HTML result tables, KURE rows in crimson

README.md

@@ -147,25 +147,39 @@ The figure plots mean nDCG@10 against mean PPS across the nine Korean benchmarks
 
 ### Results — MTEB-ko-retrieval (9 subsets)
 <!-- **공식 9개 subset 을 모두 평가한 모델**의 9-subset mean NDCG@10, PPS -->
-| Model | Params | Mean NDCG@10 | Mean PPS |
-|---|---|---|---|
-| Qwen/Qwen3-Reranker-8B | 8.2B | 0.8990 | 15.0 |
-| Qwen/Qwen3-Reranker-4B | 4.0B | 0.8916 | 24.3 |
-| KaLM-Embedding/KaLM-Reranker-V1-Large-R2 | 7.5B | 0.8910 | 25.3 |
-| **nlpai-lab/KURE-Reranker-base** | 1.7B | 0.8800 | 55.0 |
-| **nlpai-lab/KURE-Reranker-nano** | 149M | 0.8757 | 482.4 |
-| zeroentropy/zerank-2-reranker | 4.0B | 0.8652 | 29.3 |
-| mixedbread-ai/mxbai-rerank-large-v2 | 1.5B | 0.8607 | 64.8 |
-| lightonai/LightOn-rerank-PW-4B | 4.5B | 0.8605 | 15.1 |
-| Qwen/Qwen3-Reranker-0.6B | 596M | 0.8528 | 101.0 |
-| BAAI/bge-reranker-v2-m3 | 568M | 0.8518 | 412.6 |
-| nvidia/llama-nemotron-rerank-1b-v2 | 1.2B | 0.8468 | 128.2 |
-| nlpai-lab/LAMAR-600m | 568M | 0.8375 | 415.0 |
-| dragonkue/bge-reranker-v2-m3-ko | 568M | 0.8199 | 409.1 |
-| BAAI/bge-reranker-v2-gemma | 2.5B | 0.8099 | 58.8 |
-| upskyy/ko-reranker-8k | 568M | 0.8054 | 409.8 |
-| Dongjin-kr/ko-reranker | 560M | 0.7861 | 488.0 |
-| telepix/PIXIE-Spell-Reranker-Preview-0.6B | 596M | 0.7760 | 99.7 |
+<div style="width: fit-content; max-width: 100%; margin: 0 auto;">
+  <div style="overflow-x: auto;">
+    <table cellpadding="6" style="margin: 0 auto; border-collapse: collapse; color: inherit; text-align: right; font-size: 0.85em; line-height: 1.6; font-variant-numeric: tabular-nums;">
+      <thead>
+        <tr style="border-bottom: 1px solid rgba(128, 128, 128, 0.35);">
+          <th scope="col" style="text-align: left;">Model</th>
+          <th scope="col">Params</th>
+          <th scope="col">Mean NDCG@10</th>
+          <th scope="col">Mean PPS</th>
+        </tr>
+      </thead>
+      <tbody>
+        <tr><td style="text-align: left; white-space: nowrap;">Qwen/Qwen3-Reranker-8B</td><td>8.2B</td><td>0.8990</td><td>15.0</td></tr>
+        <tr><td style="text-align: left; white-space: nowrap;">Qwen/Qwen3-Reranker-4B</td><td>4.0B</td><td>0.8916</td><td>24.3</td></tr>
+        <tr><td style="text-align: left; white-space: nowrap;">KaLM-Embedding/KaLM-Reranker-V1-Large-R2</td><td>7.5B</td><td>0.8910</td><td>25.3</td></tr>
+        <tr><td style="text-align: left; white-space: nowrap;"><strong style="color: #c9496b;">nlpai-lab/KURE-Reranker-base</strong></td><td><span style="color: #c9496b;">1.7B</span></td><td><span style="color: #c9496b;">0.8800</span></td><td><span style="color: #c9496b;">55.0</span></td></tr>
+        <tr><td style="text-align: left; white-space: nowrap;"><strong style="color: #c9496b;">nlpai-lab/KURE-Reranker-nano</strong></td><td><span style="color: #c9496b;">149M</span></td><td><span style="color: #c9496b;">0.8757</span></td><td><span style="color: #c9496b;">482.4</span></td></tr>
+        <tr><td style="text-align: left; white-space: nowrap;">zeroentropy/zerank-2-reranker</td><td>4.0B</td><td>0.8652</td><td>29.3</td></tr>
+        <tr><td style="text-align: left; white-space: nowrap;">mixedbread-ai/mxbai-rerank-large-v2</td><td>1.5B</td><td>0.8607</td><td>64.8</td></tr>
+        <tr><td style="text-align: left; white-space: nowrap;">lightonai/LightOn-rerank-PW-4B</td><td>4.5B</td><td>0.8605</td><td>15.1</td></tr>
+        <tr><td style="text-align: left; white-space: nowrap;">Qwen/Qwen3-Reranker-0.6B</td><td>596M</td><td>0.8528</td><td>101.0</td></tr>
+        <tr><td style="text-align: left; white-space: nowrap;">BAAI/bge-reranker-v2-m3</td><td>568M</td><td>0.8518</td><td>412.6</td></tr>
+        <tr><td style="text-align: left; white-space: nowrap;">nvidia/llama-nemotron-rerank-1b-v2</td><td>1.2B</td><td>0.8468</td><td>128.2</td></tr>
+        <tr><td style="text-align: left; white-space: nowrap;">nlpai-lab/LAMAR-600m</td><td>568M</td><td>0.8375</td><td>415.0</td></tr>
+        <tr><td style="text-align: left; white-space: nowrap;">dragonkue/bge-reranker-v2-m3-ko</td><td>568M</td><td>0.8199</td><td>409.1</td></tr>
+        <tr><td style="text-align: left; white-space: nowrap;">BAAI/bge-reranker-v2-gemma</td><td>2.5B</td><td>0.8099</td><td>58.8</td></tr>
+        <tr><td style="text-align: left; white-space: nowrap;">upskyy/ko-reranker-8k</td><td>568M</td><td>0.8054</td><td>409.8</td></tr>
+        <tr><td style="text-align: left; white-space: nowrap;">Dongjin-kr/ko-reranker</td><td>560M</td><td>0.7861</td><td>488.0</td></tr>
+        <tr><td style="text-align: left; white-space: nowrap;">telepix/PIXIE-Spell-Reranker-Preview-0.6B</td><td>596M</td><td>0.7760</td><td>99.7</td></tr>
+      </tbody>
+    </table>
+  </div>
+</div>
 
 PPS is measured on a single NVIDIA RTX A6000 48GB per model and averaged over the nine benchmarks. For each benchmark, every model is timed on the same ~550 sampled query–document pairs (whole queries, fixed seed). Inputs are length-sorted and dynamically padded within each batch. Models run in bf16 with flash_attention_2, except `KaLM-Embedding/KaLM-Reranker-V1-Large-R2`, which uses sdpa because transformers does not support flash_attention_2 for T5Gemma2. Each query is reranked over its gold documents plus, as negatives, the BM25 top-50 minus the golds; BelebeleRetrieval uses its kor_Hang-kor_Hang subset.
 
@@ -174,54 +188,96 @@ PPS is measured on a single NVIDIA RTX A6000 48GB per model and averaged over th
 <details>
 <summary>Per-dataset NDCG@10</summary>
 
-| Model | Params | Ko-StrategyQA | AutoRAGRetrieval | PublicHealthQA | BelebeleRetrieval | MIRACLRetrieval | MrTidyRetrieval | MultiLongDocRetrieval | SQuADKorV1Retrieval | LawIRKo |
-|---|---|---|---|---|---|---|---|---|---|---|
-| Qwen/Qwen3-Reranker-8B | 8.2B | 0.8720 | 0.9653 | 0.8654 | 0.9785 | 0.8513 | 0.8416 | 0.8255 | 0.9897 | 0.9013 |
-| Qwen/Qwen3-Reranker-4B | 4.0B | 0.8733 | 0.9630 | 0.8442 | 0.9794 | 0.8559 | 0.8298 | 0.8161 | 0.9861 | 0.8766 |
-| KaLM-Embedding/KaLM-Reranker-V1-Large-R2 | 7.5B | 0.8796 | 0.9415 | 0.8633 | 0.9809 | 0.8382 | 0.8500 | 0.7970 | 0.9863 | 0.8824 |
-| **nlpai-lab/KURE-Reranker-base** | 1.7B | 0.8548 | 0.9762 | 0.8381 | 0.9767 | 0.8371 | 0.7970 | 0.8163 | 0.9891 | 0.8343 |
-| **nlpai-lab/KURE-Reranker-nano** | 149M | 0.8582 | 0.9741 | 0.8078 | 0.9768 | 0.8400 | 0.8011 | 0.7966 | 0.9895 | 0.8368 |
-| jinaai/jina-reranker-v3.5 | 597M | 0.8539 | 0.9838 | 0.8337 | 0.9659 | 0.8565 | 0.8194 | — | 0.9887 | 0.8545 |
-| jinaai/jina-reranker-v3 | 597M | 0.8553 | 0.9773 | 0.7762 | 0.9606 | 0.8449 | 0.8104 | — | 0.9859 | 0.8546 |
-| zeroentropy/zerank-2-reranker | 4.0B | 0.8712 | 0.9436 | 0.8377 | 0.9731 | 0.8003 | 0.8027 | 0.7120 | 0.9791 | 0.8669 |
-| mixedbread-ai/mxbai-rerank-large-v2 | 1.5B | 0.8563 | 0.9531 | 0.8407 | 0.9659 | 0.7939 | 0.8771 | 0.6787 | 0.9681 | 0.8130 |
-| lightonai/LightOn-rerank-PW-4B | 4.5B | 0.8567 | 0.9321 | 0.8312 | 0.9735 | 0.8072 | 0.8091 | 0.7609 | 0.9803 | 0.7938 |
-| Qwen/Qwen3-Reranker-0.6B | 596M | 0.8336 | 0.9321 | 0.8092 | 0.9721 | 0.8470 | 0.7325 | 0.7819 | 0.9804 | 0.7865 |
-| BAAI/bge-reranker-v2-m3 | 568M | 0.8487 | 0.9663 | 0.7982 | 0.9725 | 0.8129 | 0.8222 | 0.6690 | 0.9853 | 0.7906 |
-| nvidia/llama-nemotron-rerank-1b-v2 | 1.2B | 0.8536 | 0.9480 | 0.8130 | 0.9755 | 0.8182 | 0.8026 | 0.6719 | 0.9858 | 0.7527 |
-| nlpai-lab/LAMAR-600m | 568M | 0.8461 | 0.9591 | 0.8044 | 0.9740 | 0.8214 | 0.7975 | 0.5679 | 0.9850 | 0.7822 |
-| dragonkue/bge-reranker-v2-m3-ko | 568M | 0.8232 | 0.9684 | 0.8155 | 0.9744 | 0.7573 | 0.6776 | 0.7061 | 0.9846 | 0.6721 |
-| BAAI/bge-reranker-v2-gemma | 2.5B | 0.8614 | 0.9407 | 0.8069 | 0.9696 | 0.8362 | 0.8429 | 0.2881 | 0.9858 | 0.7572 |
-| upskyy/ko-reranker-8k | 568M | 0.8143 | 0.9230 | 0.7792 | 0.9609 | 0.7249 | 0.6998 | 0.5975 | 0.9718 | 0.7770 |
-| Dongjin-kr/ko-reranker | 560M | 0.8468 | 0.9014 | 0.6976 | 0.9655 | 0.8017 | 0.7772 | 0.3721 | 0.9785 | 0.7343 |
-| telepix/PIXIE-Spell-Reranker-Preview-0.6B | 596M | 0.8329 | 0.9794 | 0.8211 | 0.9689 | 0.8449 | 0.7650 | 0.1829 | 0.9850 | 0.6042 |
+<div style="width: fit-content; max-width: 100%; margin: 0 auto;">
+  <div style="overflow-x: auto;">
+    <table cellpadding="6" style="margin: 0 auto; border-collapse: collapse; color: inherit; text-align: right; font-size: 0.85em; line-height: 1.6; font-variant-numeric: tabular-nums;">
+      <thead>
+        <tr style="border-bottom: 1px solid rgba(128, 128, 128, 0.35);">
+          <th scope="col" style="text-align: left;">Model</th>
+          <th scope="col">Params</th>
+          <th scope="col">Ko-StrategyQA</th>
+          <th scope="col">AutoRAGRetrieval</th>
+          <th scope="col">PublicHealthQA</th>
+          <th scope="col">BelebeleRetrieval</th>
+          <th scope="col">MIRACLRetrieval</th>
+          <th scope="col">MrTidyRetrieval</th>
+          <th scope="col">MultiLongDocRetrieval</th>
+          <th scope="col">SQuADKorV1Retrieval</th>
+          <th scope="col">LawIRKo</th>
+        </tr>
+      </thead>
+      <tbody>
+        <tr><td style="text-align: left; white-space: nowrap;">Qwen/Qwen3-Reranker-8B</td><td>8.2B</td><td>0.8720</td><td>0.9653</td><td>0.8654</td><td>0.9785</td><td>0.8513</td><td>0.8416</td><td>0.8255</td><td>0.9897</td><td>0.9013</td></tr>
+        <tr><td style="text-align: left; white-space: nowrap;">Qwen/Qwen3-Reranker-4B</td><td>4.0B</td><td>0.8733</td><td>0.9630</td><td>0.8442</td><td>0.9794</td><td>0.8559</td><td>0.8298</td><td>0.8161</td><td>0.9861</td><td>0.8766</td></tr>
+        <tr><td style="text-align: left; white-space: nowrap;">KaLM-Embedding/KaLM-Reranker-V1-Large-R2</td><td>7.5B</td><td>0.8796</td><td>0.9415</td><td>0.8633</td><td>0.9809</td><td>0.8382</td><td>0.8500</td><td>0.7970</td><td>0.9863</td><td>0.8824</td></tr>
+        <tr><td style="text-align: left; white-space: nowrap;"><strong style="color: #c9496b;">nlpai-lab/KURE-Reranker-base</strong></td><td><span style="color: #c9496b;">1.7B</span></td><td><span style="color: #c9496b;">0.8548</span></td><td><span style="color: #c9496b;">0.9762</span></td><td><span style="color: #c9496b;">0.8381</span></td><td><span style="color: #c9496b;">0.9767</span></td><td><span style="color: #c9496b;">0.8371</span></td><td><span style="color: #c9496b;">0.7970</span></td><td><span style="color: #c9496b;">0.8163</span></td><td><span style="color: #c9496b;">0.9891</span></td><td><span style="color: #c9496b;">0.8343</span></td></tr>
+        <tr><td style="text-align: left; white-space: nowrap;"><strong style="color: #c9496b;">nlpai-lab/KURE-Reranker-nano</strong></td><td><span style="color: #c9496b;">149M</span></td><td><span style="color: #c9496b;">0.8582</span></td><td><span style="color: #c9496b;">0.9741</span></td><td><span style="color: #c9496b;">0.8078</span></td><td><span style="color: #c9496b;">0.9768</span></td><td><span style="color: #c9496b;">0.8400</span></td><td><span style="color: #c9496b;">0.8011</span></td><td><span style="color: #c9496b;">0.7966</span></td><td><span style="color: #c9496b;">0.9895</span></td><td><span style="color: #c9496b;">0.8368</span></td></tr>
+        <tr><td style="text-align: left; white-space: nowrap;">jinaai/jina-reranker-v3.5</td><td>597M</td><td>0.8539</td><td>0.9838</td><td>0.8337</td><td>0.9659</td><td>0.8565</td><td>0.8194</td><td>—</td><td>0.9887</td><td>0.8545</td></tr>
+        <tr><td style="text-align: left; white-space: nowrap;">jinaai/jina-reranker-v3</td><td>597M</td><td>0.8553</td><td>0.9773</td><td>0.7762</td><td>0.9606</td><td>0.8449</td><td>0.8104</td><td>—</td><td>0.9859</td><td>0.8546</td></tr>
+        <tr><td style="text-align: left; white-space: nowrap;">zeroentropy/zerank-2-reranker</td><td>4.0B</td><td>0.8712</td><td>0.9436</td><td>0.8377</td><td>0.9731</td><td>0.8003</td><td>0.8027</td><td>0.7120</td><td>0.9791</td><td>0.8669</td></tr>
+        <tr><td style="text-align: left; white-space: nowrap;">mixedbread-ai/mxbai-rerank-large-v2</td><td>1.5B</td><td>0.8563</td><td>0.9531</td><td>0.8407</td><td>0.9659</td><td>0.7939</td><td>0.8771</td><td>0.6787</td><td>0.9681</td><td>0.8130</td></tr>
+        <tr><td style="text-align: left; white-space: nowrap;">lightonai/LightOn-rerank-PW-4B</td><td>4.5B</td><td>0.8567</td><td>0.9321</td><td>0.8312</td><td>0.9735</td><td>0.8072</td><td>0.8091</td><td>0.7609</td><td>0.9803</td><td>0.7938</td></tr>
+        <tr><td style="text-align: left; white-space: nowrap;">Qwen/Qwen3-Reranker-0.6B</td><td>596M</td><td>0.8336</td><td>0.9321</td><td>0.8092</td><td>0.9721</td><td>0.8470</td><td>0.7325</td><td>0.7819</td><td>0.9804</td><td>0.7865</td></tr>
+        <tr><td style="text-align: left; white-space: nowrap;">BAAI/bge-reranker-v2-m3</td><td>568M</td><td>0.8487</td><td>0.9663</td><td>0.7982</td><td>0.9725</td><td>0.8129</td><td>0.8222</td><td>0.6690</td><td>0.9853</td><td>0.7906</td></tr>
+        <tr><td style="text-align: left; white-space: nowrap;">nvidia/llama-nemotron-rerank-1b-v2</td><td>1.2B</td><td>0.8536</td><td>0.9480</td><td>0.8130</td><td>0.9755</td><td>0.8182</td><td>0.8026</td><td>0.6719</td><td>0.9858</td><td>0.7527</td></tr>
+        <tr><td style="text-align: left; white-space: nowrap;">nlpai-lab/LAMAR-600m</td><td>568M</td><td>0.8461</td><td>0.9591</td><td>0.8044</td><td>0.9740</td><td>0.8214</td><td>0.7975</td><td>0.5679</td><td>0.9850</td><td>0.7822</td></tr>
+        <tr><td style="text-align: left; white-space: nowrap;">dragonkue/bge-reranker-v2-m3-ko</td><td>568M</td><td>0.8232</td><td>0.9684</td><td>0.8155</td><td>0.9744</td><td>0.7573</td><td>0.6776</td><td>0.7061</td><td>0.9846</td><td>0.6721</td></tr>
+        <tr><td style="text-align: left; white-space: nowrap;">BAAI/bge-reranker-v2-gemma</td><td>2.5B</td><td>0.8614</td><td>0.9407</td><td>0.8069</td><td>0.9696</td><td>0.8362</td><td>0.8429</td><td>0.2881</td><td>0.9858</td><td>0.7572</td></tr>
+        <tr><td style="text-align: left; white-space: nowrap;">upskyy/ko-reranker-8k</td><td>568M</td><td>0.8143</td><td>0.9230</td><td>0.7792</td><td>0.9609</td><td>0.7249</td><td>0.6998</td><td>0.5975</td><td>0.9718</td><td>0.7770</td></tr>
+        <tr><td style="text-align: left; white-space: nowrap;">Dongjin-kr/ko-reranker</td><td>560M</td><td>0.8468</td><td>0.9014</td><td>0.6976</td><td>0.9655</td><td>0.8017</td><td>0.7772</td><td>0.3721</td><td>0.9785</td><td>0.7343</td></tr>
+        <tr><td style="text-align: left; white-space: nowrap;">telepix/PIXIE-Spell-Reranker-Preview-0.6B</td><td>596M</td><td>0.8329</td><td>0.9794</td><td>0.8211</td><td>0.9689</td><td>0.8449</td><td>0.7650</td><td>0.1829</td><td>0.9850</td><td>0.6042</td></tr>
+      </tbody>
+    </table>
+  </div>
+</div>
 
 </details>
 
 <details>
 <summary>Per-dataset PPS</summary>
 
-| Model | Params | Ko-StrategyQA | AutoRAGRetrieval | PublicHealthQA | BelebeleRetrieval | MIRACLRetrieval | MrTidyRetrieval | MultiLongDocRetrieval | SQuADKorV1Retrieval | LawIRKo |
-|---|---|---|---|---|---|---|---|---|---|---|
-| Qwen/Qwen3-Reranker-8B | 8.2B | 16.5 | 7.4 | 15.3 | 20.5 | 25.0 | 26.6 | 0.7 | 10.8 | 12.4 |
-| Qwen/Qwen3-Reranker-4B | 4.0B | 26.2 | 11.8 | 25.6 | 34.5 | 40.1 | 42.8 | 1.1 | 17.1 | 19.9 |
-| KaLM-Embedding/KaLM-Reranker-V1-Large-R2 | 7.5B | 28.6 | 12.4 | 25.4 | 36.9 | 39.6 | 43.8 | 0.8 | 18.7 | 21.4 |
-| **nlpai-lab/KURE-Reranker-base** | 1.7B | 59.2 | 27.3 | 58.0 | 77.0 | 89.1 | 97.1 | 2.6 | 39.1 | 45.7 |
-| **nlpai-lab/KURE-Reranker-nano** | 149M | 475.4 | 233.2 | 529.0 | 750.0 | 765.8 | 855.2 | 16.5 | 330.8 | 386.0 |
-| jinaai/jina-reranker-v3.5 | 597M | 141.0 | 38.6 | 123.2 | 197.5 | 277.3 | 295.7 | — | 68.4 | 86.7 |
-| jinaai/jina-reranker-v3 | 597M | 113.8 | 25.0 | 95.7 | 166.3 | 244.6 | 261.6 | — | 48.7 | 64.0 |
-| zeroentropy/zerank-2-reranker | 4.0B | 31.0 | 12.7 | 29.5 | 41.4 | 51.0 | 56.4 | 1.1 | 18.8 | 22.3 |
-| mixedbread-ai/mxbai-rerank-large-v2 | 1.5B | 70.0 | 32.9 | 68.0 | 89.8 | 104.2 | 113.6 | 3.2 | 47.0 | 54.7 |
-| lightonai/LightOn-rerank-PW-4B | 4.5B | 16.2 | 7.3 | 17.0 | 22.2 | 23.4 | 26.1 | 0.7 | 10.7 | 12.4 |
-| Qwen/Qwen3-Reranker-0.6B | 596M | 105.9 | 49.4 | 109.5 | 145.8 | 162.6 | 177.4 | 4.3 | 70.8 | 83.6 |
-| BAAI/bge-reranker-v2-m3 | 568M | 420.7 | 195.6 | 440.9 | 651.8 | 653.2 | 724.8 | 9.3 | 271.6 | 345.5 |
-| nvidia/llama-nemotron-rerank-1b-v2 | 1.2B | 133.7 | 54.4 | 131.6 | 186.3 | 220.0 | 243.1 | 3.9 | 84.9 | 95.9 |
-| nlpai-lab/LAMAR-600m | 568M | 418.0 | 196.5 | 442.5 | 651.1 | 656.7 | 742.5 | 9.2 | 275.0 | 343.8 |
-| dragonkue/bge-reranker-v2-m3-ko | 568M | 416.9 | 195.1 | 434.4 | 641.3 | 653.4 | 716.7 | 9.2 | 271.9 | 342.7 |
-| BAAI/bge-reranker-v2-gemma | 2.5B | 64.7 | 26.1 | 60.5 | 83.8 | 99.8 | 106.8 | 2.5 | 40.0 | 44.7 |
-| upskyy/ko-reranker-8k | 568M | 411.8 | 195.6 | 436.2 | 641.7 | 649.3 | 726.0 | 9.2 | 275.1 | 342.8 |
-| Dongjin-kr/ko-reranker | 560M | 520.8 | 258.0 | 468.3 | 644.1 | 747.6 | 765.2 | 272.7 | 334.4 | 381.2 |
-| telepix/PIXIE-Spell-Reranker-Preview-0.6B | 596M | 104.6 | 49.4 | 106.3 | 141.5 | 160.5 | 175.6 | 4.3 | 72.1 | 82.6 |
+<div style="width: fit-content; max-width: 100%; margin: 0 auto;">
+  <div style="overflow-x: auto;">
+    <table cellpadding="6" style="margin: 0 auto; border-collapse: collapse; color: inherit; text-align: right; font-size: 0.85em; line-height: 1.6; font-variant-numeric: tabular-nums;">
+      <thead>
+        <tr style="border-bottom: 1px solid rgba(128, 128, 128, 0.35);">
+          <th scope="col" style="text-align: left;">Model</th>
+          <th scope="col">Params</th>
+          <th scope="col">Ko-StrategyQA</th>
+          <th scope="col">AutoRAGRetrieval</th>
+          <th scope="col">PublicHealthQA</th>
+          <th scope="col">BelebeleRetrieval</th>
+          <th scope="col">MIRACLRetrieval</th>
+          <th scope="col">MrTidyRetrieval</th>
+          <th scope="col">MultiLongDocRetrieval</th>
+          <th scope="col">SQuADKorV1Retrieval</th>
+          <th scope="col">LawIRKo</th>
+        </tr>
+      </thead>
+      <tbody>
+        <tr><td style="text-align: left; white-space: nowrap;">Qwen/Qwen3-Reranker-8B</td><td>8.2B</td><td>16.5</td><td>7.4</td><td>15.3</td><td>20.5</td><td>25.0</td><td>26.6</td><td>0.7</td><td>10.8</td><td>12.4</td></tr>
+        <tr><td style="text-align: left; white-space: nowrap;">Qwen/Qwen3-Reranker-4B</td><td>4.0B</td><td>26.2</td><td>11.8</td><td>25.6</td><td>34.5</td><td>40.1</td><td>42.8</td><td>1.1</td><td>17.1</td><td>19.9</td></tr>
+        <tr><td style="text-align: left; white-space: nowrap;">KaLM-Embedding/KaLM-Reranker-V1-Large-R2</td><td>7.5B</td><td>28.6</td><td>12.4</td><td>25.4</td><td>36.9</td><td>39.6</td><td>43.8</td><td>0.8</td><td>18.7</td><td>21.4</td></tr>
+        <tr><td style="text-align: left; white-space: nowrap;"><strong style="color: #c9496b;">nlpai-lab/KURE-Reranker-base</strong></td><td><span style="color: #c9496b;">1.7B</span></td><td><span style="color: #c9496b;">59.2</span></td><td><span style="color: #c9496b;">27.3</span></td><td><span style="color: #c9496b;">58.0</span></td><td><span style="color: #c9496b;">77.0</span></td><td><span style="color: #c9496b;">89.1</span></td><td><span style="color: #c9496b;">97.1</span></td><td><span style="color: #c9496b;">2.6</span></td><td><span style="color: #c9496b;">39.1</span></td><td><span style="color: #c9496b;">45.7</span></td></tr>
+        <tr><td style="text-align: left; white-space: nowrap;"><strong style="color: #c9496b;">nlpai-lab/KURE-Reranker-nano</strong></td><td><span style="color: #c9496b;">149M</span></td><td><span style="color: #c9496b;">475.4</span></td><td><span style="color: #c9496b;">233.2</span></td><td><span style="color: #c9496b;">529.0</span></td><td><span style="color: #c9496b;">750.0</span></td><td><span style="color: #c9496b;">765.8</span></td><td><span style="color: #c9496b;">855.2</span></td><td><span style="color: #c9496b;">16.5</span></td><td><span style="color: #c9496b;">330.8</span></td><td><span style="color: #c9496b;">386.0</span></td></tr>
+        <tr><td style="text-align: left; white-space: nowrap;">jinaai/jina-reranker-v3.5</td><td>597M</td><td>141.0</td><td>38.6</td><td>123.2</td><td>197.5</td><td>277.3</td><td>295.7</td><td>—</td><td>68.4</td><td>86.7</td></tr>
+        <tr><td style="text-align: left; white-space: nowrap;">jinaai/jina-reranker-v3</td><td>597M</td><td>113.8</td><td>25.0</td><td>95.7</td><td>166.3</td><td>244.6</td><td>261.6</td><td>—</td><td>48.7</td><td>64.0</td></tr>
+        <tr><td style="text-align: left; white-space: nowrap;">zeroentropy/zerank-2-reranker</td><td>4.0B</td><td>31.0</td><td>12.7</td><td>29.5</td><td>41.4</td><td>51.0</td><td>56.4</td><td>1.1</td><td>18.8</td><td>22.3</td></tr>
+        <tr><td style="text-align: left; white-space: nowrap;">mixedbread-ai/mxbai-rerank-large-v2</td><td>1.5B</td><td>70.0</td><td>32.9</td><td>68.0</td><td>89.8</td><td>104.2</td><td>113.6</td><td>3.2</td><td>47.0</td><td>54.7</td></tr>
+        <tr><td style="text-align: left; white-space: nowrap;">lightonai/LightOn-rerank-PW-4B</td><td>4.5B</td><td>16.2</td><td>7.3</td><td>17.0</td><td>22.2</td><td>23.4</td><td>26.1</td><td>0.7</td><td>10.7</td><td>12.4</td></tr>
+        <tr><td style="text-align: left; white-space: nowrap;">Qwen/Qwen3-Reranker-0.6B</td><td>596M</td><td>105.9</td><td>49.4</td><td>109.5</td><td>145.8</td><td>162.6</td><td>177.4</td><td>4.3</td><td>70.8</td><td>83.6</td></tr>
+        <tr><td style="text-align: left; white-space: nowrap;">BAAI/bge-reranker-v2-m3</td><td>568M</td><td>420.7</td><td>195.6</td><td>440.9</td><td>651.8</td><td>653.2</td><td>724.8</td><td>9.3</td><td>271.6</td><td>345.5</td></tr>
+        <tr><td style="text-align: left; white-space: nowrap;">nvidia/llama-nemotron-rerank-1b-v2</td><td>1.2B</td><td>133.7</td><td>54.4</td><td>131.6</td><td>186.3</td><td>220.0</td><td>243.1</td><td>3.9</td><td>84.9</td><td>95.9</td></tr>
+        <tr><td style="text-align: left; white-space: nowrap;">nlpai-lab/LAMAR-600m</td><td>568M</td><td>418.0</td><td>196.5</td><td>442.5</td><td>651.1</td><td>656.7</td><td>742.5</td><td>9.2</td><td>275.0</td><td>343.8</td></tr>
+        <tr><td style="text-align: left; white-space: nowrap;">dragonkue/bge-reranker-v2-m3-ko</td><td>568M</td><td>416.9</td><td>195.1</td><td>434.4</td><td>641.3</td><td>653.4</td><td>716.7</td><td>9.2</td><td>271.9</td><td>342.7</td></tr>
+        <tr><td style="text-align: left; white-space: nowrap;">BAAI/bge-reranker-v2-gemma</td><td>2.5B</td><td>64.7</td><td>26.1</td><td>60.5</td><td>83.8</td><td>99.8</td><td>106.8</td><td>2.5</td><td>40.0</td><td>44.7</td></tr>
+        <tr><td style="text-align: left; white-space: nowrap;">upskyy/ko-reranker-8k</td><td>568M</td><td>411.8</td><td>195.6</td><td>436.2</td><td>641.7</td><td>649.3</td><td>726.0</td><td>9.2</td><td>275.1</td><td>342.8</td></tr>
+        <tr><td style="text-align: left; white-space: nowrap;">Dongjin-kr/ko-reranker</td><td>560M</td><td>520.8</td><td>258.0</td><td>468.3</td><td>644.1</td><td>747.6</td><td>765.2</td><td>272.7</td><td>334.4</td><td>381.2</td></tr>
+        <tr><td style="text-align: left; white-space: nowrap;">telepix/PIXIE-Spell-Reranker-Preview-0.6B</td><td>596M</td><td>104.6</td><td>49.4</td><td>106.3</td><td>141.5</td><td>160.5</td><td>175.6</td><td>4.3</td><td>72.1</td><td>82.6</td></tr>
+      </tbody>
+    </table>
+  </div>
+</div>
 
 Batch size starts at 8 and doubles until an out-of-memory error. Samples are repeated to fill complete batches. After warmup, three full passes are timed with CUDA events. Throughput is the total number of processed pairs divided by the accumulated GPU forward time. The highest-throughput successful batch is reported. Tokenization, data loading, and CPU preprocessing are excluded. The batch-search approach is informed by the [Ettin reranker speed benchmark](https://huggingface.co/blog/ettin-reranker#speed).