diff --git a/rpent/benchmarks/docs.css b/rpent/benchmarks/docs.css
index 8c8435f..87dc250 100644
--- a/rpent/benchmarks/docs.css
+++ b/rpent/benchmarks/docs.css
@@ -43,6 +43,9 @@ html[data-theme="dark"] .rpent-static-leaderboard{--rpent-divider:#be96e0}
 @media(max-width:700px){.rpent-static-leaderboard .page-section-title{font-size:32px}.rpent-static-leaderboard [data-benchmark],.rpent-static-leaderboard .cost-group{padding-inline:0}}
 
 .rpent-static-leaderboard .scope-note{font-size:14px;line-height:1.6;color:var(--pst-color-text-muted,inherit);margin:14px 0;scroll-margin-top:5rem}
+.rpent-static-leaderboard .leaderboard-footer{margin-top:28px;padding-bottom:24px;color:var(--pst-color-text-muted,inherit)}
+.rpent-static-leaderboard .leaderboard-footer>span{display:block;font-size:17px}
+.rpent-static-leaderboard .leaderboard-footer .evaluation-notice{margin:0 0 12px}
 .rpent-static-leaderboard .note-reference{font-size:.75em;margin-inline-start:.2em;white-space:nowrap}
 .rpent-static-leaderboard .note-reference a{text-decoration:underline;text-underline-offset:2px}
 .rpent-static-leaderboard .note-reference a:focus-visible,.rpent-static-leaderboard .scope-note:focus-visible{outline:2px solid var(--rpent-divider);outline-offset:3px}
diff --git a/rpent/benchmarks/embed.css b/rpent/benchmarks/embed.css
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@@ -93,6 +93,9 @@ button:focus-visible,a:focus-visible,select:focus-visible,summary:focus-visible,
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 @media print{.section-nav,.module-directory{display:none}.section-layout{display:block}.module-header{position:static}}
 footer{padding-inline:max(var(--gutter),calc((100% - var(--content-max))/2 + var(--gutter)))}
+footer{align-items:flex-end}.footer-context{flex:1;min-width:0}.footer-context>[data-i18n="title"]{display:block;font-size:17px}footer .evaluation-notice{margin:0 0 12px}
+.leaderboard-footer{margin-top:28px;padding-bottom:24px;color:var(--muted)}.leaderboard-footer>span{display:block;font-size:17px}.leaderboard-footer .evaluation-notice{margin:0 0 12px}
+@media print{footer{display:block!important}footer .footer-context>[data-i18n="title"],footer>a{display:none!important}}
 noscript .section-layout{padding-inline:0}noscript .module-header{position:static}
 noscript .brand{font-size:24px;font-weight:400}noscript .page-section-title{padding:12px 0 24px}
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diff --git a/rpent/benchmarks/embed.html b/rpent/benchmarks/embed.html
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@@ -24,6 +24,6 @@ <h2 id="page-section-heading" class="page-section-title">Performance</h2>
     </section>
     <section id="time-token-costs" class="cost-section" aria-label="Time &amp; Token Costs" data-i18n-label="costs"></section>
   </main>
-  <footer><span data-i18n="title">RPent Leaderboard</span><a href="#top" data-i18n="backTop">返回顶部</a></footer>
+  <footer><div class="footer-context"><p class="scope-note evaluation-notice" data-evaluation-notice>* Leaderboard 中所有 RPent 方法均展示 2026.09 测试结果。</p><span data-i18n="title">RPent Leaderboard</span></div><a href="#top" data-i18n="backTop">返回顶部</a></footer>
   <div id="chart-tooltip" class="chart-tooltip" role="tooltip" hidden></div>
   <p id="announcement" class="sr-only" aria-live="polite"></p>
diff --git a/rpent/benchmarks/index.html b/rpent/benchmarks/index.html
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@@ -25,13 +25,13 @@ <h2 id="page-section-heading" class="page-section-title">Performance</h2>
     </section>
     <section id="time-token-costs" class="cost-section" aria-label="Time &amp; Token Costs" data-i18n-label="costs"></section>
   </main>
-  <footer><span data-i18n="title">RPent Leaderboard</span><a href="#top" data-i18n="backTop">返回顶部</a></footer>
+  <footer><div class="footer-context"><p class="scope-note evaluation-notice" data-evaluation-notice>* Leaderboard 中所有 RPent 方法均展示 2026.09 测试结果。</p><span data-i18n="title">RPent Leaderboard</span></div><a href="#top" data-i18n="backTop">返回顶部</a></footer>
   <div id="chart-tooltip" class="chart-tooltip" role="tooltip" hidden></div>
   <p id="announcement" class="sr-only" aria-live="polite"></p>
-<noscript><div class="section-inner"><style>.page-header,#main,footer{display:none}</style><h1 class="brand">RPent 排行榜</h1><nav class="section-nav"><a href="#native-performance">评测成绩</a><a href="#native-time-token-costs">耗时与 Token 开销</a></nav><section id="native-performance" data-performance="true"><h2 class="page-section-title">评测成绩</h2><div class="section-layout"><details class="module-directory" open><summary>环境</summary><nav class="module-nav" aria-label="评测成绩"><a href="#native-libero-pro">LIBERO-PRO</a><a href="#native-standard-libero">LIBERO</a><a href="#native-robocasa">RoboCasa365</a><a href="#native-robotwin">RoboTwin</a></nav></details><div class="section-results">
+<noscript><div class="section-inner"><style>.page-header,#main,footer{display:none!important}</style><h1 class="brand">RPent 排行榜</h1><nav class="section-nav"><a href="#native-performance">评测成绩</a><a href="#native-time-token-costs">耗时与 Token 开销</a></nav><section id="native-performance" data-performance="true"><h2 class="page-section-title">评测成绩</h2><div class="section-layout"><details class="module-directory" open><summary>环境</summary><nav class="module-nav" aria-label="评测成绩"><a href="#native-libero-pro">LIBERO-PRO</a><a href="#native-standard-libero">LIBERO</a><a href="#native-robocasa">RoboCasa365</a><a href="#native-robotwin">RoboTwin</a></nav></details><div class="section-results">
 <section data-benchmark="libero-pro"><details data-module="libero-pro"><summary class="module-header"><h3>LIBERO-PRO</h3></summary><div id="native-libero-pro" class="module-content"><table><thead><tr><th>方法</th><th>成功率</th></tr></thead><tbody><tr><td>Codex / GPT-6 Astra / low / reasoning<sup class="note-reference"><a href="#libero-pro-astra-memory" role="doc-noteref" aria-label="GPT-6 Astra的评测说明 4">[4]</a></sup></td><td>92.63%</td></tr><tr><td>Claude Code / Opus-4.7 / max.reasoning</td><td>82.4%</td></tr><tr><td>Codex / GPT-5.5 / xhigh / reasoning</td><td>75.13%</td></tr><tr><td>RPent Flash Mode<sup class="note-reference"><a href="#libero-pro-note-2" role="doc-noteref" aria-label="RPent Flash Mode的评测说明 2">[2]</a></sup></td><td>72.63%</td></tr><tr><td>Qwen3.6 27B / no-reasoning</td><td>70.63%</td></tr><tr><td>ASPIRE<sup class="note-reference"><a href="#libero-pro-note-1" role="doc-noteref" aria-label="ASPIRE的评测说明 1">[1]</a></sup></td><td>61.36%</td></tr><tr><td>π_RLinf</td><td>50.0%</td></tr><tr><td>π0.5</td><td>11.0%</td></tr><tr><td>AtomVLA</td><td>6.3%</td></tr><tr><td>X-VLA</td><td>3.8%</td></tr><tr><td>MolmoAct</td><td>1.5%</td></tr><tr><td>π0</td><td>0.3%</td></tr></tbody></table><p>* GPT-6 Astra: 92.63% (741/800). RPent Flash Mode / Molmo2-8B: 72.63% (581/800). Qwen3.6 27B / no-reasoning: 70.63% (565/800).</p><h4>完整方法与分项成绩</h4><div class="table-wrap" tabindex="0"><table><thead><tr><th>方法</th><th>总体</th><th>Spatial Task</th><th>Spatial Swap</th><th>Object Task</th><th>Object Swap</th><th>Goal Task</th><th>Goal Swap</th><th>Long Task</th><th>Long Swap</th></tr></thead><tbody><tr><td>GPT-6 Astra<sup class="note-reference"><a href="#libero-pro-astra-memory" role="doc-noteref" aria-label="GPT-6 Astra的评测说明 4">[4]</a></sup></td><td>92.63%</td><td>100%</td><td>98%</td><td>100%</td><td>99%</td><td>88%</td><td>99%</td><td>85%</td><td>72%</td></tr><tr><td>Opus-4.7 / max.reasoning</td><td>82.4%</td><td>—</td><td>—</td><td>—</td><td>—</td><td>—</td><td>—</td><td>—</td><td>—</td></tr><tr><td>GPT-5.5</td><td>75.13%</td><td>81.0%</td><td>69.0%</td><td>94.0%</td><td>91.0%</td><td>75.0%</td><td>66.0%</td><td>70.00%</td><td>55.00%</td></tr><tr><td>RPent Flash Mode / Molmo2-8B<sup class="note-reference"><a href="#libero-pro-note-2" role="doc-noteref" aria-label="RPent Flash Mode的评测说明 2">[2]</a></sup></td><td>72.63%</td><td>79.00%</td><td>70.00%</td><td>86.00%</td><td>93.00%</td><td>74.00%</td><td>65.00%</td><td>60.00%</td><td>54.00%</td></tr><tr><td>Qwen3.6 27B / no-reasoning</td><td>70.63%</td><td>82.00%</td><td>78.00%</td><td>83.00%</td><td>84.00%</td><td>68.00%</td><td>68.00%</td><td>61.00%</td><td>41.00%</td></tr><tr><td>ASPIRE<sup class="note-reference"><a href="#libero-pro-note-1" role="doc-noteref" aria-label="ASPIRE的评测说明 1">[1]</a></sup></td><td>61.36%</td><td>60.0%</td><td>51.0%</td><td>95.0%</td><td>98.0%</td><td>45.0%</td><td>81.0%</td><td>38.3%</td><td>22.6%</td></tr><tr><td>π_RLinf</td><td>50.0%</td><td>42.0%</td><td>59.0%</td><td>71.0%</td><td>78.0%</td><td>45.0%</td><td>42.0%</td><td>49.0%</td><td>14.0%</td></tr><tr><td>π0.5</td><td>11.0%</td><td>1.0%</td><td>20.0%</td><td>1.0%</td><td>17.0%</td><td>2.0%</td><td>38.0%</td><td>1.0%</td><td>8.0%</td></tr><tr><td>AtomVLA</td><td>6.3%</td><td>1.0%</td><td>16.0%</td><td>0.0%</td><td>10.0%</td><td>11.0%</td><td>2.0%</td><td>9.0%</td><td>1.0%</td></tr><tr><td>X-VLA</td><td>3.8%</td><td>0.0%</td><td>0.0%</td><td>8.0%</td><td>2.0%</td><td>9.0%</td><td>1.0%</td><td>10.0%</td><td>0.0%</td></tr><tr><td>MolmoAct</td><td>1.5%</td><td>0.0%</td><td>0.0%</td><td>0.0%</td><td>6.0%</td><td>0.0%</td><td>0.0%</td><td>6.0%</td><td>0.0%</td></tr><tr><td>π0</td><td>0.3%</td><td>0.0%</td><td>0.0%</td><td>0.0%</td><td>2.0%</td><td>0.0%</td><td>0.0%</td><td>0.0%</td><td>0.0%</td></tr><tr><td>Cap-X</td><td>—</td><td>14.0%</td><td>12.0%</td><td>18.0%</td><td>22.0%</td><td>17.0%</td><td>26.0%</td><td>—</td><td>—</td></tr><tr><td>RPent / GPT-6 Motor Only<sup class="note-reference"><a href="#libero-pro-note-3" role="doc-noteref" aria-label="RPent / GPT-6 Motor Only的评测说明 3">[3]</a></sup></td><td>—</td><td>—</td><td>—</td><td>—</td><td>—</td><td>—</td><td>—</td><td>38.0%</td><td>—</td></tr><tr><td>RATS</td><td>—</td><td>31.0%</td><td>29.0%</td><td>63.0%</td><td>61.0%</td><td>36.0%</td><td>43.0%</td><td>—</td><td>—</td></tr></tbody></table></div><p id="libero-pro-note-1" class="scope-note" tabindex="-1" role="note">* <span class="note-number">[1]</span> ASPIRE：Long Task 和 Long Swap 使用 LIBERO-90 技能库进行 zero-shot 迁移。</p><p id="libero-pro-note-2" class="scope-note" tabindex="-1" role="note">* <span class="note-number">[2]</span> RPent Flash Mode 使用直接下载的、官方公开的 GPT-5.5 explore memory；Molmo2-8B 用于视觉定位。该测试结果使用了 s0-s9 中表现最好的 seed。</p><p id="libero-pro-note-3" class="scope-note" tabindex="-1" role="note">* <span class="note-number">[3]</span> RPent / GPT-6 Motor Only 为纯电机控制方法，通过 execute_action 直接输出末端位姿增量与夹爪指令，不调用 VLA / primitive，不加载 memory。</p><p id="libero-pro-astra-memory" class="scope-note memory-context" tabindex="-1" role="note">* <span class="note-number">[4]</span> GPT-6 Astra（使用 memory 的配置）：Long Task/Swap 与其余六套件使用各自探索后冻结的 memory 文件快照，评测期间不更新。Overall 合并两个不重叠批次：Long 157/200，加上其余套件 584/600，得到 741/800（92.63%）；并非全部回合共享同一份 memory 快照。</p></div></details></section>
 <section data-benchmark="standard-libero"><details data-module="standard-libero"><summary class="module-header"><h3>LIBERO</h3></summary><div id="native-standard-libero" class="module-content"><table><thead><tr><th>方法</th><th>成功率</th></tr></thead><tbody><tr><td>AtomVLA</td><td>97.0%</td></tr><tr><td>Claude Code / Opus-4.7 / max.reasoning</td><td>96.0%</td></tr><tr><td>π_RLinf</td><td>95.3%</td></tr><tr><td>π0</td><td>94.2%</td></tr><tr><td>NORA</td><td>79.5%</td></tr><tr><td>OpenVLA</td><td>76.5%</td></tr></tbody></table></div></details></section>
 <section data-benchmark="robocasa"><details data-module="robocasa"><summary class="module-header"><h3>RoboCasa365 · Target50</h3></summary><div id="native-robocasa" class="module-content"><table><thead><tr><th>方法</th><th>成功率</th></tr></thead><tbody><tr><td>Codex / GPT-6 Astra / low / reasoning</td><td>59.20%</td></tr><tr><td>Xiaomi-Robotics-1</td><td>57.4%</td></tr><tr><td>Codex / GPT-5.5 / xhigh / reasoning</td><td>57.1%</td></tr><tr><td>Claude Code / Opus-4.7 / max.reasoning</td><td>48.6%</td></tr><tr><td>WorldDreamer</td><td>35.3%</td></tr><tr><td>RLDX-1</td><td>30.0%</td></tr><tr><td>π0.5</td><td>16.9%</td></tr><tr><td>π0</td><td>14.8%</td></tr></tbody></table><h4>RoboCasa365 · Target50 · 完整方法与分项成绩</h4><div class="table-wrap" tabindex="0"><table><thead><tr><th>方法</th><th>总体</th><th>Atomic-Seen</th><th>Composite-Seen</th><th>Composite-Unseen</th></tr></thead><tbody><tr><td>GPT-6 Astra</td><td>59.20%</td><td>87.78%</td><td>43.75%</td><td>42.50%</td></tr><tr><td>Xiaomi-Robotics-1</td><td>57.4%</td><td>80.2%</td><td>57.1%</td><td>32.1%</td></tr><tr><td>GPT-5.5</td><td>57.1%</td><td>92.0%</td><td>61.0%</td><td>13.8%</td></tr><tr><td>Opus-4.7 / max.reasoning</td><td>48.6%</td><td>79.4%</td><td>47.5%</td><td>15.0%</td></tr><tr><td>WorldDreamer</td><td>35.3%</td><td>66.3%</td><td>26.7%</td><td>9.0%</td></tr><tr><td>RLDX-1</td><td>30.0%</td><td>60.0%</td><td>21.3%</td><td>5.0%</td></tr><tr><td>π0.5</td><td>16.9%</td><td>39.6%</td><td>7.1%</td><td>1.2%</td></tr><tr><td>π0</td><td>14.8%</td><td>34.6%</td><td>6.1%</td><td>1.1%</td></tr></tbody></table></div></div></details></section>
 <section data-benchmark="robotwin"><details data-module="robotwin"><summary class="module-header"><h3>RoboTwin</h3></summary><div id="native-robotwin" class="module-content"><table><thead><tr><th>方法</th><th>成功率</th></tr></thead><tbody><tr><td>Codex / GPT-5.5 / xhigh / reasoning</td><td>62.4%</td></tr><tr><td>Claude Code / Opus-4.7 / max.reasoning</td><td>58.4%</td></tr><tr><td>LingBot-VLA</td><td>50.4%</td></tr><tr><td>π0.5</td><td>47.9%</td></tr><tr><td>GR00T-N1.7</td><td>20.7%</td></tr><tr><td>StarVLA</td><td>10.6%</td></tr></tbody></table></div></details></section>
 </div></div></section>
-<section id="native-time-token-costs" class="cost-section" data-costs="true"><h2 class="page-section-title">耗时与 Token 开销</h2><div class="section-layout"><details class="module-directory" open><summary>环境</summary><nav class="module-nav" aria-label="耗时与 Token 开销"><a href="#native-costs-libero-pro">LIBERO-PRO</a><a href="#native-costs-robocasa">RoboCasa365</a><a href="#native-costs-robotwin">RoboTwin</a></nav></details><div class="section-results"><section class="cost-group" data-cost-group="libero-pro"><details data-module="costs-libero-pro"><summary class="module-header"><h3>LIBERO-PRO</h3></summary><div id="native-costs-libero-pro" class="module-content"><div class="table-wrap" tabindex="0"><table data-sort-key="costs-libero-pro" data-sortable="false" data-highlight-best="false"><thead><tr><th>方法</th><th>平均每回合耗时（秒）</th><th>总输出 token</th></tr></thead><tbody><tr data-cost-record="libero-pro--gpt-6-astra-low"><td class="cost-method">RPent / GPT-6 Astra<small> Codex · low · reasoning</small></td><td data-value="412.14">412.14</td><td data-value="3364938">3,364,938</td></tr><tr data-cost-record="libero-pro--gpt-5-6-xhigh"><td class="cost-method">RPent / GPT-5.6 Sol<small> Codex · xhigh · reasoning</small></td><td data-value="529.23">529.23</td><td data-value="3635426">3,635,426</td></tr><tr data-cost-record="libero-pro--gpt-5-6-no-reasoning"><td class="cost-method">RPent / GPT-5.6 Sol<small> Codex · no-reasoning</small></td><td data-value="344.17">344.17</td><td data-value="2569676">2,569,676</td></tr><tr data-cost-record="libero-pro--rpent-flash-mode"><td class="cost-method">RPent Flash Mode<small> Molmo2-8B · 视觉定位</small></td><td data-value="60.19">60.19</td><td data-value="0">0</td></tr><tr data-cost-record="libero-pro--qwen3-6-27b-no-reasoning"><td class="cost-method">RPent / Qwen3.6 27B<small> no-reasoning</small></td><td data-value="626.3">626.3</td><td data-value="3270754">3,270,754</td></tr></tbody></table></div></div></details></section><section class="cost-group" data-cost-group="robocasa"><details data-module="costs-robocasa"><summary class="module-header"><h3>RoboCasa365</h3></summary><div id="native-costs-robocasa" class="module-content"><div class="table-wrap" tabindex="0"><table data-sort-key="costs-robocasa" data-sortable="false" data-highlight-best="false"><thead><tr><th>方法</th><th>平均每回合耗时（秒）</th><th>总输出 token</th></tr></thead><tbody><tr data-cost-record="robocasa--gpt-6-astra-low"><td class="cost-method">RPent / GPT-6 Astra<small> Codex · low · reasoning</small></td><td data-value="1168.95">1,168.95</td><td data-value="5894436">5,894,436</td></tr><tr data-cost-record="robocasa--gpt-5-6-xhigh"><td class="cost-method">RPent / GPT-5.6 Sol<small> Codex · xhigh · reasoning</small></td><td data-value="1197.56">1,197.56</td><td data-value="5258391">5,258,391</td></tr><tr data-cost-record="robocasa--gpt-5-6-no-reasoning"><td class="cost-method">RPent / GPT-5.6 Sol<small> Codex · no-reasoning</small></td><td data-value="1013.21">1,013.21</td><td data-value="4792104">4,792,104</td></tr></tbody></table></div></div></details></section><section class="cost-group" data-cost-group="robotwin"><details data-module="costs-robotwin"><summary class="module-header"><h3>RoboTwin</h3></summary><div id="native-costs-robotwin" class="module-content"><div class="table-wrap" tabindex="0"><table data-sort-key="costs-robotwin" data-sortable="false" data-highlight-best="false"><thead><tr><th>方法</th><th>平均每回合耗时（秒）</th><th>总输出 token</th></tr></thead><tbody><tr data-cost-record="robotwin--gpt-6-astra-low"><td class="cost-method">RPent / GPT-6 Astra<small> Codex · low · reasoning</small></td><td data-value="1078.8">1,078.8</td><td data-value="2763641">2,763,641</td></tr><tr data-cost-record="robotwin--gpt-5-6-xhigh"><td class="cost-method">RPent / GPT-5.6 Sol<small> Codex · xhigh · reasoning</small></td><td data-value="1828.7">1,828.7</td><td data-value="4763915">4,763,915</td></tr><tr 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atus":"reported","rate":"61.00","successes":61,"episodes":100,"source_ids":["contributor-qwen-costs-20260918"],"protocol_id":"rpent-pro-qwen-eight-suites"},{"id":"long-swap--qwen3-6-27b-no-reasoning","view_id":"long-swap","configuration_id":"qwen3-6-27b-no-reasoning","status":"reported","rate":"41.00","successes":41,"episodes":100,"source_ids":["contributor-qwen-costs-20260918"],"protocol_id":"rpent-pro-qwen-eight-suites"},{"id":"long-task--gpt-6-astra-low-motor-only","view_id":"long-task","configuration_id":"gpt-6-astra-low-motor-only","status":"reported","rate":"38.0","successes":38,"episodes":100,"source_ids":["contributor-motor-only-long-task-20260920"],"protocol_id":"rpent-pro-motor-only"}],"sources":[{"id":"paper-table-2","url":"https://arxiv.org/html/2607.08448v4#S3.T2"},{"id":"paper-table-3","url":"https://arxiv.org/html/2607.08448v4#S3.T3"},{"id":"paper-table-4","url":"https://arxiv.org/html/2607.08448v4#S3.T4"},{"id":"paper-table-6","url":"https://arxiv.org/html/2607.08448v4#S3.T6"},{"id":"astra-long-20260907","report_sha256":"627d6d8a95fb1e10357961694867cd0bf0a2aa9011c96c8a33504b904cb96c80"},{"id":"astra-pro-remaining-20260913","report_sha256":"627d6d8a95fb1e10357961694867cd0bf0a2aa9011c96c8a33504b904cb96c80"},{"id":"contributor-20260915","kind":"contributor-confirmed"},{"id":"contributor-settings-20260915","kind":"contributor-confirmed"},{"id":"contributor-correction-20260916","kind":"contributor-confirmed","note":"Corrected model labels and aggregate scores; removed requested configurations. No missing suite scores inferred."},{"id":"repo-flash-mode-800","url":"https://github.com/RLinf/RPent/blob/c19727c7bbdd277cd86a985aae3f2741ba01df7a/docs/source-en/rst_source/usage/task_card.rst","note":"581 successes across all 800 LIBERO-PRO cases; missing source traces count as failures. Molmo is used for visual localization, not planning."},{"id":"repo-robotwin-20260915","url":"https://github.com/RLinf/RPent/blob/849143bf740a562367345cec0de9ef4657dafd73/docs/source-en/rst_source/usage/robotwin.rst"},{"id":"xiaomi-official-robocasa365","url":"https://robocasa.ai/leaderboard.html","submission_url":"https://github.com/robocasa-benchmark/leaderboard/blob/main/submissions_md/Xiaomi-Robotics_2026_07_08.md","checked_at":"2026-09-16","environment_split":"pretrain","note":"Published 50-task benchmark scores. Displayed alongside RPent by request; protocol equivalence has not been verified. Episode counts for this reported batch are unspecified here."},{"id":"xiaomi-paper-v2-table-3","url":"https://arxiv.org/html/2607.15330v2#S3.T3"},{"id":"aspire-table-2","url":"https://arxiv.org/html/2607.00272v1#A2.T2","note":"Table 2 main LIBERO-Pro results; Pos maps to Swap, Task maps to Task. Ten tasks and seeds 1-50 per suite. Published rounded rates are preserved, not converted to integer successes. Six-item Overall 72% is not an eight-suite Overall."},{"id":"aspire-table-5","url":"https://arxiv.org/html/2607.00272v1#A3.T5","note":"Table 5, N=90 zero-shot library transfer: Task 0.383, Pos 0.226. A different evaluation regime from the six main suites; no combined Overall is inferred."},{"id":"flash-published-family-counts","url":"https://github.com/RLinf/misc/blob/main/rpent/task_card/task_card_libero_pro_performance_time.png","note":"Exact printed titles: Spatial 149/200, Object 179/200, Goal 139/200, Long 114/200. Sum 581/800. Cross-check only; independent Task/Swap scores come from contributor outcome records, not digitized bar heights."},{"id":"molmo2-model-card","url":"https://huggingface.co/allenai/Molmo2-8B","note":"Official model identity. RPent uses Molmo2-8B for visual localization only."},{"id":"flash-contributor-800-outcomes","type":"contributor_archive","artifact":"task_card.zip","sha256":"a997e5a0f823abf2402e1374e22a068c43dfecc35a778a185d0274efd45e31e2","note":"Eight Task/Swap aggregates from 800 unique contributor result.json cases, checked against summary.json and published family totals. Each component includes all 100 expected cases (task IDs 0–9, seeds 0–9). The 20 no-card cases remain in the denominator and are not counted as successes.","aggregates":{"spatial_task":{"successes":79,"episodes":100,"outcomes":{"success":79,"failure":21},"rate":"79.00"},"spatial_swap":{"successes":70,"episodes":100,"outcomes":{"success":70,"failure":30},"rate":"70.00"},"object_task":{"successes":86,"episodes":100,"outcomes":{"success":86,"failure":14},"rate":"86.00"},"object_swap":{"successes":93,"episodes":100,"outcomes":{"success":93,"failure":7},"rate":"93.00"},"goal_task":{"successes":74,"episodes":100,"outcomes":{"failure":26,"success":74},"rate":"74.00"},"goal_swap":{"successes":65,"episodes":100,"outcomes":{"no_card":10,"failure":25,"success":65},"rate":"65.00"},"long_task":{"successes":60,"episodes":100,"outcomes":{"success":60,"failure":40},"rate":"60.00"},"long_swap":{"successes":54,"episodes":100,"outcomes":{"success":54,"failure":36,"no_card":10},"rate":"54.00"}}},{"id":"robocasa-astra-contributor-correction-20260920","kind":"contributor_correction","checked_at":"2026-09-20","protocol_url":"https://github.com/RLinf/RPent/blob/d14afccce78fb1bdbeba564fdd3ce7984c71bf2e/docs/source-en/rst_source/usage/robocasa.rst#harness-vla-target50-reproduction-protocol","environment_split":"target","tasks":50,"episodes":340,"groups":{"atomic":{"tasks":18,"episodes":180},"composite_seen":{"tasks":16,"episodes":80},"composite_unseen":{"tasks":16,"episodes":80}},"note":"The contributor corrected the full evaluation to 340 cells on 2026-09-20. Split denominators follow the existing RPent Target50 matrix (180/80/80). Reported success rates remain unchanged; full-run success counts are not inferred from rounded or task-weighted rates. The earlier archive is retained as historical local evidence, not as verification of the corrected full-run counts."},{"id":"contributor-qwen-costs-20260918","type":"contributor_report","note":"Contributor-provided Qwen eight-suite counts and cost table. Mean elapsed time includes all evaluation episodes (success, failure, and timeout); tokens count evaluation-stage output only. No unreported planner, hardware, or token accounting details are inferred."},{"id":"contributor-motor-only-long-task-20260920","type":"contributor_report","note":"Contributor-reported complete Long Task evaluation on another server: 38 successes in 100 episodes. Codex / GPT-6 Astra / low reasoning; execute_action motor-only control without VLA / primitives or memory. This aggregate is independent of the incomplete local motor-only campaign; no unreported suite, runtime, or token measurements are inferred."},{"id":"repo-libero-long-reproduction-20260922","url":"https://github.com/RLinf/RPent/blob/15420b438e3adbf82e6640799a0668a2609e3bb7/docs/source-en/rst_source/usage/libero.rst#reproducing-results","note":"Codex / GPT-5.5 / xhigh reproduction on reproduce/libero reports Long Task 70/100 and Long Swap 55/100. The updated leaderboard combines these two results with the six unchanged Table 3 suites: (81+69+94+91+75+66+70+55)/8 = 75.125%, displayed as 75.13%. This derived Overall is not the paper's original aggregate."},{"id":"flash-memory-confirmation-20260922","kind":"contributor-confirmed","url":"https://huggingface.co/datasets/RLinf/RPent-memory","note":"The contributor confirms that the displayed Flash Mode evaluation uses directly downloaded, officially released GPT-5.5 exploration memory and the best-performing seed from s0-s9. Molmo2-8B is used for visual localization. No memory snapshot revision is inferred."}],"astra":{"completed":800,"successes":741,"failures":59,"overall_rate":"92.63","memory_batches":[{"campaign_id":"libero_long_gpt6_astra_20260907","suites":["long-task","long-swap"]},{"campaign_id":"libero_pro_remaining_gpt6_astra_20260913","suites":["spatial-task","spatial-swap","object-task","object-swap","goal-task","goal-swap"],"manifest_sha256":"f08702d7c13d9b40a3127b64765ad4d336d022be028396d5f873d7fc4c4c4928"}],"suites":[{"view_id":"spatial-task","suite":"Spatial Task","dataset":"libero_spatial_task","completed":100,"planned":100,"successes":100,"failures":0,"remaining":0,"rate":"100.00","status":"complete"},{"view_id":"spatial-swap","suite":"Spatial Swap","dataset":"libero_spatial_swap","completed":100,"planned":100,"successes":98,"failures":2,"remaining":0,"rate":"98.00","status":"complete"},{"view_id":"object-task","suite":"Object Task","dataset":"libero_object_task","completed":100,"planned":100,"successes":100,"failures":0,"remaining":0,"rate":"100.00","status":"complete"},{"view_id":"object-swap","suite":"Object Swap","dataset":"libero_object_swap","completed":100,"planned":100,"successes":99,"failures":1,"remaining":0,"rate":"99.00","status":"complete"},{"view_id":"goal-task","suite":"Goal Task","dataset":"libero_goal_task","completed":100,"planned":100,"successes":88,"failures":12,"remaining":0,"rate":"88.00","status":"complete"},{"view_id":"goal-swap","suite":"Goal Swap","dataset":"libero_goal_swap","completed":100,"planned":100,"successes":99,"failures":1,"remaining":0,"rate":"99.00","status":"complete"},{"view_id":"long-task","suite":"Long Task","dataset":"libero_10_task","completed":100,"planned":100,"successes":85,"failures":15,"remaining":0,"rate":"85.00","status":"complete"},{"view_id":"long-swap","suite":"Long Swap","dataset":"libero_10_swap","completed":100,"planned":100,"successes":72,"failures":28,"remaining":0,"rate":"72.00","status":"complete"}]},"sections":[{"id":"libero-pro","name":"LIBERO-PRO","heading":{"en":"Eight-suite evaluation","zh":"八套件评测"},"summary":{"en":"Astra · low · reasoning: 92.63% (741/800)","zh":"Astra · low · reasoning：92.63%（741/800）"},"context":{"en":"* GPT-6 Astra (memory-enabled configuration): Long Task/Swap and the other six suites use separate memory-file snapshots frozen after their respective exploration phases, with no updates during evaluation. Overall combines two non-overlapping batches: Long 157/200 plus the other suites 584/600, giving 741/800 (92.63%); the 800 episodes do not share a single memory snapshot.","zh":"* GPT-6 Astra（使用 memory 的配置）：Long Task/Swap 与其余六套件使用各自探索后冻结的 memory 文件快照，评测期间不更新。Overall 合并两个不重叠批次：Long 157/200，加上其余套件 584/600，得到 741/800（92.63%）；并非全部回合共享同一份 memory 快照。"},"default_view":"libero-pro","views":["libero-pro","spatial-task","spatial-swap","object-task","object-swap","goal-task","goal-swap","long-task","long-swap"],"notes":{"en":"* ASPIRE: Long Task and Long Swap use zero-shot transfer from the LIBERO-90 skill library.\n\n* RPent Flash Mode uses directly downloaded, officially released GPT-5.5 exploration memory; Molmo2-8B is used for visual localization. These results use the best-performing seed from s0-s9.\n\n* RPent / GPT-6 Motor Only uses motor-only control: it directly outputs end-effector pose increments and gripper commands through execute_action, without invoking VLA / primitives or loading memory.","zh":"* ASPIRE：Long Task 和 Long Swap 使用 LIBERO-90 技能库进行 zero-shot 迁移。\n\n* RPent Flash Mode 使用直接下载的、官方公开的 GPT-5.5 explore memory；Molmo2-8B 用于视觉定位。该测试结果使用了 s0-s9 中表现最好的 seed。\n\n* RPent / GPT-6 Motor Only 为纯电机控制方法，通过 execute_action 直接输出末端位姿增量与夹爪指令，不调用 VLA / primitive，不加载 memory。"}},{"id":"standard-libero","name":"LIBERO","heading":{"en":"Four-suite evaluation","zh":"四套件评测"},"default_view":"standard-libero","views":["standard-libero","standard-spatial","standard-object","standard-goal","standard-long"]},{"id":"robocasa","name":"RoboCasa365 · Target50","heading":{"en":"50 tasks · task-weighted Overall","zh":"50 个任务 · Overall 按任务等权"},"default_view":"robocasa","views":["robocasa","robocasa-atomic-seen","robocasa-composite-seen","robocasa-composite-unseen"]},{"id":"robotwin","name":"RoboTwin","heading":{"en":"Clean → Randomized","zh":"Clean → Randomized"},"summary":{"en":"50 tasks · 250 episodes","zh":"50 个任务 · 250 回合"},"default_view":"robotwin","views":["robotwin"]}],"assets":["ellipsis.svg","download.svg","arrow-down.svg"],"cost_method_order":["gpt-6-astra-low","gpt-5-6-xhigh","gpt-5-6-no-reasoning","rpent-flash-mode","qwen3-6-27b-no-reasoning"],"cost_results":[{"id":"libero-pro--gpt-5-6-xhigh","benchmark_id":"libero-pro","configuration_id":"gpt-5-6-xhigh","mean_elapsed_seconds":529.23,"total_output_tokens":3635426,"source_ids":["contributor-qwen-costs-20260918"]},{"id":"libero-pro--gpt-5-6-no-reasoning","benchmark_id":"libero-pro","configuration_id":"gpt-5-6-no-reasoning","mean_elapsed_seconds":344.17,"total_output_tokens":2569676,"source_ids":["contributor-qwen-costs-20260918"]},{"id":"libero-pro--gpt-6-astra-low","benchmark_id":"libero-pro","configuration_id":"gpt-6-astra-low","mean_elapsed_seconds":412.14,"total_output_tokens":3364938,"source_ids":["contributor-qwen-costs-20260918"]},{"id":"libero-pro--qwen3-6-27b-no-reasoning","benchmark_id":"libero-pro","configuration_id":"qwen3-6-27b-no-reasoning","mean_elapsed_seconds":626.3,"total_output_tokens":3270754,"source_ids":["contributor-qwen-costs-20260918"]},{"id":"libero-pro--rpent-flash-mode","benchmark_id":"libero-pro","configuration_id":"rpent-flash-mode","mean_elapsed_seconds":60.19,"total_output_tokens":0,"source_ids":["contributor-qwen-costs-20260918"]},{"id":"robocasa--gpt-5-6-xhigh","benchmark_id":"robocasa","configuration_id":"gpt-5-6-xhigh","mean_elapsed_seconds":1197.56,"total_output_tokens":5258391,"source_ids":["contributor-qwen-costs-20260918"]},{"id":"robocasa--gpt-5-6-no-reasoning","benchmark_id":"robocasa","configuration_id":"gpt-5-6-no-reasoning","mean_elapsed_seconds":1013.21,"total_output_tokens":4792104,"source_ids":["contributor-qwen-costs-20260918"]},{"id":"robocasa--gpt-6-astra-low","benchmark_id":"robocasa","configuration_id":"gpt-6-astra-low","mean_elapsed_seconds":1168.95,"total_output_tokens":5894436,"source_ids":["contributor-qwen-costs-20260918"]},{"id":"robotwin--gpt-5-6-xhigh","benchmark_id":"robotwin","configuration_id":"gpt-5-6-xhigh","mean_elapsed_seconds":1828.7,"total_output_tokens":4763915,"source_ids":["contributor-qwen-costs-20260918"]},{"id":"robotwin--gpt-5-6-no-reasoning","benchmark_id":"robotwin","configuration_id":"gpt-5-6-no-reasoning","mean_elapsed_seconds":957.4,"total_output_tokens":2456401,"source_ids":["contributor-qwen-costs-20260918"]},{"id":"robotwin--gpt-6-astra-low","benchmark_id":"robotwin","configuration_id":"gpt-6-astra-low","mean_elapsed_seconds":1078.8,"total_output_tokens":2763641,"source_ids":["contributor-qwen-costs-20260918"]}]}</script><script src="table-sort.js"></script><script src="leaderboard.js"></script></body></html>
+<section id="native-time-token-costs" class="cost-section" data-costs="true"><h2 class="page-section-title">耗时与 Token 开销</h2><div class="section-layout"><details class="module-directory" open><summary>环境</summary><nav class="module-nav" aria-label="耗时与 Token 开销"><a href="#native-costs-libero-pro">LIBERO-PRO</a><a href="#native-costs-robocasa">RoboCasa365</a><a href="#native-costs-robotwin">RoboTwin</a></nav></details><div class="section-results"><section class="cost-group" data-cost-group="libero-pro"><details data-module="costs-libero-pro"><summary class="module-header"><h3>LIBERO-PRO</h3></summary><div id="native-costs-libero-pro" class="module-content"><div class="table-wrap" tabindex="0"><table data-sort-key="costs-libero-pro" data-sortable="false" data-highlight-best="false"><thead><tr><th>方法</th><th>平均每回合耗时（秒）</th><th>总输出 token</th></tr></thead><tbody><tr data-cost-record="libero-pro--gpt-6-astra-low"><td class="cost-method">RPent / GPT-6 Astra<small> Codex · low · reasoning</small></td><td data-value="412.14">412.14</td><td data-value="3364938">3,364,938</td></tr><tr data-cost-record="libero-pro--gpt-5-6-xhigh"><td class="cost-method">RPent / GPT-5.6 Sol<small> Codex · xhigh · reasoning</small></td><td data-value="529.23">529.23</td><td data-value="3635426">3,635,426</td></tr><tr data-cost-record="libero-pro--gpt-5-6-no-reasoning"><td class="cost-method">RPent / GPT-5.6 Sol<small> Codex · no-reasoning</small></td><td data-value="344.17">344.17</td><td data-value="2569676">2,569,676</td></tr><tr data-cost-record="libero-pro--rpent-flash-mode"><td class="cost-method">RPent Flash Mode<small> Molmo2-8B · 视觉定位</small></td><td data-value="60.19">60.19</td><td data-value="0">0</td></tr><tr data-cost-record="libero-pro--qwen3-6-27b-no-reasoning"><td class="cost-method">RPent / Qwen3.6 27B<small> no-reasoning</small></td><td data-value="626.3">626.3</td><td data-value="3270754">3,270,754</td></tr></tbody></table></div></div></details></section><section class="cost-group" data-cost-group="robocasa"><details data-module="costs-robocasa"><summary class="module-header"><h3>RoboCasa365</h3></summary><div id="native-costs-robocasa" class="module-content"><div class="table-wrap" tabindex="0"><table data-sort-key="costs-robocasa" data-sortable="false" data-highlight-best="false"><thead><tr><th>方法</th><th>平均每回合耗时（秒）</th><th>总输出 token</th></tr></thead><tbody><tr data-cost-record="robocasa--gpt-6-astra-low"><td class="cost-method">RPent / GPT-6 Astra<small> Codex · low · reasoning</small></td><td data-value="1168.95">1,168.95</td><td data-value="5894436">5,894,436</td></tr><tr data-cost-record="robocasa--gpt-5-6-xhigh"><td class="cost-method">RPent / GPT-5.6 Sol<small> Codex · xhigh · reasoning</small></td><td data-value="1197.56">1,197.56</td><td data-value="5258391">5,258,391</td></tr><tr data-cost-record="robocasa--gpt-5-6-no-reasoning"><td class="cost-method">RPent / GPT-5.6 Sol<small> Codex · no-reasoning</small></td><td data-value="1013.21">1,013.21</td><td data-value="4792104">4,792,104</td></tr></tbody></table></div></div></details></section><section class="cost-group" data-cost-group="robotwin"><details data-module="costs-robotwin"><summary class="module-header"><h3>RoboTwin</h3></summary><div id="native-costs-robotwin" class="module-content"><div class="table-wrap" tabindex="0"><table data-sort-key="costs-robotwin" data-sortable="false" data-highlight-best="false"><thead><tr><th>方法</th><th>平均每回合耗时（秒）</th><th>总输出 token</th></tr></thead><tbody><tr data-cost-record="robotwin--gpt-6-astra-low"><td class="cost-method">RPent / GPT-6 Astra<small> Codex · low · reasoning</small></td><td data-value="1078.8">1,078.8</td><td data-value="2763641">2,763,641</td></tr><tr data-cost-record="robotwin--gpt-5-6-xhigh"><td class="cost-method">RPent / GPT-5.6 Sol<small> Codex · xhigh · reasoning</small></td><td data-value="1828.7">1,828.7</td><td data-value="4763915">4,763,915</td></tr><tr data-cost-record="robotwin--gpt-5-6-no-reasoning"><td class="cost-method">RPent / GPT-5.6 Sol<small> Codex · no-reasoning</small></td><td data-value="957.4">957.4</td><td data-value="2456401">2,456,401</td></tr></tbody></table></div></div></details></section></div></div></section><div class="leaderboard-footer"><p class="scope-note evaluation-notice">* Leaderboard 中所有 RPent 方法均展示 2026.09 测试结果。</p><span>RPent Leaderboard</span></div></div></noscript><script id="results-data" type="application/json">{"schema_version":2,"checked_at":"2026-09-22","evaluation_notice":{"en":"* All RPent method results shown in this leaderboard are from September 2026 evaluations.","zh":"* Leaderboard 中所有 RPent 方法均展示 2026.09 测试结果。"},"paper_revision":"2607.08448v4","repository_revision":"43f32aa08cba07bd4d49a4bfa5eba4ef633e9b92","configurations":[{"id":"gpt-5-5-xhigh","kind":"rpent","backend":"Codex","model":"GPT-5.5","reasoning":true,"effort":"xhigh"},{"id":"opus-4-7-max","kind":"rpent","backend":"Claude 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atus":"reported","rate":"61.00","successes":61,"episodes":100,"source_ids":["contributor-qwen-costs-20260918"],"protocol_id":"rpent-pro-qwen-eight-suites"},{"id":"long-swap--qwen3-6-27b-no-reasoning","view_id":"long-swap","configuration_id":"qwen3-6-27b-no-reasoning","status":"reported","rate":"41.00","successes":41,"episodes":100,"source_ids":["contributor-qwen-costs-20260918"],"protocol_id":"rpent-pro-qwen-eight-suites"},{"id":"long-task--gpt-6-astra-low-motor-only","view_id":"long-task","configuration_id":"gpt-6-astra-low-motor-only","status":"reported","rate":"38.0","successes":38,"episodes":100,"source_ids":["contributor-motor-only-long-task-20260920"],"protocol_id":"rpent-pro-motor-only"}],"sources":[{"id":"paper-table-2","url":"https://arxiv.org/html/2607.08448v4#S3.T2"},{"id":"paper-table-3","url":"https://arxiv.org/html/2607.08448v4#S3.T3"},{"id":"paper-table-4","url":"https://arxiv.org/html/2607.08448v4#S3.T4"},{"id":"paper-table-6","url":"https://arxiv.org/html/2607.08448v4#S3.T6"},{"id":"astra-long-20260907","report_sha256":"627d6d8a95fb1e10357961694867cd0bf0a2aa9011c96c8a33504b904cb96c80"},{"id":"astra-pro-remaining-20260913","report_sha256":"627d6d8a95fb1e10357961694867cd0bf0a2aa9011c96c8a33504b904cb96c80"},{"id":"contributor-20260915","kind":"contributor-confirmed"},{"id":"contributor-settings-20260915","kind":"contributor-confirmed"},{"id":"contributor-correction-20260916","kind":"contributor-confirmed","note":"Corrected model labels and aggregate scores; removed requested configurations. No missing suite scores inferred."},{"id":"repo-flash-mode-800","url":"https://github.com/RLinf/RPent/blob/c19727c7bbdd277cd86a985aae3f2741ba01df7a/docs/source-en/rst_source/usage/task_card.rst","note":"581 successes across all 800 LIBERO-PRO cases; missing source traces count as failures. Molmo is used for visual localization, not planning."},{"id":"repo-robotwin-20260915","url":"https://github.com/RLinf/RPent/blob/849143bf740a562367345cec0de9ef4657dafd73/docs/source-en/rst_source/usage/robotwin.rst"},{"id":"xiaomi-official-robocasa365","url":"https://robocasa.ai/leaderboard.html","submission_url":"https://github.com/robocasa-benchmark/leaderboard/blob/main/submissions_md/Xiaomi-Robotics_2026_07_08.md","checked_at":"2026-09-16","environment_split":"pretrain","note":"Published 50-task benchmark scores. Displayed alongside RPent by request; protocol equivalence has not been verified. Episode counts for this reported batch are unspecified here."},{"id":"xiaomi-paper-v2-table-3","url":"https://arxiv.org/html/2607.15330v2#S3.T3"},{"id":"aspire-table-2","url":"https://arxiv.org/html/2607.00272v1#A2.T2","note":"Table 2 main LIBERO-Pro results; Pos maps to Swap, Task maps to Task. Ten tasks and seeds 1-50 per suite. Published rounded rates are preserved, not converted to integer successes. Six-item Overall 72% is not an eight-suite Overall."},{"id":"aspire-table-5","url":"https://arxiv.org/html/2607.00272v1#A3.T5","note":"Table 5, N=90 zero-shot library transfer: Task 0.383, Pos 0.226. A different evaluation regime from the six main suites; no combined Overall is inferred."},{"id":"flash-published-family-counts","url":"https://github.com/RLinf/misc/blob/main/rpent/task_card/task_card_libero_pro_performance_time.png","note":"Exact printed titles: Spatial 149/200, Object 179/200, Goal 139/200, Long 114/200. Sum 581/800. Cross-check only; independent Task/Swap scores come from contributor outcome records, not digitized bar heights."},{"id":"molmo2-model-card","url":"https://huggingface.co/allenai/Molmo2-8B","note":"Official model identity. RPent uses Molmo2-8B for visual localization only."},{"id":"flash-contributor-800-outcomes","type":"contributor_archive","artifact":"task_card.zip","sha256":"a997e5a0f823abf2402e1374e22a068c43dfecc35a778a185d0274efd45e31e2","note":"Eight Task/Swap aggregates from 800 unique contributor result.json cases, checked against summary.json and published family totals. Each component includes all 100 expected cases (task IDs 0–9, seeds 0–9). The 20 no-card cases remain in the denominator and are not counted as successes.","aggregates":{"spatial_task":{"successes":79,"episodes":100,"outcomes":{"success":79,"failure":21},"rate":"79.00"},"spatial_swap":{"successes":70,"episodes":100,"outcomes":{"success":70,"failure":30},"rate":"70.00"},"object_task":{"successes":86,"episodes":100,"outcomes":{"success":86,"failure":14},"rate":"86.00"},"object_swap":{"successes":93,"episodes":100,"outcomes":{"success":93,"failure":7},"rate":"93.00"},"goal_task":{"successes":74,"episodes":100,"outcomes":{"failure":26,"success":74},"rate":"74.00"},"goal_swap":{"successes":65,"episodes":100,"outcomes":{"no_card":10,"failure":25,"success":65},"rate":"65.00"},"long_task":{"successes":60,"episodes":100,"outcomes":{"success":60,"failure":40},"rate":"60.00"},"long_swap":{"successes":54,"episodes":100,"outcomes":{"success":54,"failure":36,"no_card":10},"rate":"54.00"}}},{"id":"robocasa-astra-contributor-correction-20260920","kind":"contributor_correction","checked_at":"2026-09-20","protocol_url":"https://github.com/RLinf/RPent/blob/d14afccce78fb1bdbeba564fdd3ce7984c71bf2e/docs/source-en/rst_source/usage/robocasa.rst#harness-vla-target50-reproduction-protocol","environment_split":"target","tasks":50,"episodes":340,"groups":{"atomic":{"tasks":18,"episodes":180},"composite_seen":{"tasks":16,"episodes":80},"composite_unseen":{"tasks":16,"episodes":80}},"note":"The contributor corrected the full evaluation to 340 cells on 2026-09-20. Split denominators follow the existing RPent Target50 matrix (180/80/80). Reported success rates remain unchanged; full-run success counts are not inferred from rounded or task-weighted rates. The earlier archive is retained as historical local evidence, not as verification of the corrected full-run counts."},{"id":"contributor-qwen-costs-20260918","type":"contributor_report","note":"Contributor-provided Qwen eight-suite counts and cost table. Mean elapsed time includes all evaluation episodes (success, failure, and timeout); tokens count evaluation-stage output only. No unreported planner, hardware, or token accounting details are inferred."},{"id":"contributor-motor-only-long-task-20260920","type":"contributor_report","note":"Contributor-reported complete Long Task evaluation on another server: 38 successes in 100 episodes. Codex / GPT-6 Astra / low reasoning; execute_action motor-only control without VLA / primitives or memory. This aggregate is independent of the incomplete local motor-only campaign; no unreported suite, runtime, or token measurements are inferred."},{"id":"repo-libero-long-reproduction-20260922","url":"https://github.com/RLinf/RPent/blob/15420b438e3adbf82e6640799a0668a2609e3bb7/docs/source-en/rst_source/usage/libero.rst#reproducing-results","note":"Codex / GPT-5.5 / xhigh reproduction on reproduce/libero reports Long Task 70/100 and Long Swap 55/100. The updated leaderboard combines these two results with the six unchanged Table 3 suites: (81+69+94+91+75+66+70+55)/8 = 75.125%, displayed as 75.13%. This derived Overall is not the paper's original aggregate."},{"id":"flash-memory-confirmation-20260922","kind":"contributor-confirmed","url":"https://huggingface.co/datasets/RLinf/RPent-memory","note":"The contributor confirms that the displayed Flash Mode evaluation uses directly downloaded, officially released GPT-5.5 exploration memory and the best-performing seed from s0-s9. Molmo2-8B is used for visual localization. No memory snapshot revision is inferred."}],"astra":{"completed":800,"successes":741,"failures":59,"overall_rate":"92.63","memory_batches":[{"campaign_id":"libero_long_gpt6_astra_20260907","suites":["long-task","long-swap"]},{"campaign_id":"libero_pro_remaining_gpt6_astra_20260913","suites":["spatial-task","spatial-swap","object-task","object-swap","goal-task","goal-swap"],"manifest_sha256":"f08702d7c13d9b40a3127b64765ad4d336d022be028396d5f873d7fc4c4c4928"}],"suites":[{"view_id":"spatial-task","suite":"Spatial Task","dataset":"libero_spatial_task","completed":100,"planned":100,"successes":100,"failures":0,"remaining":0,"rate":"100.00","status":"complete"},{"view_id":"spatial-swap","suite":"Spatial Swap","dataset":"libero_spatial_swap","completed":100,"planned":100,"successes":98,"failures":2,"remaining":0,"rate":"98.00","status":"complete"},{"view_id":"object-task","suite":"Object Task","dataset":"libero_object_task","completed":100,"planned":100,"successes":100,"failures":0,"remaining":0,"rate":"100.00","status":"complete"},{"view_id":"object-swap","suite":"Object Swap","dataset":"libero_object_swap","completed":100,"planned":100,"successes":99,"failures":1,"remaining":0,"rate":"99.00","status":"complete"},{"view_id":"goal-task","suite":"Goal Task","dataset":"libero_goal_task","completed":100,"planned":100,"successes":88,"failures":12,"remaining":0,"rate":"88.00","status":"complete"},{"view_id":"goal-swap","suite":"Goal Swap","dataset":"libero_goal_swap","completed":100,"planned":100,"successes":99,"failures":1,"remaining":0,"rate":"99.00","status":"complete"},{"view_id":"long-task","suite":"Long Task","dataset":"libero_10_task","completed":100,"planned":100,"successes":85,"failures":15,"remaining":0,"rate":"85.00","status":"complete"},{"view_id":"long-swap","suite":"Long Swap","dataset":"libero_10_swap","completed":100,"planned":100,"successes":72,"failures":28,"remaining":0,"rate":"72.00","status":"complete"}]},"sections":[{"id":"libero-pro","name":"LIBERO-PRO","heading":{"en":"Eight-suite evaluation","zh":"八套件评测"},"summary":{"en":"Astra · low · reasoning: 92.63% (741/800)","zh":"Astra · low · reasoning：92.63%（741/800）"},"context":{"en":"* GPT-6 Astra (memory-enabled configuration): Long Task/Swap and the other six suites use separate memory-file snapshots frozen after their respective exploration phases, with no updates during evaluation. Overall combines two non-overlapping batches: Long 157/200 plus the other suites 584/600, giving 741/800 (92.63%); the 800 episodes do not share a single memory snapshot.","zh":"* GPT-6 Astra（使用 memory 的配置）：Long Task/Swap 与其余六套件使用各自探索后冻结的 memory 文件快照，评测期间不更新。Overall 合并两个不重叠批次：Long 157/200，加上其余套件 584/600，得到 741/800（92.63%）；并非全部回合共享同一份 memory 快照。"},"default_view":"libero-pro","views":["libero-pro","spatial-task","spatial-swap","object-task","object-swap","goal-task","goal-swap","long-task","long-swap"],"notes":{"en":"* ASPIRE: Long Task and Long Swap use zero-shot transfer from the LIBERO-90 skill library.\n\n* RPent Flash Mode uses directly downloaded, officially released GPT-5.5 exploration memory; Molmo2-8B is used for visual localization. These results use the best-performing seed from s0-s9.\n\n* RPent / GPT-6 Motor Only uses motor-only control: it directly outputs end-effector pose increments and gripper commands through execute_action, without invoking VLA / primitives or loading memory.","zh":"* ASPIRE：Long Task 和 Long Swap 使用 LIBERO-90 技能库进行 zero-shot 迁移。\n\n* RPent Flash Mode 使用直接下载的、官方公开的 GPT-5.5 explore memory；Molmo2-8B 用于视觉定位。该测试结果使用了 s0-s9 中表现最好的 seed。\n\n* RPent / GPT-6 Motor Only 为纯电机控制方法，通过 execute_action 直接输出末端位姿增量与夹爪指令，不调用 VLA / primitive，不加载 memory。"}},{"id":"standard-libero","name":"LIBERO","heading":{"en":"Four-suite evaluation","zh":"四套件评测"},"default_view":"standard-libero","views":["standard-libero","standard-spatial","standard-object","standard-goal","standard-long"]},{"id":"robocasa","name":"RoboCasa365 · Target50","heading":{"en":"50 tasks · task-weighted Overall","zh":"50 个任务 · Overall 按任务等权"},"default_view":"robocasa","views":["robocasa","robocasa-atomic-seen","robocasa-composite-seen","robocasa-composite-unseen"]},{"id":"robotwin","name":"RoboTwin","heading":{"en":"Clean → Randomized","zh":"Clean → Randomized"},"summary":{"en":"50 tasks · 250 episodes","zh":"50 个任务 · 250 回合"},"default_view":"robotwin","views":["robotwin"]}],"assets":["ellipsis.svg","download.svg","arrow-down.svg"],"cost_method_order":["gpt-6-astra-low","gpt-5-6-xhigh","gpt-5-6-no-reasoning","rpent-flash-mode","qwen3-6-27b-no-reasoning"],"cost_results":[{"id":"libero-pro--gpt-5-6-xhigh","benchmark_id":"libero-pro","configuration_id":"gpt-5-6-xhigh","mean_elapsed_seconds":529.23,"total_output_tokens":3635426,"source_ids":["contributor-qwen-costs-20260918"]},{"id":"libero-pro--gpt-5-6-no-reasoning","benchmark_id":"libero-pro","configuration_id":"gpt-5-6-no-reasoning","mean_elapsed_seconds":344.17,"total_output_tokens":2569676,"source_ids":["contributor-qwen-costs-20260918"]},{"id":"libero-pro--gpt-6-astra-low","benchmark_id":"libero-pro","configuration_id":"gpt-6-astra-low","mean_elapsed_seconds":412.14,"total_output_tokens":3364938,"source_ids":["contributor-qwen-costs-20260918"]},{"id":"libero-pro--qwen3-6-27b-no-reasoning","benchmark_id":"libero-pro","configuration_id":"qwen3-6-27b-no-reasoning","mean_elapsed_seconds":626.3,"total_output_tokens":3270754,"source_ids":["contributor-qwen-costs-20260918"]},{"id":"libero-pro--rpent-flash-mode","benchmark_id":"libero-pro","configuration_id":"rpent-flash-mode","mean_elapsed_seconds":60.19,"total_output_tokens":0,"source_ids":["contributor-qwen-costs-20260918"]},{"id":"robocasa--gpt-5-6-xhigh","benchmark_id":"robocasa","configuration_id":"gpt-5-6-xhigh","mean_elapsed_seconds":1197.56,"total_output_tokens":5258391,"source_ids":["contributor-qwen-costs-20260918"]},{"id":"robocasa--gpt-5-6-no-reasoning","benchmark_id":"robocasa","configuration_id":"gpt-5-6-no-reasoning","mean_elapsed_seconds":1013.21,"total_output_tokens":4792104,"source_ids":["contributor-qwen-costs-20260918"]},{"id":"robocasa--gpt-6-astra-low","benchmark_id":"robocasa","configuration_id":"gpt-6-astra-low","mean_elapsed_seconds":1168.95,"total_output_tokens":5894436,"source_ids":["contributor-qwen-costs-20260918"]},{"id":"robotwin--gpt-5-6-xhigh","benchmark_id":"robotwin","configuration_id":"gpt-5-6-xhigh","mean_elapsed_seconds":1828.7,"total_output_tokens":4763915,"source_ids":["contributor-qwen-costs-20260918"]},{"id":"robotwin--gpt-5-6-no-reasoning","benchmark_id":"robotwin","configuration_id":"gpt-5-6-no-reasoning","mean_elapsed_seconds":957.4,"total_output_tokens":2456401,"source_ids":["contributor-qwen-costs-20260918"]},{"id":"robotwin--gpt-6-astra-low","benchmark_id":"robotwin","configuration_id":"gpt-6-astra-low","mean_elapsed_seconds":1078.8,"total_output_tokens":2763641,"source_ids":["contributor-qwen-costs-20260918"]}]}</script><script src="table-sort.js"></script><script src="leaderboard.js"></script></body></html>
diff --git a/rpent/benchmarks/leaderboard-en-dark.png b/rpent/benchmarks/leaderboard-en-dark.png
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diff --git a/rpent/benchmarks/leaderboard-en-light.png b/rpent/benchmarks/leaderboard-en-light.png
index 75c4a36..7016c7c 100644
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diff --git a/rpent/benchmarks/leaderboard-zh-dark.png b/rpent/benchmarks/leaderboard-zh-dark.png
index 32af3cc..94474b6 100644
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diff --git a/rpent/benchmarks/leaderboard-zh-light.png b/rpent/benchmarks/leaderboard-zh-light.png
index a172fa7..6e1c568 100644
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diff --git a/rpent/benchmarks/leaderboard.css b/rpent/benchmarks/leaderboard.css
index 2a04c2f..5f0e2d2 100644
--- a/rpent/benchmarks/leaderboard.css
+++ b/rpent/benchmarks/leaderboard.css
@@ -92,6 +92,9 @@ button:focus-visible,a:focus-visible,select:focus-visible,summary:focus-visible,
 @media(max-width:700px){.page-header .brand{font-size:20px}.page-header .masthead{flex-wrap:wrap;gap:12px}.page-titles{flex-basis:100%}.page-section-title,.cost-section .page-section-title{font-size:32px}.section-nav{font-size:18px}.benchmark-wordmark,[data-benchmark] .module-header h3,.cost-group h3{font-size:22px}}
 @media print{.section-nav,.module-directory{display:none}.section-layout{display:block}.module-header{position:static}}
 footer{padding-inline:max(var(--gutter),calc((100% - var(--content-max))/2 + var(--gutter)))}
+footer{align-items:flex-end}.footer-context{flex:1;min-width:0}.footer-context>[data-i18n="title"]{display:block;font-size:17px}footer .evaluation-notice{margin:0 0 12px}
+.leaderboard-footer{margin-top:28px;padding-bottom:24px;color:var(--muted)}.leaderboard-footer>span{display:block;font-size:17px}.leaderboard-footer .evaluation-notice{margin:0 0 12px}
+@media print{footer{display:block!important}footer .footer-context>[data-i18n="title"],footer>a{display:none!important}}
 noscript .section-layout{padding-inline:0}noscript .module-header{position:static}
 noscript .brand{font-size:24px;font-weight:400}noscript .page-section-title{padding:12px 0 24px}
 @media(max-width:700px){noscript .brand{font-size:20px}}
diff --git a/rpent/benchmarks/leaderboard.js b/rpent/benchmarks/leaderboard.js
index 7a93427..c343c45 100644
--- a/rpent/benchmarks/leaderboard.js
+++ b/rpent/benchmarks/leaderboard.js
@@ -188,6 +188,7 @@
     (options.embedded?document.host:document.documentElement).lang=lang==='en'?'en':'zh-CN';
     if(!options.embedded)document.title=t('title');
     document.querySelectorAll('[data-i18n]').forEach(el=>el.textContent=t(el.dataset.i18n));
+    document.querySelectorAll('[data-evaluation-notice]').forEach(el=>el.textContent=tr(d.evaluation_notice));
     document.querySelectorAll('[data-i18n-label]').forEach(el=>el.setAttribute('aria-label',t(el.dataset.i18nLabel)));
     const language=document.getElementById('language');language.textContent=lang==='en'?'中文':'English';language.lang=lang==='en'?'zh-CN':'en';language.setAttribute('aria-label',lang==='en'?'切换为中文':'Switch to English');
     document.getElementById('leaderboards').innerHTML=d.sections.filter(s=>!s.parent).map(s=>`<section id="${s.id}" class="benchmark-band"><div class="benchmark-shell"><details data-module="${s.id}"${collapsedModules.has(s.id)?'':' open'}><summary class="module-header"><h3 class="benchmark-wordmark">${h(s.name)}</h3></summary><div class="module-content"><div id="panel-${s.id}"></div>${d.sections.filter(c=>c.parent===s.id).map(c=>`<section id="${c.id}" class="benchmark-subsection"><div id="panel-${c.id}"></div></section>`).join('')}</div></details></div></section>`).join('');
diff --git a/rpent/benchmarks/results.json b/rpent/benchmarks/results.json
index 4f637b5..9bed95c 100644
--- a/rpent/benchmarks/results.json
+++ b/rpent/benchmarks/results.json
@@ -1,6 +1,10 @@
 {
   "schema_version": 2,
   "checked_at": "2026-09-22",
+  "evaluation_notice": {
+    "en": "* All RPent method results shown in this leaderboard are from September 2026 evaluations.",
+    "zh": "* Leaderboard 中所有 RPent 方法均展示 2026.09 测试结果。"
+  },
   "paper_revision": "2607.08448v4",
   "repository_revision": "43f32aa08cba07bd4d49a4bfa5eba4ef633e9b92",
   "configurations": [
diff --git a/rpent/reproducibility/astra-robocasa-20261009/OpenDrawer-matched-s1-s5.zip b/rpent/reproducibility/astra-robocasa-20261009/OpenDrawer-matched-s1-s5.zip
new file mode 100644
index 0000000..7d5c696
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