# Phase 0 v2 drilldown — semantic agreement + sema_boost outliers

**Source:** `cwola_export_20260414.json` (N = 1865; A = 125, B = 1740)
**Missing embeddings:** 0 rows (should be 0 post-backfill)

Two questions this doc answers:

1. Does **cos(query_sema, top_candidate_sema)** differentiate A vs B buckets? This is the simplest possible intra-query semantic-agreement signal — orthogonal to the 9 tier scores.
2. What do the **top-k sema_boost queries** actually look like? Phase 0 v1 found `mean(sema_boost | B) ≈ 2× mean(sema_boost | A)` — are the high-sema_boost B queries template-shaped as the antiresonance hypothesis predicts?

## Part 1 — semantic agreement (cos q,c)

| split | n | mean | median | p10 | p25 | p75 | p90 | stdev |
|---|---|---|---|---|---|---|---|---|
| all | 1865 | 0.642 | 0.682 | 0.392 | 0.540 | 0.795 | 0.836 | 0.192 |
| A | 125 | 0.598 | 0.630 | 0.291 | 0.480 | 0.771 | 0.827 | 0.221 |
| B | 1740 | 0.645 | 0.682 | 0.398 | 0.544 | 0.796 | 0.837 | 0.189 |

**A-bucket mean cosine − B-bucket mean cosine = -0.0469**

Non-trivial shift. B queries are more semantically aligned with their top candidate. Worth checking whether this is driven by query-length bias (short queries project into a sparser 20d region so cosines get inflated) before drawing conclusions.

## Part 2 — sema_boost outliers

### Top-k sema_boost scores in **A-bucket** (accepted retrievals)

| retrieval_id | score | cos(q,c) | n_tiers | requery_Δs | query |
|---|---|---|---|---|---|
| 1752 | 509.8 | 0.680 | 8 | — | What is the value of report data? |
| 1178 | 481.5 | 0.464 | 9 | — | What is the value of all ranks in two-brain-audit? |
| 1377 | 321.5 | 0.648 | 8 | — | What is the value of gene b? |
| 1078 | 321.2 | 0.463 | 9 | — | What is the value of p3z in steamapps? |
| 960 | 316.2 | 0.555 | 9 | — | What is the classname value in CosmicTasha/web/page? |
| 1637 | 291.0 | 0.297 | 9 | — | What is the value of url? |
| 1164 | 283.2 | 0.441 | 9 | — | What is the slot value in CosmicTasha/web/card? |
| 1720 | 251.2 | 0.730 | 7 | — | What is the value of flattenterrain? |
| 1062 | 243.0 | 0.702 | 9 | — | What is the value of expected in Education? |
| 516 | 239.3 | 0.738 | 9 | — | What is the files value in steamapps/common/README? |

### Top-k sema_boost scores in **B-bucket** (re-queried within 60s)

| retrieval_id | score | cos(q,c) | n_tiers | requery_Δs | query |
|---|---|---|---|---|---|
| 1564 | 2034.1 | 0.707 | 9 | 19.6 | What is the min tasks value in Education/fleet/outcome_tracker? |
| 1563 | 1962.5 | 0.785 | 9 | 16.7 | What is the min tasks configured in Education fleet? |
| 1412 | 1666.5 | 0.806 | 9 | 20.1 | What is the categories value in Education/fleet/digest_2026-03-26? |
| 1116 | 1604.0 | 0.855 | 9 | 15.7 | What is the index value in Education/fleet/api_lua_inventory? |
| 968 | 1585.6 | 0.722 | 9 | 19.2 | What is the days value in Education/fleet/metering_blueprint? |
| 1452 | 1580.4 | 0.680 | 9 | 27.0 | What is the 27t23 value in Education/fleet/source_meta? |
| 1611 | 1544.4 | 0.876 | 9 | 16.7 | What is the payments configured in Education fleet? |
| 1612 | 1535.1 | 0.711 | 9 | 17.8 | What is the payments value in Education/fleet/payments? |
| 967 | 1475.3 | 0.843 | 9 | 17.7 | What is the days configured in Education fleet? |
| 1584 | 1466.0 | 0.633 | 9 | 14.7 | What is the peers by url value in Education/fleet/discovery? |

## Antiresonance read

The antiresonance hypothesis: high tier agreement on template-shape queries is the failure mode (every tier reads the same surface feature; confidence is lockstep but wrong). If this is what sema_boost is picking up, the top-B queries above should show:

- Template structure ('what is X of Y', 'how does A relate to B') more often than the top-A queries
- cos(q, c) similar or higher than A-bucket (dimensions agree semantically AND score highly — all-green failure mode)
- Multiple tiers fired simultaneously (n_tiers_fired high)

Inspect the tables above. If the pattern holds, sema_boost is a concrete lever for tuning: either down-weight it when other tiers also score high (lockstep detection), or invert its usage (high sema_boost = suspicious, not confident).

## Next moves

1. Label the top-B queries by hand (template / natural / mixed) and    quantify whether the antiresonance signature holds past eyeballing.
2. Full 9×9 tier correlation matrix (not in this drilldown — that    needs the per-tier raw scores and belongs with the batman follow-up).
3. If the cosine delta in Part 1 is non-trivial, investigate    query-length bias before crediting the signal.

— Laude (generated by `scripts/pwpc/phase0_v2_drilldown.py`)