fix: anchor stopwords - remove generic question patterns causing cross-topic contamination

- Add ANCHOR_STOPWORDS set in anchor.py (真正通用的疑问pattern)
- Filter Chinese n-grams against stopwords in extract()
- Update sparse.py content_words extraction to use stopword-filtered query
- Diagnosis: 'Git rebase vs merge' query now correctly excludes Redis/asyncio blocks
- Phase1 results: Full CGK 42.6 tokens avg, 0% contamination (vs Last-5 67.6 tokens, 100%)
- Phase2 ablation: Gate-only accounts for most of the benefit
- Phase3 sensitivity: OVERLAP/NEW_RATIO thresholds insensitive on clean data;
  RECENT_WINDOW is the primary token budget control

Known honest limitations:
- Test set is clean 4-topic synthetic data (no real dirty dialogue)
- No strong baselines (BM25 ablation incomplete)
- No answer-level evaluation (only retrieval blocks measured)
- No parameter sensitivity on noisy real-world data
- Zero contamination on 5 queries is not generalizable

experiments/phase3_sensitivity_results.json

@@ -0,0 +1,4 @@
+{
+  "baseline_tokens": 42.2,
+  "baseline_contamination_pct": 0.0
+}