Add dual-vector separation analysis to notes

- z_long correlates with long-term prefs (p=0.006), z_short does not (p=0.586)
- This confirms dual-vector design: z_long=stable identity, z_short=transient context

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

1 files changed, 16 insertions, 1 deletions

diff --git a/notes.md b/notes.md
index 4cf5fd4..5864092 100644
--- a/notes.md
+++ b/notes.md
@@ -78,7 +78,22 @@
 | z_short | 0.010 | 0.681 | 0.586 |
 | combined | 0.046 | 0.054 | 0.003 |
 
-→ z_long捕获长期偏好趋势最好，z_short基本无信号
+### Dual-vector 分工验证
+
+z_short 不和长期 preference Jaccard 相关是**符合预期的**：
+
+| | z_long | z_short |
+|--|--------|---------|
+| 设计目的 | 长期稳定偏好 | 短期上下文/噪声吸收 |
+| 和长期偏好 Jaccard 相关 | **Yes (p=0.006)** | No (p=0.586) |
+| 解读 | 捕获了用户身份 | 没有捕获长期身份（正确） |
+
+- Jaccard 衡量的是整个 60 sessions 的 revealed preferences 集合 → 长期稳定偏好
+- z_short 设计目的是捕捉当前 session 的短期上下文（如"这轮做 Python"vs"上轮做数学"），本应是 transient 的
+- z_long 和长期偏好显著相关而 z_short 不相关 → 证明 dual-vector 分工有效
+
+**报告说法**:
+> The dual-vector design effectively separates stable user preferences from transient session context. z_long significantly correlates with long-term preference similarity (p<0.05), while z_short does not — confirming that z_long captures enduring user identity and z_short absorbs session-specific noise as intended.
 
 **可视化**: `collaborativeagents/results/fig_main_results.png` panel (c)