diff --git a/qdrant-landing/content/documentation/tutorials-search-engineering/multi-representation-search.md b/qdrant-landing/content/documentation/tutorials-search-engineering/multi-representation-search.md
index d0b7aa2fd..e8d0d1e73 100644
--- a/qdrant-landing/content/documentation/tutorials-search-engineering/multi-representation-search.md
+++ b/qdrant-landing/content/documentation/tutorials-search-engineering/multi-representation-search.md
@@ -28,27 +28,36 @@ Use Python <3.13. Not all dependencies support the newest Python versions yet.
 
 ## Dataset
 
-You'll work with a sample of arXiv papers from the [`gfissore/arxiv-abstracts-2021`](https://huggingface.co/datasets/gfissore/arxiv-abstracts-2021) Hugging Face dataset, filtered to machine-learning and computer-science categories. Each paper has a title, an abstract, and category tags, which gives you four natural representations once the abstract is split into chunks: title, full abstract as a summary, abstract sentences as chunks, and categories as tags.
+You'll work with 20 000 arXiv papers from the [`gfissore/arxiv-abstracts-2021`](https://huggingface.co/datasets/gfissore/arxiv-abstracts-2021) Hugging Face dataset, filtered to ML/CS categories and to papers from 2018 onward — earlier ML papers predate most of the topics queries care about. Each paper has a title, an abstract, and category tags, which gives you four natural representations once the abstract is split into chunks: title, full abstract as a summary, abstract sentences as chunks, and categories as tags.
 
 ```python
 from datasets import load_dataset
 
 ML_CATEGORIES = {"cs.LG", "cs.CV", "cs.CL", "cs.AI", "stat.ML"}
 
-dataset = load_dataset(
-    "gfissore/arxiv-abstracts-2021", split="train", streaming=True
-)
+# Non-streaming so HF caches the parquet locally; first run downloads ~2.5 GB, re-runs are instant.
+dataset = load_dataset("gfissore/arxiv-abstracts-2021", split="train")
+
 papers = []
-for row in dataset:
-    if len(papers) >= 2000:
-        break # 2000 ML/CS papers is enough for this tutorial
+# IDs are roughly chronological; iterate from the end to land on 2021/2020/2019 papers first.
+for i in range(len(dataset) - 1, -1, -1):
+    if len(papers) >= 20000:
+        break
+    row = dataset[i]
     if not row["abstract"] or not row["title"]:
         continue
-    cats = list(row["categories"])
+    # categories arrive as space-joined strings (e.g. ["cs.LG cs.CV"]); split each entry.
+    cats = [tok for entry in row["categories"] for tok in entry.split()]
     if not any(c in ML_CATEGORIES for c in cats):
-        continue # ML/CS papers only
+        continue
+    # Year lives in the YYMM prefix of new-format arXiv IDs ("2104.01234" -> 2021).
+    arxiv_id = row["id"]
+    if "/" in arxiv_id or "." not in arxiv_id:
+        continue  # skip pre-2007 IDs like "math/0506001"
+    if 2000 + int(arxiv_id[:2]) < 2018:
+        continue
     papers.append({
-        "arxiv_id": row["id"],
+        "arxiv_id": arxiv_id,
         "title": row["title"].strip(),
         "abstract": row["abstract"].strip(),
         "categories": cats,