How much do the two embedding models agree?

The same16 selected seed profiles were searched over388,656 eligible state-1/cohort-4 profiles. These counts compare the64 retrieved neuron-neighbor text identities per seed, after same-neuron-family exclusion and ordered-text deduplication. Controls are not included in this overlap calculation.

Comparison Same first neighbor Mean overlap at16 Mean overlap at64 Shared at64: median (range)
Cross-encoder raw 3/16 17.2% 18.6% 8.5 (0–47)
Cross-encoder change direction 3/16 28.1% 29.9% 14 (7–41)
Gemma raw vs change direction 3/16 27.0% 26.1% 15 (4–37)
Qwen raw vs change direction 7/16 34.0% 33.9% 21 (9–54)

Overlap is intersection size divided by k; exact Jaccard values and all per-seed counts are in RESULT.json. This is arithmetic on saved ranked lists, not another numerical audit or full vector rerank.

The differences show that representation and raw-versus-baseline-change scoring alter which examples are surfaced. They do not establish which model is better, whether a behavior replicated, or how common a behavior is. Semantic follow-up must examine the actual prompts and outputs, including failures and controls.

Seed profile Cross-encoder raw shared/64 Cross-encoder change shared/64 Gemma raw/change shared/64 Qwen raw/change shared/64
28187 11 12 21 22
82241 18 12 29 37
90499 13 17 31 35
164063 6 7 13 25
173645 6 13 6 11
188032 7 23 4 14
195983 4 14 7 12
202491 47 40 37 54
211999 26 41 29 20
219105 6 14 5 12
219822 6 26 9 9
219853 11 30 15 27
220225 13 25 24 23
263390 10 10 15 28
309603 6 12 15 9
441736 0 10 7 9