BERTopic random results with random_state set when input order is changed

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I'm using BERTopic with UMAP and HDBScan. I set the random_state of `UMAP` to a specific number, but I still get wildly different results on `fit_transform` if my input array (the corpus) is in another order. For example, When I have a collection of descriptions of size ~350 and I switch the first and last element, then my amount of clusters go from 20 to 24. Is this normal? Which step in the BERTopic process creates this randomness?

Code in question where I create the model:

bi_encoder = SentenceTransformer("all-mpnet-base-v2")
embeddings = bi_encoder.encode(input_corpus, show_progress_bar=True)

umap_model = UMAP(n_neighbors=3, n_components=3, min_dist=0.1, metric="cosine", random_state=40)
hdbscan_model = HDBSCAN(prediction_data=True)
vectorizer_model = CountVectorizer(ngram_range=(1, 2), stop_words="english")

topic_model = BERTopic(
embedding_model=bi_encoder,
vectorizer_model=vectorizer_model,
hdbscan_model=hdbscan_model,
umap_model=umap_model,
verbose=True,
)

Versions:
```
bertopic==0.16.4
umap-learn==0.5.7
hdbscan==0.8.40
sentence-transformers==3.3.1
```

Start with the supplied BERTopic reproduction and compare results after swapping the first and last corpus elements. Isolate the stages named in the report—fit_transform, UMAP, and HDBSCAN—to determine where order sensitivity appears. Done means identifying the responsible stage and documenting whether the behavior is expected or what actionable correction is needed.

Written by the indexing model from the issue text.