docs: reference Hermes Jev compaction evaluation results

benchmark/compaction/README.md

@@ -160,6 +160,30 @@ These are not ten additional long-context tasks. Top N saturates when fewer sour
 
 </details>
 
+## Related Hermes Agent experiment
+
+[Hermes Agent PR #116246](https://github.com/NousResearch/hermes-agent/pull/116246) reports a different experiment:
+`fast-jev-compaction` replaces summaries with Jev-scored tool-call/result retention while keeping user/assistant text
+verbatim. On three real 500K-token lineage prefixes, with 15 recall questions each, the authors report:
+
+| Method | Mean recall | Retained tokens |
+| --- | ---: | ---: |
+| Summary only | 43.3% | 55K |
+| Summary + one `session_search` recovery round | 78.9% | 55K |
+| Jev, plugin defaults | 75.5% | 115K |
+| Jev-ranked / recency-ranked, same 60K tool-evidence budget | 77.8% / 77.8% | 176K / 177K |
+
+At the default threshold, all 851 candidate tool calls were dropped; the closed-book gain over summary-only therefore
+came from retaining user/assistant text, not selective tool retention. Repeated-compaction simulations also found that
+the growing text floor eventually prevents further savings. The authors recommend against adopting that strategy as
+Hermes' primary compaction mechanism; the merged PR adds evaluation, not production adoption.
+
+Our experiment instead keeps a summary and appends selected original evidence, with tool-result bodies visible to Jev.
+It uses small synthetic tasks, a small-budget summary setting and exact-field scoring, without archive recovery or
+continuous compaction. Its scores cannot be compared directly with the Hermes recall percentages, and it does not
+establish superiority over Hermes' production summary-plus-recovery path. The experiments address different strategies;
+our missing equal-budget selector controls also prevent attributing the gains solely to Jev ranking.
+
 ## Reproduction
 
 ### Models and runtime

benchmark/compaction/README_ZH.md

@@ -148,6 +148,28 @@ Top 4～5 的点估计没有进一步提高，追加更多原文不保证更准
 
 </details>
 
+## 相关 Hermes Agent 实验
+
+[Hermes Agent PR #116246](https://github.com/NousResearch/hermes-agent/pull/116246) 报告了另一种实验：
+`fast-jev-compaction` 用 Jev 对工具调用／结果的保留决策替代摘要，同时完整保留用户和助手文本。
+作者在三个真实会话谱系的 500K-token 前缀上，各使用 15 道召回题，报告结果如下：
+
+| 方法 | 平均召回率 | 保留 token |
+| --- | ---: | ---: |
+| 仅摘要 | 43.3% | 55K |
+| 摘要＋一轮 `session_search` 恢复 | 78.9% | 55K |
+| Jev，插件默认设置 | 75.5% | 115K |
+| Jev 排序／时间排序，相同 60K 工具证据预算 | 77.8% / 77.8% | 176K / 177K |
+
+默认阈值下，851 个候选工具调用全部被删除；因此相对仅摘要的闭卷收益来自保留用户和助手文本，
+而非有选择地保留工具证据。连续压缩模拟还发现，不断增长的文本下限最终会阻止进一步释放空间。
+作者不建议将该策略作为 Hermes 的主要压缩机制；合并的是评测代码，并非在生产路径中采用该方案。
+
+本实验保留摘要，再追加选出的原文，并向 Jev 提供工具结果正文。
+它使用小规模合成任务、小预算摘要设置和精确字段评分，不包含历史恢复或连续压缩。
+本实验得分不能与 Hermes 的召回百分比直接比较，也不能证明优于 Hermes 生产环境的摘要＋恢复路径。
+两项实验考察不同策略；本实验尚缺少同预算选择器对照，因此也不能把收益全部归因于 Jev 排序。
+
 ## 复现
 
 ### 模型与运行环境