diff --git a/README.md b/README.md
index 03acb96..93ac89b 100644
--- a/README.md
+++ b/README.md
@@ -527,7 +527,7 @@ deduplicated_records = semhash.self_deduplicate().selected
 
 ## Benchmarks
 
-SemHash is extremely fast and scales to large datasets with millions of records. We've benchmarked both text and image deduplication across a variety of datasets. For example, deduplicating text 1.8M records takes only ~83 seconds on CPU.
+SemHash is extremely fast and scales to large datasets with millions of records. We've benchmarked both text and image deduplication across a variety of datasets. For example, deduplicating text 1.8M records takes only ~74 seconds on CPU.
 
 For detailed benchmark results and analysis, see the [benchmarks directory](benchmarks/README.md).
 
diff --git a/benchmarks/README.md b/benchmarks/README.md
index 7ebf432..e8580ce 100644
--- a/benchmarks/README.md
+++ b/benchmarks/README.md
@@ -21,6 +21,7 @@ This directory contains the benchmarking code and results for SemHash. The bench
 ### Setup
 
 All text benchmarks were run with the following configuration:
+- **Device**: MacBook Pro (Apple M5, 48 GB RAM)
 - **CPU-only**: All benchmarks run on CPU (no GPU acceleration)
 - **ANN backend**: Default backend (USearch)
 - **Encoder**: Default encoder ([potion-base-8M](https://huggingface.co/minishlab/potion-base-8M))
@@ -35,23 +36,23 @@ This benchmark measures the performance of deduplicating within a single trainin
 
 | Dataset              |  Original Train Size |  Deduplicated Train Size |  % Removed |   Deduplication Time (s) |
 |----------------------|----------------------|--------------------------|------------|--------------------------|
-| bbc                  |                 1225 |                     1144 |       6.61 |                     0.57 |
-| senteval_cr          |                 3012 |                     2990 |       0.73 |                     0.14 |
-| tweet_sentiment_extraction |                27481 |                    26695 |       2.86 |                     1.77 |
-| emotion              |                16000 |                    15695 |       1.91 |                     0.77 |
-| amazon_counterfactual |                 5000 |                     4992 |       0.16 |                     0.33 |
-| ag_news              |               120000 |                   106921 |      10.90 |                     5.20 |
-| enron_spam           |                31716 |                    20540 |      35.24 |                     2.03 |
-| subj                 |                 8000 |                     7990 |       0.12 |                     0.63 |
-| sst5                 |                 8544 |                     8526 |       0.21 |                     0.58 |
-| 20_newgroups         |                11314 |                    10684 |       5.57 |                     0.73 |
-| hatespeech_offensive |                22783 |                    22090 |       3.04 |                     0.92 |
-| ade                  |                17637 |                    15718 |      10.88 |                     0.73 |
-| imdb                 |                25000 |                    24830 |       0.68 |                     1.76 |
-| massive_scenario     |                11514 |                     9366 |      18.66 |                     0.47 |
-| student              |               117519 |                    63856 |      45.66 |                     8.80 |
-| squad_v2             |               130319 |                   109698 |      15.82 |                     8.81 |
-| wikitext             |              1801350 |                   884645 |      50.89 |                    83.53 |
+| bbc                  |                 1225 |                     1148 |       6.29 |                     0.19 |
+| senteval_cr          |                 3012 |                     2992 |       0.66 |                     0.15 |
+| tweet_sentiment_extraction |                27481 |                    26775 |       2.57 |                     1.67 |
+| emotion              |                16000 |                    15739 |       1.63 |                     0.68 |
+| amazon_counterfactual |                 5000 |                     4992 |       0.16 |                     0.27 |
+| ag_news              |               120000 |                   107882 |      10.10 |                     5.75 |
+| enron_spam           |                31716 |                    21121 |      33.41 |                     1.63 |
+| subj                 |                 8000 |                     7990 |       0.12 |                     0.45 |
+| sst5                 |                 8544 |                     8526 |       0.21 |                     0.46 |
+| 20_newgroups         |                11314 |                    10717 |       5.28 |                     0.61 |
+| hatespeech_offensive |                22783 |                    22233 |       2.41 |                     0.96 |
+| ade                  |                17637 |                    15723 |      10.85 |                     0.74 |
+| imdb                 |                25000 |                    24847 |       0.61 |                     1.64 |
+| massive_scenario     |                11514 |                     9665 |      16.06 |                     0.45 |
+| student              |               117519 |                    69696 |      40.69 |                     8.28 |
+| squad_v2             |               130319 |                   110480 |      15.22 |                     9.66 |
+| wikitext             |              1801350 |                   900554 |      50.01 |                    74.31 |
 
 ### Train/Test Deduplication Benchmark
 
@@ -59,29 +60,29 @@ This benchmark measures the performance of deduplicating a test dataset against
 
 | Dataset              |   Train Size |    Test Size |   Deduplicated Test Size |  % Removed |   Deduplication Time (s) |
 |----------------------|--------------|--------------|--------------------------|------------|--------------------------|
-| bbc                  |         1225 |         1000 |                      870 |      13.00 |                     0.71 |
-| senteval_cr          |         3012 |          753 |                      750 |       0.40 |                     0.13 |
-| tweet_sentiment_extraction |        27481 |         3534 |                     3412 |       3.45 |                     1.53 |
-| emotion              |        16000 |         2000 |                     1926 |       3.70 |                     0.65 |
-| amazon_counterfactual |         5000 |         5000 |                     4990 |       0.20 |                     0.51 |
-| ag_news              |       120000 |         7600 |                     6198 |      18.45 |                     3.74 |
-| enron_spam           |        31716 |         2000 |                     1060 |      47.00 |                     1.94 |
-| subj                 |         8000 |         2000 |                     1999 |       0.05 |                     0.62 |
-| sst5                 |         8544 |         2210 |                     2205 |       0.23 |                     0.59 |
-| 20_newgroups         |        11314 |         7532 |                     7098 |       5.76 |                     2.25 |
-| hatespeech_offensive |        22783 |         2000 |                     1925 |       3.75 |                     0.77 |
-| ade                  |        17637 |         5879 |                     4952 |      15.77 |                     0.81 |
-| imdb                 |        25000 |        25000 |                    24795 |       0.82 |                     2.81 |
-| massive_scenario     |        11514 |         2974 |                     2190 |      26.36 |                     0.46 |
-| student              |       117519 |         5000 |                     2393 |      52.14 |                     3.78 |
-| squad_v2             |       130319 |        11873 |                    11863 |       0.08 |                     7.13 |
-| wikitext             |      1801350 |         4358 |                     2139 |      50.92 |                    40.32 |
+| bbc                  |         1225 |         1000 |                      874 |      12.60 |                     0.29 |
+| senteval_cr          |         3012 |          753 |                      750 |       0.40 |                     0.15 |
+| tweet_sentiment_extraction |        27481 |         3534 |                     3411 |       3.48 |                     1.48 |
+| emotion              |        16000 |         2000 |                     1926 |       3.70 |                     0.58 |
+| amazon_counterfactual |         5000 |         5000 |                     4990 |       0.20 |                     0.44 |
+| ag_news              |       120000 |         7600 |                     6201 |      18.41 |                     3.95 |
+| enron_spam           |        31716 |         2000 |                     1064 |      46.80 |                     1.56 |
+| subj                 |         8000 |         2000 |                     1999 |       0.05 |                     0.44 |
+| sst5                 |         8544 |         2210 |                     2205 |       0.23 |                     0.45 |
+| 20_newgroups         |        11314 |         7532 |                     7098 |       5.76 |                     1.51 |
+| hatespeech_offensive |        22783 |         2000 |                     1925 |       3.75 |                     0.78 |
+| ade                  |        17637 |         5879 |                     4953 |      15.75 |                     0.79 |
+| imdb                 |        25000 |        25000 |                    24797 |       0.81 |                     2.55 |
+| massive_scenario     |        11514 |         2974 |                     2188 |      26.43 |                     0.47 |
+| student              |       117519 |         5000 |                     2400 |      52.00 |                     4.36 |
+| squad_v2             |       130319 |        11873 |                    11863 |       0.08 |                     7.07 |
+| wikitext             |      1801350 |         4358 |                     2134 |      51.03 |                    46.29 |
 
 ### Key Findings
 
 SemHash is extremely fast and scales to large datasets with millions of records. Some notable findings include:
 
-- **Speed**: Deduplication is fast even for large datasets (e.g., 1.8M records in ~83 seconds)
+- **Speed**: Deduplication is fast even for large datasets (e.g., 1.8M records in ~74 seconds)
 - **Train/Test Leakage**: Several datasets show significant train/test overlap:
   - `enron_spam`: 47% of test data overlaps with training data
   - `student`: 52% of test data overlaps with training data
@@ -106,7 +107,7 @@ make benchmark-text
 ### Setup
 
 All image benchmarks were run with the following configuration:
-- **Device**: Apple Silicon GPU (MPS)
+- **Device**: MacBook Pro (Apple M5, 48 GB RAM), GPU via MPS
 - **ANN backend**: Default backend (USearch)
 - **Encoder**: MobileNetV3-Small ([mobilenetv3_small_100.lamb_in1k](https://huggingface.co/timm/mobilenetv3_small_100.lamb_in1k))
 - **Batch size**: 128 images per batch
@@ -120,8 +121,8 @@ This benchmark measures the performance of deduplicating within a single trainin
 
 | Dataset              |  Original Train Size |  Deduplicated Train Size |  % Removed |   Deduplication Time (s) |
 |----------------------|----------------------|--------------------------|------------|--------------------------|
-| cifar10              |                50000 |                    48274 |       3.45 |                    61.20 |
-| fashion_mnist        |                60000 |                    16714 |      72.14 |                    86.61 |
+| cifar10              |                50000 |                    48390 |       3.22 |                    38.05 |
+| fashion_mnist        |                60000 |                    23549 |      60.75 |                    42.70 |
 
 #### Train/Test Deduplication Benchmark
 
@@ -129,14 +130,14 @@ This benchmark measures the performance of deduplicating a test dataset against
 
 | Dataset              |   Train Size |    Test Size |   Deduplicated Test Size |  % Removed |   Deduplication Time (s) |
 |----------------------|--------------|--------------|--------------------------|------------|--------------------------|
-| cifar10              |        50000 |        10000 |                     9397 |       6.03 |                    67.43 |
-| fashion_mnist        |        60000 |        10000 |                     2052 |      79.48 |                    72.14 |
+| cifar10              |        50000 |        10000 |                     9397 |       6.03 |                    43.35 |
+| fashion_mnist        |        60000 |        10000 |                     2056 |      79.44 |                    46.89 |
 
 ### Key Findings
 
-- **Fashion-MNIST high deduplication**: Fashion-MNIST shows very high duplication rates (72% train, 79% test) due to the simple nature of the dataset (10 clothing categories with similar items)
-- **CIFAR-10 moderate deduplication**: CIFAR-10 shows lower duplication (3.45% train, 6.03% test) as it contains more diverse natural images
-- **Speed**: Image deduplication is fast even for large datasets (60k images in ~87 seconds on MPS); note that the actual deduplication step is quick, with most time spent on encoding images
+- **Fashion-MNIST high deduplication**: Fashion-MNIST shows very high duplication rates (61% train, 79% test) due to the simple nature of the dataset (10 clothing categories with similar items)
+- **CIFAR-10 moderate deduplication**: CIFAR-10 shows lower duplication (3.22% train, 6.03% test) as it contains more diverse natural images
+- **Speed**: Image deduplication is fast even for large datasets (60k images in ~43 seconds on MPS); note that the actual deduplication step is quick, with most time spent on encoding images
 
 ### Running Image Benchmarks
 
diff --git a/benchmarks/results/image_train_benchmark_results.json b/benchmarks/results/image_train_benchmark_results.json
index 53fa71b..171402c 100644
--- a/benchmarks/results/image_train_benchmark_results.json
+++ b/benchmarks/results/image_train_benchmark_results.json
@@ -2,19 +2,19 @@
   {
     "dataset": "cifar10",
     "original_train_size": 50000,
-    "deduplicated_train_size": 48274,
-    "percent_removed": 3.4519999999999995,
-    "build_time_seconds": 56.00128899999254,
-    "deduplication_time_seconds": 5.201297917010379,
-    "time_seconds": 61.20258691700292
+    "deduplicated_train_size": 48390,
+    "percent_removed": 3.2200000000000006,
+    "build_time_seconds": 36.14036558299995,
+    "deduplication_time_seconds": 1.9100148340003216,
+    "time_seconds": 38.05038041700027
   },
   {
     "dataset": "fashion_mnist",
     "original_train_size": 60000,
-    "deduplicated_train_size": 16714,
-    "percent_removed": 72.14333333333333,
-    "build_time_seconds": 61.14413262500602,
-    "deduplication_time_seconds": 25.46288070900482,
-    "time_seconds": 86.60701333401084
+    "deduplicated_train_size": 23549,
+    "percent_removed": 60.75166666666667,
+    "build_time_seconds": 38.96308354100256,
+    "deduplication_time_seconds": 3.733158041999559,
+    "time_seconds": 42.69624158300212
   }
 ]
diff --git a/benchmarks/results/image_train_test_benchmark_results.json b/benchmarks/results/image_train_test_benchmark_results.json
index 290c7e3..8932214 100644
--- a/benchmarks/results/image_train_test_benchmark_results.json
+++ b/benchmarks/results/image_train_test_benchmark_results.json
@@ -5,18 +5,18 @@
     "test_size": 10000,
     "deduplicated_test_size": 9397,
     "percent_removed": 6.030000000000002,
-    "build_time_seconds": 56.00128899999254,
-    "deduplication_time_seconds": 11.428115875009098,
-    "time_seconds": 67.42940487500164
+    "build_time_seconds": 36.14036558299995,
+    "deduplication_time_seconds": 7.210748125002283,
+    "time_seconds": 43.35111370800223
   },
   {
     "dataset": "fashion_mnist",
     "train_size": 60000,
     "test_size": 10000,
-    "deduplicated_test_size": 2052,
-    "percent_removed": 79.47999999999999,
-    "build_time_seconds": 61.14413262500602,
-    "deduplication_time_seconds": 10.998616750002839,
-    "time_seconds": 72.14274937500886
+    "deduplicated_test_size": 2056,
+    "percent_removed": 79.44,
+    "build_time_seconds": 38.96308354100256,
+    "deduplication_time_seconds": 7.9287375419989985,
+    "time_seconds": 46.89182108300156
   }
 ]
diff --git a/benchmarks/results/train_benchmark_results.json b/benchmarks/results/train_benchmark_results.json
index 01cc3f4..ae092db 100644
--- a/benchmarks/results/train_benchmark_results.json
+++ b/benchmarks/results/train_benchmark_results.json
@@ -2,154 +2,154 @@
   {
     "dataset": "bbc",
     "original_train_size": 1225,
-    "deduplicated_train_size": 1144,
-    "percent_removed": 6.612244897959185,
-    "build_time_seconds": 0.5598082079086453,
-    "deduplication_time_seconds": 0.008702374994754791,
-    "time_seconds": 0.5685105829034001
+    "deduplicated_train_size": 1148,
+    "percent_removed": 6.285714285714283,
+    "build_time_seconds": 0.1762163750026957,
+    "deduplication_time_seconds": 0.009695124997961102,
+    "time_seconds": 0.18591150000065682
   },
   {
     "dataset": "senteval_cr",
     "original_train_size": 3012,
-    "deduplicated_train_size": 2990,
-    "percent_removed": 0.7304116865869847,
-    "build_time_seconds": 0.10847400000784546,
-    "deduplication_time_seconds": 0.027519959025084972,
-    "time_seconds": 0.13599395903293043
+    "deduplicated_train_size": 2992,
+    "percent_removed": 0.6640106241699861,
+    "build_time_seconds": 0.12137100000109058,
+    "deduplication_time_seconds": 0.029432167000777554,
+    "time_seconds": 0.15080316700186813
   },
   {
     "dataset": "tweet_sentiment_extraction",
     "original_train_size": 27481,
-    "deduplicated_train_size": 26695,
-    "percent_removed": 2.860157927295226,
-    "build_time_seconds": 1.3568968329345807,
-    "deduplication_time_seconds": 0.41194633406121284,
-    "time_seconds": 1.7688431669957936
+    "deduplicated_train_size": 26775,
+    "percent_removed": 2.569047705687566,
+    "build_time_seconds": 1.3021473750013683,
+    "deduplication_time_seconds": 0.37088470900198445,
+    "time_seconds": 1.6730320840033528
   },
   {
     "dataset": "emotion",
     "original_train_size": 16000,
-    "deduplicated_train_size": 15695,
-    "percent_removed": 1.9062499999999982,
-    "build_time_seconds": 0.5511152499821037,
-    "deduplication_time_seconds": 0.21407662506680936,
-    "time_seconds": 0.7651918750489131
+    "deduplicated_train_size": 15739,
+    "percent_removed": 1.6312499999999952,
+    "build_time_seconds": 0.4828177079980378,
+    "deduplication_time_seconds": 0.19528612499925657,
+    "time_seconds": 0.6781038329972944
   },
   {
     "dataset": "amazon_counterfactual",
     "original_train_size": 5000,
     "deduplicated_train_size": 4992,
     "percent_removed": 0.16000000000000458,
-    "build_time_seconds": 0.2848535830853507,
-    "deduplication_time_seconds": 0.048574666026979685,
-    "time_seconds": 0.3334282491123304
+    "build_time_seconds": 0.22153624999918975,
+    "deduplication_time_seconds": 0.04794116700213635,
+    "time_seconds": 0.2694774170013261
   },
   {
     "dataset": "ag_news",
     "original_train_size": 120000,
-    "deduplicated_train_size": 106921,
-    "percent_removed": 10.899166666666671,
-    "build_time_seconds": 3.0319770000642166,
-    "deduplication_time_seconds": 2.171258582966402,
-    "time_seconds": 5.203235583030619
+    "deduplicated_train_size": 107882,
+    "percent_removed": 10.098333333333331,
+    "build_time_seconds": 3.275759290998394,
+    "deduplication_time_seconds": 2.4731626249995315,
+    "time_seconds": 5.748921915997926
   },
   {
     "dataset": "enron_spam",
     "original_train_size": 31716,
-    "deduplicated_train_size": 20540,
-    "percent_removed": 35.23773489721276,
-    "build_time_seconds": 1.3818323339801282,
-    "deduplication_time_seconds": 0.6438171250047162,
-    "time_seconds": 2.0256494589848444
+    "deduplicated_train_size": 21121,
+    "percent_removed": 33.405851935931395,
+    "build_time_seconds": 1.1878026249978575,
+    "deduplication_time_seconds": 0.44431537499986007,
+    "time_seconds": 1.6321179999977176
   },
   {
     "dataset": "subj",
     "original_train_size": 8000,
     "deduplicated_train_size": 7990,
     "percent_removed": 0.12499999999999734,
-    "build_time_seconds": 0.5059439589967951,
-    "deduplication_time_seconds": 0.12505983305163682,
-    "time_seconds": 0.6310037920484319
+    "build_time_seconds": 0.34191370800181176,
+    "deduplication_time_seconds": 0.10950004199912655,
+    "time_seconds": 0.4514137500009383
   },
   {
     "dataset": "sst5",
     "original_train_size": 8544,
     "deduplicated_train_size": 8526,
     "percent_removed": 0.2106741573033699,
-    "build_time_seconds": 0.4805819580797106,
-    "deduplication_time_seconds": 0.10166720801498741,
-    "time_seconds": 0.582249166094698
+    "build_time_seconds": 0.35040350000053877,
+    "deduplication_time_seconds": 0.10490237500198418,
+    "time_seconds": 0.45530587500252295
   },
   {
     "dataset": "20_newgroups",
     "original_train_size": 11314,
-    "deduplicated_train_size": 10684,
-    "percent_removed": 5.568322432384654,
-    "build_time_seconds": 0.610724583035335,
-    "deduplication_time_seconds": 0.11600329191423953,
-    "time_seconds": 0.7267278749495745
+    "deduplicated_train_size": 10717,
+    "percent_removed": 5.276648400212125,
+    "build_time_seconds": 0.49274829199930537,
+    "deduplication_time_seconds": 0.11272816700147814,
+    "time_seconds": 0.6054764590007835
   },
   {
     "dataset": "hatespeech_offensive",
     "original_train_size": 22783,
-    "deduplicated_train_size": 22090,
-    "percent_removed": 3.0417416494754823,
-    "build_time_seconds": 0.6471997499465942,
-    "deduplication_time_seconds": 0.2704670410603285,
-    "time_seconds": 0.9176667910069227
+    "deduplicated_train_size": 22233,
+    "percent_removed": 2.4140806741868914,
+    "build_time_seconds": 0.6664763750013663,
+    "deduplication_time_seconds": 0.2959712080009922,
+    "time_seconds": 0.9624475830023584
   },
   {
     "dataset": "ade",
     "original_train_size": 17637,
-    "deduplicated_train_size": 15718,
-    "percent_removed": 10.880535238419231,
-    "build_time_seconds": 0.5221591669833288,
-    "deduplication_time_seconds": 0.20764074998442084,
-    "time_seconds": 0.7297999169677496
+    "deduplicated_train_size": 15723,
+    "percent_removed": 10.852185745875154,
+    "build_time_seconds": 0.5355710839976382,
+    "deduplication_time_seconds": 0.2090475000004517,
+    "time_seconds": 0.7446185839980899
   },
   {
     "dataset": "imdb",
     "original_train_size": 25000,
-    "deduplicated_train_size": 24830,
-    "percent_removed": 0.6800000000000028,
-    "build_time_seconds": 1.460668999934569,
-    "deduplication_time_seconds": 0.29758112493436784,
-    "time_seconds": 1.7582501248689368
+    "deduplicated_train_size": 24847,
+    "percent_removed": 0.6120000000000014,
+    "build_time_seconds": 1.325603665998642,
+    "deduplication_time_seconds": 0.31292716700045276,
+    "time_seconds": 1.6385308329990949
   },
   {
     "dataset": "massive_scenario",
     "original_train_size": 11514,
-    "deduplicated_train_size": 9366,
-    "percent_removed": 18.655549765502865,
-    "build_time_seconds": 0.35503324994351715,
-    "deduplication_time_seconds": 0.11619104200508446,
-    "time_seconds": 0.4712242919486016
+    "deduplicated_train_size": 9665,
+    "percent_removed": 16.05871113427132,
+    "build_time_seconds": 0.3495884580006532,
+    "deduplication_time_seconds": 0.10384745799819939,
+    "time_seconds": 0.4534359159988526
   },
   {
     "dataset": "student",
     "original_train_size": 117519,
-    "deduplicated_train_size": 63856,
-    "percent_removed": 45.66325445247151,
-    "build_time_seconds": 2.9044899590080604,
-    "deduplication_time_seconds": 5.895973875070922,
-    "time_seconds": 8.800463834078982
+    "deduplicated_train_size": 69696,
+    "percent_removed": 40.69384525055523,
+    "build_time_seconds": 3.4031454579999263,
+    "deduplication_time_seconds": 4.874128916999325,
+    "time_seconds": 8.277274374999251
   },
   {
     "dataset": "squad_v2",
     "original_train_size": 130319,
-    "deduplicated_train_size": 109698,
-    "percent_removed": 15.823479308466148,
-    "build_time_seconds": 6.078755749971606,
-    "deduplication_time_seconds": 2.7270843340083957,
-    "time_seconds": 8.805840083980002
+    "deduplicated_train_size": 110480,
+    "percent_removed": 15.22341331655399,
+    "build_time_seconds": 5.937046999999438,
+    "deduplication_time_seconds": 3.722715916999732,
+    "time_seconds": 9.65976291699917
   },
   {
     "dataset": "wikitext",
     "original_train_size": 1801350,
-    "deduplicated_train_size": 884645,
-    "percent_removed": 50.88988813945097,
-    "build_time_seconds": 39.38258587510791,
-    "deduplication_time_seconds": 44.1503732081037,
-    "time_seconds": 83.53295908321161
+    "deduplicated_train_size": 900554,
+    "percent_removed": 50.00671718433397,
+    "build_time_seconds": 44.6954585420026,
+    "deduplication_time_seconds": 29.619445874999656,
+    "time_seconds": 74.31490441700225
   }
 ]
diff --git a/benchmarks/results/train_test_benchmark_results.json b/benchmarks/results/train_test_benchmark_results.json
index 0508700..80d1d58 100644
--- a/benchmarks/results/train_test_benchmark_results.json
+++ b/benchmarks/results/train_test_benchmark_results.json
@@ -3,11 +3,11 @@
     "dataset": "bbc",
     "train_size": 1225,
     "test_size": 1000,
-    "deduplicated_test_size": 870,
-    "percent_removed": 13.0,
-    "build_time_seconds": 0.5598082079086453,
-    "deduplication_time_seconds": 0.1528247919632122,
-    "time_seconds": 0.7126329998718575
+    "deduplicated_test_size": 874,
+    "percent_removed": 12.6,
+    "build_time_seconds": 0.1762163750026957,
+    "deduplication_time_seconds": 0.10950220800077659,
+    "time_seconds": 0.2857185830034723
   },
   {
     "dataset": "senteval_cr",
@@ -15,19 +15,19 @@
     "test_size": 753,
     "deduplicated_test_size": 750,
     "percent_removed": 0.3984063745019917,
-    "build_time_seconds": 0.10847400000784546,
-    "deduplication_time_seconds": 0.019297500024549663,
-    "time_seconds": 0.12777150003239512
+    "build_time_seconds": 0.12137100000109058,
+    "deduplication_time_seconds": 0.027684791002684506,
+    "time_seconds": 0.14905579100377508
   },
   {
     "dataset": "tweet_sentiment_extraction",
     "train_size": 27481,
     "test_size": 3534,
-    "deduplicated_test_size": 3412,
-    "percent_removed": 3.452178834182229,
-    "build_time_seconds": 1.3568968329345807,
-    "deduplication_time_seconds": 0.17268049996346235,
-    "time_seconds": 1.529577332898043
+    "deduplicated_test_size": 3411,
+    "percent_removed": 3.4804753820034007,
+    "build_time_seconds": 1.3021473750013683,
+    "deduplication_time_seconds": 0.1789753750017553,
+    "time_seconds": 1.4811227500031237
   },
   {
     "dataset": "emotion",
@@ -35,9 +35,9 @@
     "test_size": 2000,
     "deduplicated_test_size": 1926,
     "percent_removed": 3.7000000000000033,
-    "build_time_seconds": 0.5511152499821037,
-    "deduplication_time_seconds": 0.10135454102419317,
-    "time_seconds": 0.6524697910062969
+    "build_time_seconds": 0.4828177079980378,
+    "deduplication_time_seconds": 0.09223724999901606,
+    "time_seconds": 0.5750549579970539
   },
   {
     "dataset": "amazon_counterfactual",
@@ -45,29 +45,29 @@
     "test_size": 5000,
     "deduplicated_test_size": 4990,
     "percent_removed": 0.20000000000000018,
-    "build_time_seconds": 0.2848535830853507,
-    "deduplication_time_seconds": 0.22846354101784527,
-    "time_seconds": 0.513317124103196
+    "build_time_seconds": 0.22153624999918975,
+    "deduplication_time_seconds": 0.21453241600102047,
+    "time_seconds": 0.4360686660002102
   },
   {
     "dataset": "ag_news",
     "train_size": 120000,
     "test_size": 7600,
-    "deduplicated_test_size": 6198,
-    "percent_removed": 18.447368421052634,
-    "build_time_seconds": 3.0319770000642166,
-    "deduplication_time_seconds": 0.7034984159981832,
-    "time_seconds": 3.7354754160623997
+    "deduplicated_test_size": 6201,
+    "percent_removed": 18.407894736842103,
+    "build_time_seconds": 3.275759290998394,
+    "deduplication_time_seconds": 0.6717791250011942,
+    "time_seconds": 3.9475384159995883
   },
   {
     "dataset": "enron_spam",
     "train_size": 31716,
     "test_size": 2000,
-    "deduplicated_test_size": 1060,
-    "percent_removed": 47.0,
-    "build_time_seconds": 1.3818323339801282,
-    "deduplication_time_seconds": 0.553584959008731,
-    "time_seconds": 1.9354172929888591
+    "deduplicated_test_size": 1064,
+    "percent_removed": 46.8,
+    "build_time_seconds": 1.1878026249978575,
+    "deduplication_time_seconds": 0.37047674999848823,
+    "time_seconds": 1.5582793749963457
   },
   {
     "dataset": "subj",
@@ -75,9 +75,9 @@
     "test_size": 2000,
     "deduplicated_test_size": 1999,
     "percent_removed": 0.04999999999999449,
-    "build_time_seconds": 0.5059439589967951,
-    "deduplication_time_seconds": 0.11624520795885473,
-    "time_seconds": 0.6221891669556499
+    "build_time_seconds": 0.34191370800181176,
+    "deduplication_time_seconds": 0.09621087499908754,
+    "time_seconds": 0.4381245830008993
   },
   {
     "dataset": "sst5",
@@ -85,9 +85,9 @@
     "test_size": 2210,
     "deduplicated_test_size": 2205,
     "percent_removed": 0.2262443438914019,
-    "build_time_seconds": 0.4805819580797106,
-    "deduplication_time_seconds": 0.11375170899555087,
-    "time_seconds": 0.5943336670752615
+    "build_time_seconds": 0.35040350000053877,
+    "deduplication_time_seconds": 0.09645383300085086,
+    "time_seconds": 0.44685733300138963
   },
   {
     "dataset": "20_newgroups",
@@ -95,9 +95,9 @@
     "test_size": 7532,
     "deduplicated_test_size": 7098,
     "percent_removed": 5.762081784386619,
-    "build_time_seconds": 0.610724583035335,
-    "deduplication_time_seconds": 1.6346445409581065,
-    "time_seconds": 2.2453691239934415
+    "build_time_seconds": 0.49274829199930537,
+    "deduplication_time_seconds": 1.0146297089995642,
+    "time_seconds": 1.5073780009988695
   },
   {
     "dataset": "hatespeech_offensive",
@@ -105,49 +105,49 @@
     "test_size": 2000,
     "deduplicated_test_size": 1925,
     "percent_removed": 3.749999999999998,
-    "build_time_seconds": 0.6471997499465942,
-    "deduplication_time_seconds": 0.12372829194646329,
-    "time_seconds": 0.7709280418930575
+    "build_time_seconds": 0.6664763750013663,
+    "deduplication_time_seconds": 0.11403075000271201,
+    "time_seconds": 0.7805071250040783
   },
   {
     "dataset": "ade",
     "train_size": 17637,
     "test_size": 5879,
-    "deduplicated_test_size": 4952,
-    "percent_removed": 15.76798775301922,
-    "build_time_seconds": 0.5221591669833288,
-    "deduplication_time_seconds": 0.28758599993307143,
-    "time_seconds": 0.8097451669164002
+    "deduplicated_test_size": 4953,
+    "percent_removed": 15.75097805749277,
+    "build_time_seconds": 0.5355710839976382,
+    "deduplication_time_seconds": 0.2526170000019192,
+    "time_seconds": 0.7881880839995574
   },
   {
     "dataset": "imdb",
     "train_size": 25000,
     "test_size": 25000,
-    "deduplicated_test_size": 24795,
-    "percent_removed": 0.8199999999999985,
-    "build_time_seconds": 1.460668999934569,
-    "deduplication_time_seconds": 1.3489695829339325,
-    "time_seconds": 2.8096385828685015
+    "deduplicated_test_size": 24797,
+    "percent_removed": 0.8120000000000016,
+    "build_time_seconds": 1.325603665998642,
+    "deduplication_time_seconds": 1.2251233749993844,
+    "time_seconds": 2.5507270409980265
   },
   {
     "dataset": "massive_scenario",
     "train_size": 11514,
     "test_size": 2974,
-    "deduplicated_test_size": 2190,
-    "percent_removed": 26.36180228648285,
-    "build_time_seconds": 0.35503324994351715,
-    "deduplication_time_seconds": 0.10878237499855459,
-    "time_seconds": 0.46381562494207174
+    "deduplicated_test_size": 2188,
+    "percent_removed": 26.429051782111635,
+    "build_time_seconds": 0.3495884580006532,
+    "deduplication_time_seconds": 0.11969712500285823,
+    "time_seconds": 0.4692855830035114
   },
   {
     "dataset": "student",
     "train_size": 117519,
     "test_size": 5000,
-    "deduplicated_test_size": 2393,
-    "percent_removed": 52.14,
-    "build_time_seconds": 2.9044899590080604,
-    "deduplication_time_seconds": 0.8721794589655474,
-    "time_seconds": 3.776669417973608
+    "deduplicated_test_size": 2400,
+    "percent_removed": 52.0,
+    "build_time_seconds": 3.4031454579999263,
+    "deduplication_time_seconds": 0.9607139999970968,
+    "time_seconds": 4.363859457997023
   },
   {
     "dataset": "squad_v2",
@@ -155,18 +155,18 @@
     "test_size": 11873,
     "deduplicated_test_size": 11863,
     "percent_removed": 0.08422471153036737,
-    "build_time_seconds": 6.078755749971606,
-    "deduplication_time_seconds": 1.0497459589969367,
-    "time_seconds": 7.1285017089685425
+    "build_time_seconds": 5.937046999999438,
+    "deduplication_time_seconds": 1.1297352090005006,
+    "time_seconds": 7.066782208999939
   },
   {
     "dataset": "wikitext",
     "train_size": 1801350,
     "test_size": 4358,
-    "deduplicated_test_size": 2139,
-    "percent_removed": 50.91785222579165,
-    "build_time_seconds": 39.38258587510791,
-    "deduplication_time_seconds": 0.9325925830053166,
-    "time_seconds": 40.31517845811322
+    "deduplicated_test_size": 2134,
+    "percent_removed": 51.032583754015604,
+    "build_time_seconds": 44.6954585420026,
+    "deduplication_time_seconds": 1.594421958001476,
+    "time_seconds": 46.289880500004074
   }
 ]
diff --git a/semhash/semhash.py b/semhash/semhash.py
index b72225f..56c2a66 100644
--- a/semhash/semhash.py
+++ b/semhash/semhash.py
@@ -9,6 +9,7 @@
 from model2vec import StaticModel
 from pyversity import Strategy, diversify
 from vicinity import Backend
+from vicinity.datatypes import SingleQueryResult
 
 from semhash.datamodels import DeduplicationResult, DuplicateRecord, FilterResult
 from semhash.index import Index
@@ -44,7 +45,7 @@ def __init__(self, index: Index, model: Encoder, columns: Sequence[str], was_str
         self.model = model
         self.columns = columns
         self._was_string = was_string
-        self._ranking_cache: FilterResult | None = None
+        self._ranking_cache: tuple[FilterResult, np.ndarray] | None = None
 
     @classmethod
     def from_records(
@@ -284,10 +285,12 @@ def find_representative(
         :param strategy: Diversification strategy (MMR, MSD, DPP, COVER, SSD). Default is MMR.
         :return: A FilterResult with the diversified candidates.
         """
-        ranking = self._rank_by_average_similarity(records)
+        ranking, embeddings = self._rank_by_average_similarity(records)
         if candidate_limit == "auto":
             candidate_limit = compute_candidate_limit(total=len(ranking.selected), selection_size=selection_size)
-        return self._to_output(self._diversify(ranking, candidate_limit, selection_size, diversity, strategy))
+        return self._to_output(
+            self._diversify(ranking, embeddings, candidate_limit, selection_size, diversity, strategy)
+        )
 
     def self_find_representative(
         self,
@@ -310,10 +313,12 @@ def self_find_representative(
         :param strategy: Diversification strategy (MMR, MSD, DPP, COVER, SSD). Default is MMR.
         :return: A FilterResult with the diversified representatives.
         """
-        ranking = self._self_rank_by_average_similarity()
+        ranking, embeddings = self._self_rank_by_average_similarity()
         if candidate_limit == "auto":
             candidate_limit = compute_candidate_limit(total=len(ranking.selected), selection_size=selection_size)
-        return self._to_output(self._diversify(ranking, candidate_limit, selection_size, diversity, strategy))
+        return self._to_output(
+            self._diversify(ranking, embeddings, candidate_limit, selection_size, diversity, strategy)
+        )
 
     def filter_outliers(
         self,
@@ -333,7 +338,8 @@ def filter_outliers(
         """
         if outlier_percentage < 0.0 or outlier_percentage > 1.0:
             raise ValueError("outlier_percentage must be between 0 and 1")
-        return self._split_into_outliers(self._rank_by_average_similarity(records), outlier_percentage)
+        ranking, _ = self._rank_by_average_similarity(records)
+        return self._split_into_outliers(ranking, outlier_percentage)
 
     def self_filter_outliers(
         self,
@@ -351,7 +357,7 @@ def self_filter_outliers(
         """
         if outlier_percentage < 0.0 or outlier_percentage > 1.0:
             raise ValueError("outlier_percentage must be between 0 and 1")
-        ranking = self._self_rank_by_average_similarity()
+        ranking, _ = self._self_rank_by_average_similarity()
         # Exact copies share the score of their group, so every fitted record is returned.
         groups = {id(group[0]): group for group in self.index.items}
         ranked = [
@@ -400,66 +406,61 @@ def _to_output(self, result: FilterResult) -> FilterResult:
     def _rank_by_average_similarity(
         self,
         records: Sequence[Record],
-    ) -> FilterResult:
+    ) -> tuple[FilterResult, np.ndarray]:
         """
         Rank a given set of records based on the average cosine similarity of the neighbors in the fitted index.
 
         :param records: A sequence of records.
-        :return: A FilterResult containing the ranking (records sorted and their average similarity scores).
+        :return: A FilterResult containing the ranking, and the embeddings of the records in ranked order.
         """
         dict_records = self._validate_if_strings(records)
         embeddings = featurize(records=dict_records, columns=self.columns, model=self.model)
         results = self.index.query_top_k(embeddings, k=100, vectors_are_in_index=False)
-
-        # Compute the average similarity for each record.
-        sorted_scores = sorted(
-            ((record, np.mean(sims)) for record, (_, sims) in zip(dict_records, results)),
-            key=lambda x: x[1],
-            reverse=True,
-        )
-        selected, scores_selected = zip(*sorted_scores)
-
-        return FilterResult(
-            selected=list(selected),
-            filtered=[],
-            scores_selected=list(scores_selected),
-            scores_filtered=[],
-        )
+        return self._rank_from_neighbors(dict_records, embeddings, results)
 
     def _self_rank_by_average_similarity(
         self,
-    ) -> FilterResult:
+    ) -> tuple[FilterResult, np.ndarray]:
         """
         Rank the records stored in the fitted index based on the average cosine similarity of the neighbors.
 
-        :return: A FilterResult containing the ranking.
+        :return: A FilterResult containing the ranking, and the embeddings of the records in ranked order.
         """
         if self._ranking_cache is not None:
             return self._ranking_cache
 
         dict_records = [record[0] for record in self.index.items]
         results = self.index.query_top_k(self.index.vectors, k=100, vectors_are_in_index=True)
+        self._ranking_cache = self._rank_from_neighbors(dict_records, self.index.vectors, results)
+        return self._ranking_cache
 
-        # Compute the average similarity for each record.
-        sorted_scores = sorted(
-            ((record, np.mean(sims)) for record, (_, sims) in zip(dict_records, results)),
-            key=lambda x: x[1],
-            reverse=True,
-        )
-        selected, scores_selected = zip(*sorted_scores)
+    @staticmethod
+    def _rank_from_neighbors(
+        records: list[dict[str, Any]], embeddings: np.ndarray, neighbors: list[SingleQueryResult]
+    ) -> tuple[FilterResult, np.ndarray]:
+        """
+        Rank records by the average similarity of their already computed neighbors, keeping their embeddings aligned.
 
+        :param records: The records to rank.
+        :param embeddings: The embeddings of the records.
+        :param neighbors: The nearest neighbors of each record.
+        :return: A FilterResult containing the ranking, and the embeddings of the records in ranked order.
+        """
+        scores = [np.mean(sims) for _, sims in neighbors]
+        # Stable descending sort, so ties keep their input order.
+        order = sorted(range(len(records)), key=lambda i: scores[i], reverse=True)
         ranking = FilterResult(
-            selected=list(selected),
+            selected=[records[i] for i in order],
             filtered=[],
-            scores_selected=list(scores_selected),
+            scores_selected=[scores[i] for i in order],
             scores_filtered=[],
         )
-        self._ranking_cache = ranking
-        return ranking
+        return ranking, embeddings[order]
 
     def _diversify(
         self,
         ranked_results: FilterResult,
+        ranked_embeddings: np.ndarray,
         candidate_limit: int,
         selection_size: int,
         diversity: float,
@@ -472,9 +473,8 @@ def _diversify(
         if not candidates:
             return FilterResult(selected=[], filtered=[], scores_selected=[], scores_filtered=[])
 
-        embeddings = featurize(records=candidates, columns=self.columns, model=self.model)
         result = diversify(
-            embeddings=embeddings,
+            embeddings=ranked_embeddings[:candidate_limit],
             scores=np.array(relevance),
             k=selection_size,
             strategy=strategy,
diff --git a/semhash/version.py b/semhash/version.py
index 2094209..2647262 100644
--- a/semhash/version.py
+++ b/semhash/version.py
@@ -1,2 +1,2 @@
-__version_triple__ = (0, 4, 2)  # pragma: no cover
+__version_triple__ = (0, 5, 0)  # pragma: no cover
 __version__ = ".".join(map(str, __version_triple__))  # pragma: no cover
diff --git a/tests/test_semhash.py b/tests/test_semhash.py
index 799baef..df4b6ef 100644
--- a/tests/test_semhash.py
+++ b/tests/test_semhash.py
@@ -239,30 +239,26 @@ def test_self_filter_outliers(model: Encoder, train_texts: list[str]) -> None:
         semhash.self_filter_outliers(outlier_percentage=1.5)
 
 
-def test__diversify(monkeypatch: pytest.MonkeyPatch) -> None:
+def test__diversify() -> None:
     """Test the _diversify method."""
-    from semhash import semhash
-
     semhash_instance = SemHash(index=None, model=None, columns=["text"], was_string=True)
     # Prepare a fake ranking with three records
     records = ["a", "b", "c"]
     scores = [3.0, 2.0, 1.0]
     ranking = FilterResult(selected=records, filtered=[], scores_selected=scores, scores_filtered=[])
-    # Create dummy embeddings for the records
+    # Dummy embeddings for the records, in ranked order
     embeddings = np.array([[1.0, 0.0], [0.5, 0.5], [0.0, 1.0]])
-    # Monkeypatch featurize to return the dummy embeddings
-    monkeypatch.setattr(semhash, "featurize", lambda records, columns, model: embeddings)
 
     # Test diversity=0.0: pure relevance, should pick top 2 by score
-    result_rel = semhash_instance._diversify(ranking, candidate_limit=3, selection_size=2, diversity=0.0)
+    result_rel = semhash_instance._diversify(ranking, embeddings, candidate_limit=3, selection_size=2, diversity=0.0)
     assert result_rel.selected == ["a", "b"]
 
     # Test diversity=1.0: pure diversity, should first pick 'a', then pick most dissimilar: 'c'
-    result_div = semhash_instance._diversify(ranking, candidate_limit=3, selection_size=2, diversity=1.0)
+    result_div = semhash_instance._diversify(ranking, embeddings, candidate_limit=3, selection_size=2, diversity=1.0)
     assert result_div.selected == ["a", "c"]
 
     # Test empty candidates (candidate_limit=0)
-    result_empty = semhash_instance._diversify(ranking, candidate_limit=0, selection_size=2, diversity=0.5)
+    result_empty = semhash_instance._diversify(ranking, embeddings, candidate_limit=0, selection_size=2, diversity=0.5)
     assert result_empty.selected == []
     assert result_empty.filtered == []
     assert result_empty.scores_selected == []
@@ -389,6 +385,24 @@ def test_deduplicate_edge_cases(model: Encoder) -> None:
         semhash_dict.deduplicate([{"col": "a"}, "b"], threshold=0.95)
 
 
+def test_representatives_reuse_embeddings(model: Encoder, train_texts: list[str], test_texts: list[str]) -> None:
+    """Representative selection reuses the ranking embeddings instead of encoding candidates again."""
+
+    class CountingEncoder:
+        calls = 0
+
+        def encode(self, inputs: Sequence[Any], **kwargs: Any) -> np.ndarray:
+            CountingEncoder.calls += 1
+            return model.encode(inputs, **kwargs)
+
+    semhash = SemHash.from_records(train_texts, model=CountingEncoder())
+    CountingEncoder.calls = 0
+    semhash.self_find_representative(selection_size=3)
+    assert CountingEncoder.calls == 0
+    semhash.find_representative(test_texts, selection_size=3)
+    assert CountingEncoder.calls == 1
+
+
 def test_self_filter_outliers_keeps_exact_copies(model: Encoder, train_texts: list[str]) -> None:
     """Exact copies are ranked with their group, so every fitted record is returned."""
     records = train_texts + ["car", "car"]