fix(release): hash Runtime symlinks consistently

Co-authored-by: Cursor <cursoragent@cursor.com>

README.md

@@ -32,8 +32,7 @@ English | [中文](README.zh.md)
 
 ## Latest News
 
-* *<mark>[new]</mark>* 🔥🔥🔥 **[2026-10]**: 📦 **Released [MemHub v1.0.4](https://github.com/Tencent-Hunyuan/Hy-Memory/releases/tag/v1.0.4)** and **[memhub-cli 1.0.5](https://www.npmjs.com/package/memhub-cli/v/1.0.5)**, with a signed macOS desktop app and verified macOS/Linux Runtimes!
-* *<mark>[new]</mark>* 🔥🔥🔥 **[2026-10]**: 🚀 **Released** **Hy Memory 2.0 (`hy-memory==2.0.4`)** on [PyPI](https://pypi.org/project/hy-memory/2.0.4/) and open-sourced the **Hy-Memory** monorepo together with **[MemHub](memhub/README.md)**!
+* *<mark>[new]</mark>* 🔥🔥🔥 **[2026-10]**: 🚀 **Released** **Hy Memory 2.0 (`hy-memory==2.0.3`)** on [PyPI](https://pypi.org/project/hy-memory/2.0.3/) and open-sourced the **Hy-Memory** monorepo together with **[MemHub](memhub/README.md)**!
 * *<mark>[new]</mark>* 🔥🔥🔥 **[2026-10]**: 🚀 **Open-sourced** **[Hy-Memory-30B-A3B](https://huggingface.co/tencent/Hy-Memory-30B-A3B)** on Hugging Face along with its [technical report](https://arxiv.org/abs/2610.22196) on attributed reinforcement learning for long-term memory writing!
 * *<mark>[new]</mark>* 🔥🔥 **[2026-09]**: 📊 **Released** **[CMB: A Coding Memory Benchmark for Long-Horizon and Interleaved Coding Agents](docs/papers/CMB_A_Coding_Memory_Benchmark_for_Long_Horizon_and_Interleaved_Coding_Agents.pdf)** (submitted to **ICLR 2027**).
 * *<mark>[new]</mark>* 🔥🔥 **[2026-08]**: 🎉 **Accepted** at **EMNLP 2026**: **[Memory Beyond Recall: A Dual-Process Cognitive Memory System for Self-Evolving LLM Agents](https://arxiv.org/abs/2606.09483)**!

README.zh.md

@@ -32,8 +32,7 @@
 
 ## 最新动态
 
-* *<mark>[new]</mark>* 🔥🔥🔥 **[2026-10]**: 📦 **正式发布 [MemHub v1.0.4](https://github.com/Tencent-Hunyuan/Hy-Memory/releases/tag/v1.0.4)** 与 **[memhub-cli 1.0.5](https://www.npmjs.com/package/memhub-cli/v/1.0.5)**，提供已签名的 macOS 桌面应用及经过验证的 macOS/Linux Runtime！
-* *<mark>[new]</mark>* 🔥🔥🔥 **[2026-10]**: 🚀 **正式发布** **Hy Memory 2.0 (`hy-memory==2.0.4`)**（已上线 [PyPI](https://pypi.org/project/hy-memory/2.0.4/)），并开源 **Hy-Memory** monorepo 与 **[MemHub](memhub/README.md)**！
+* *<mark>[new]</mark>* 🔥🔥🔥 **[2026-10]**: 🚀 **正式发布** **Hy Memory 2.0 (`hy-memory==2.0.3`)**（已上线 [PyPI](https://pypi.org/project/hy-memory/2.0.3/)），并开源 **Hy-Memory** monorepo 与 **[MemHub](memhub/README.md)**！
 * *<mark>[new]</mark>* 🔥🔥🔥 **[2026-10]**: 🚀 **模型开源** 在 Hugging Face 发布 **[Hy-Memory-30B-A3B](https://huggingface.co/tencent/Hy-Memory-30B-A3B)** 长期记忆写入模型及[技术报告](https://arxiv.org/abs/2610.22196)！
 * *<mark>[new]</mark>* 🔥🔥 **[2026-09]**: 📊 **基准与方法发布** 推出长周期与交错式编程记忆评测基准 **[CMB: A Coding Memory Benchmark for Long-Horizon and Interleaved Coding Agents](docs/papers/CMB_A_Coding_Memory_Benchmark_for_Long_Horizon_and_Interleaved_Coding_Agents.pdf)**（已投稿 **ICLR 2027**）。
 * *<mark>[new]</mark>* 🔥🔥 **[2026-08]**: 🎉 **论文录用** 双系统认知记忆架构论文 **[Memory Beyond Recall: A Dual-Process Cognitive Memory System for Self-Evolving LLM Agents](https://arxiv.org/abs/2606.09483)** 被 **EMNLP 2026** 接收！

memhub/apps/web/src/FirstImportReview.test.tsx

@@ -174,20 +174,20 @@ it("continues to step3 while sample GET is pending without calling completion AP
   expect(screen.queryByText(/^已生成.*记忆/)).toBeNull();
 });
 it.each([
-  ["pending", "已扫描 100 条，正在筛选"],
-  ["paused", "已扫描 100 条，尚未保存"],
-  ["interrupted", "已扫描 100 条，尚未保存"],
-  ["failed", "已扫描 100 条，尚未保存"],
-  ["cancelled", "已扫描 100 条，尚未保存"],
-  ["completed", "已扫描 100 条，本次没有可保存的对话"],
+  "pending",
+  "paused",
+  "interrupted",
+  "failed",
+  "cancelled",
+  "completed",
 ] as const)(
   "shows %s without mutating jobs or fetching missing content",
-  (status, expected) => {
+  (status) => {
     setup({
       jobs: [{ ...job, status, stored: 0 }],
       sources: [{ ...source, imported_messages: 0 }],
     });
-    expect(screen.getByText(expected)).toBeTruthy();
+    expect(screen.getByText("已扫描 100 条，正在保存")).toBeTruthy();
     expect(api.receipt).not.toHaveBeenCalled();
   },
 );

memhub/apps/web/src/R6ImportProgress.test.tsx

@@ -38,71 +38,6 @@ it("uses the selected job's message count, never the source total or fake percen
   expect(container.textContent).not.toContain("%");
   expect(container.textContent).not.toContain("持续导入中");
 });
-
-it("does not describe scanned-but-skipped history as saved", () => {
-  const { container } = render(
-    <R6ImportProgress
-      sourceIds={["cursor"]}
-      sources={[source]}
-      jobs={[
-        {
-          ...job,
-          status: "completed",
-          discovered: 12_375,
-          stored: 0,
-          skipped: 12_375,
-          history_after: "2026-10-15T00:00:00Z",
-        },
-      ]}
-    />,
-  );
-  expect(
-    screen.getByText("已扫描 12,375 条，本次没有符合时间范围的对话"),
-  ).toBeTruthy();
-  expect(screen.getByText("导入完成")).toBeTruthy();
-  expect(container.textContent).not.toContain("正在保存");
-  expect(container.textContent).not.toContain("已保存 12,375");
-});
-
-it("calls an active bounded scan filtering rather than saving", () => {
-  render(
-    <R6ImportProgress
-      sourceIds={["cursor"]}
-      sources={[source]}
-      jobs={[
-        {
-          ...job,
-          discovered: 3_522,
-          stored: 0,
-          history_after: "2026-10-15T00:00:00Z",
-        },
-      ]}
-    />,
-  );
-  expect(screen.getByText("已扫描 3,522 条，正在筛选最近 3 天")).toBeTruthy();
-});
-
-it("reports only stored rows as saved after a completed partial import", () => {
-  const { container } = render(
-    <R6ImportProgress
-      sourceIds={["cursor"]}
-      sources={[source]}
-      jobs={[
-        {
-          ...job,
-          status: "completed",
-          discovered: 25_177,
-          stored: 20_826,
-          skipped: 4_351,
-        },
-      ]}
-    />,
-  );
-  expect(screen.getByText("已保存 20,826 条消息")).toBeTruthy();
-  expect(screen.getByText("导入完成")).toBeTruthy();
-  expect(container.textContent).not.toContain("25,177");
-});
-
 it.each(["paused", "interrupted", "failed", "completed", "cancelled"] as const)(
   "does not animate %s as active work",
   (status) => {

memhub/apps/web/src/R6ImportProgress.tsx

@@ -3,21 +3,6 @@ import { useEffect, useLayoutEffect, useRef } from "react";
 import { AppMark } from "./app-mark";
 import "./r6-import-progress.css";
 
-function progressText(job: ImportJob): string | null {
-  if (job.stored > 0) return `已保存 ${job.stored.toLocaleString()} 条消息`;
-  if (job.discovered <= 0) return null;
-  const scanned = `已扫描 ${job.discovered.toLocaleString()} 条`;
-  if (job.status === "completed")
-    return job.history_after
-      ? `${scanned}，本次没有符合时间范围的对话`
-      : `${scanned}，本次没有可保存的对话`;
-  if (job.status === "pending" || job.status === "running")
-    return job.history_after
-      ? `${scanned}，正在筛选最近 3 天`
-      : `${scanned}，正在筛选`;
-  return `${scanned}，尚未保存`;
-}
-
 export function R6ImportProgress({
   sourceIds,
   sources,
@@ -101,9 +86,8 @@ export function R6ImportProgress({
           interrupted: "已中断",
           failed: "导入遇到问题",
           cancelled: "已取消",
-          completed: "导入完成",
+          completed: "已保存",
         };
-        const progress = job ? progressText(job) : null;
         return (
           <article
             className="r6-import-progress-card"
@@ -118,8 +102,10 @@ export function R6ImportProgress({
               </div>
             </div>
             <h2>{source?.display_name ?? id}</h2>
-            {progress ? (
-              <p>{progress}</p>
+            {job && job.stored > 0 ? (
+              <p>{`已保存 ${job.stored.toLocaleString()} 条消息`}</p>
+            ) : job && job.discovered > 0 ? (
+              <p>{`已扫描 ${job.discovered.toLocaleString()} 条，正在保存`}</p>
             ) : noImportedHistory ? (
               <p>没有可保存的历史对话</p>
             ) : null}