    ],
+    "difficulty": "🟢 Applied",
+    "layman": "This research optimizes language AI.",
+    "interest_category": "Lightweight Systems"
+  },
+  {
+    "title": "A Non-Adversarial Approach to Idempotent Generative Modelling",
+    "summary": " Idempotent Generative Networks (IGNs) are deep generative models that also function as local data manifold projectors, mapping arbitrary inputs back onto the manifold . IGNs suffer from mode collapse, mode dropping, and training instability due to their objectives .",
+    "link": "http://arxiv.org/abs/2511.02614v1",
+    "pdf_link": "https://arxiv.org/pdf/2511.02614.pdf",
+    "arxiv_id": "2511.02614",
+    "category": "cs.LG",
+    "published": "2025-11-04",
+    "relevance_score": 1,
+    "matched_keywords": [
+      "local"
+    ],
+    "difficulty": "🟢 Applied",
+    "layman": "This research creating new content with machine learning.",
+    "interest_category": "Offline-First / Local AI"
+  }
+];
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+    // ============================================
+    document.getElementById('likeButton').addEventListener('click', toggleLike);
+    document.getElementById('exportButton').addEventListener('click', exportLikes);
+
+    // Update like button when scrolling
+    document.getElementById('feed-container').addEventListener('scroll', () => {
+      updateLikeButton();
+
+      // Hide scroll indicator after first scroll
+      const scrollIndicator = document.getElementById('scrollIndicator');
+      if (document.getElementById('feed-container').scrollTop > 50) {
+        scrollIndicator.classList.add('hide-indicator');
+      }
+    });
+
+    // ============================================
+    // INITIALIZATION
+    // ============================================
+    renderFeed();
+    updateLikeButton();
+    updateCounter();
+    updateExportButton();
+  </script>
+</body>
+</html>