From c90b48e3f8da9dd0f8d2ae82ddf977436bb0cfc3 Mon Sep 17 00:00:00 2001
From: YurenHao0426 <blackhao0426@gmail.com>
Date: Sun, 15 Feb 2026 18:19:50 +0000
Subject: Initial implementation of HAG (Hopfield-Augmented Generation)

Core Hopfield retrieval module with energy-based convergence guarantees,
memory bank, FAISS baseline retriever, evaluation metrics, and end-to-end
pipeline. All 45 tests passing on CPU with synthetic data.

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
---
 notebooks/demo.ipynb | 62 ++++++++++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 62 insertions(+)
 create mode 100644 notebooks/demo.ipynb

(limited to 'notebooks')

diff --git a/notebooks/demo.ipynb b/notebooks/demo.ipynb
new file mode 100644
index 0000000..caace5b
--- /dev/null
+++ b/notebooks/demo.ipynb
@@ -0,0 +1,62 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# HAG: Hopfield-Augmented Generation Demo\n",
+    "\n",
+    "This notebook demonstrates the core Hopfield retrieval mechanism with synthetic data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import torch\n",
+    "import torch.nn.functional as F\n",
+    "\n",
+    "from hag.config import HopfieldConfig\n",
+    "from hag.hopfield import HopfieldRetrieval\n",
+    "from hag.energy import compute_energy_curve, verify_monotonic_decrease, compute_attention_entropy"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Create synthetic memory bank and query\n",
+    "torch.manual_seed(42)\n",
+    "d, N = 64, 200\n",
+    "memory = F.normalize(torch.randn(d, N), dim=0)\n",
+    "query = F.normalize(torch.randn(1, d), dim=-1)\n",
+    "\n",
+    "# Run Hopfield retrieval with different beta values\n",
+    "for beta in [0.5, 1.0, 2.0, 5.0]:\n",
+    "    config = HopfieldConfig(beta=beta, max_iter=20, conv_threshold=1e-6)\n",
+    "    hopfield = HopfieldRetrieval(config)\n",
+    "    result = hopfield.retrieve(query, memory, return_energy=True)\n",
+    "    curve = compute_energy_curve(result)\n",
+    "    entropy = compute_attention_entropy(result.attention_weights)\n",
+    "    print(f'beta={beta}: steps={result.num_steps}, entropy={entropy:.4f}, monotonic={verify_monotonic_decrease(curve)}')"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "name": "python",
+   "version": "3.10.0"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
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