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diff --git a/scripts/day3_demo_feedback.py b/scripts/day3_demo_feedback.py
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+#!/usr/bin/env python3
+"""
+Day 3 Demo: Feedback Loop Simulation (Reward + Gating).
+"""
+
+import sys
+import os
+import json
+import numpy as np
+import random
+
+# Add src to sys.path
+sys.path.append(os.path.join(os.path.dirname(__file__), "../src"))
+
+from personalization.config.settings import load_local_models_config
+from personalization.models.embedding.qwen3_8b import Qwen3Embedding8B
+from personalization.models.reranker.qwen3_reranker import Qwen3Reranker
+from personalization.retrieval.preference_store.schemas import MemoryCard, ChatTurn
+from personalization.user_model.tensor_store import UserTensorStore
+from personalization.retrieval.pipeline import retrieve_with_rerank
+from personalization.feedback.handlers import eval_step
+
+def main():
+    # Paths
+    cards_path = "data/corpora/memory_cards.jsonl"
+    embs_path = "data/corpora/memory_embeddings.npy"
+    item_proj_path = "data/corpora/item_projection.npz"
+    user_store_path = "data/users/user_store.npz"
+    oasst_path = "data/raw_datasets/oasst1_queries.jsonl" # Source of turns
+    
+    # 1. Load Data
+    print("Loading data stores...")
+    if not os.path.exists(cards_path) or not os.path.exists(embs_path):
+        print("Memory data missing.")
+        sys.exit(1)
+        
+    cards = []
+    with open(cards_path, "r") as f:
+        for line in f:
+            cards.append(MemoryCard.model_validate_json(line))
+            
+    memory_embeddings = np.load(embs_path)
+    
+    proj_data = np.load(item_proj_path)
+    item_vectors = proj_data["V"]
+    
+    # 2. Load Models
+    print("Loading models...")
+    cfg = load_local_models_config()
+    embedder = Qwen3Embedding8B.from_config(cfg)
+    reranker = Qwen3Reranker.from_config(cfg)
+    
+    user_store = UserTensorStore(k=256, path=user_store_path)
+    
+    # 3. Simulate a Session
+    # Since we don't have full sessions in 'oasst1_queries.jsonl' (it's flat queries),
+    # we'll mock a session or try to find one if we had full chat logs.
+    # For demo, let's construct a synthetic scenario.
+    
+    print("\n--- Synthetic Session Evaluation ---")
+    
+    # Scenario 1: Success
+    # User asks python, system gives good python answer, user asks follow up.
+    
+    user_id = "test_user_ok"
+    q_t = "How do I list files in a directory in Python?"
+    
+    # Mock retrieval results (relevant)
+    # Ideally we'd run retrieval, but let's assume we found a relevant card
+    # For demo, let's actually run retrieval
+    hits = retrieve_with_rerank(
+        user_id=user_id,
+        query=q_t,
+        embed_model=embedder,
+        reranker=reranker,
+        memory_cards=cards,
+        memory_embeddings=memory_embeddings,
+        user_store=user_store,
+        item_vectors=item_vectors,
+        topk_dense=64,
+        topk_rerank=3
+    )
+    
+    a_t = "You can use os.listdir() or pathlib.Path.iterdir(). Here is an example..."
+    q_t1 = "Great, can you show me the pathlib one?"
+    
+    print(f"\n[Scenario 1]")
+    print(f"Q_t: {q_t}")
+    print(f"A_t: {a_t}")
+    print(f"Q_t+1: {q_t1}")
+    print(f"Memories: {[m.note_text for m in hits]}")
+    
+    # Eval
+    e_q = embedder.encode([q_t], return_tensor=False)[0]
+    e_q1 = embedder.encode([q_t1], return_tensor=False)[0]
+    e_q = np.array(e_q)
+    e_q1 = np.array(e_q1)
+    
+    r_hat, g_hat = eval_step(q_t, a_t, q_t1, hits, e_q, e_q1)
+    print(f"-> Reward: {r_hat:.2f}")
+    print(f"-> Gating: {g_hat:.2f}")
+    
+    # Scenario 2: Failure (Complaint)
+    q_t = "Explain quantum entanglement."
+    a_t = "Quantum entanglement is a phenomenon where particles..."
+    q_t1 = "No, that's not what I meant. Explain it simply like I'm five."
+    
+    # Mock retrieval (irrelevant or empty?)
+    # Let's say we retrieved some python stuff again by mistake
+    
+    print(f"\n[Scenario 2]")
+    print(f"Q_t: {q_t}")
+    print(f"A_t: {a_t}")
+    print(f"Q_t+1: {q_t1}")
+    print(f"Memories: {[m.note_text for m in hits]} (Irrelevant)")
+    
+    e_q = embedder.encode([q_t], return_tensor=False)[0]
+    e_q1 = embedder.encode([q_t1], return_tensor=False)[0]
+    e_q = np.array(e_q)
+    e_q1 = np.array(e_q1)
+    
+    r_hat, g_hat = eval_step(q_t, a_t, q_t1, hits, e_q, e_q1)
+    print(f"-> Reward: {r_hat:.2f}")
+    print(f"-> Gating: {g_hat:.2f}")
+
+if __name__ == "__main__":
+    main()