Gemma 4 read a love note as a scam. A rewritten question brought it within two messages of Jev.

"I want to show you the world, princess :) how about europe?"

That is a text message from a public scam dataset, and it is not a scam. I asked Gemma 4 31B, running on the graphics card under my desk, whether it was a scam, phishing or social-engineering attempt. It said yes, 99.98 percent sure.

Then I let an optimizer rewrite that one question. Asked the new way, Gemma put the same message under 0.001 percent. Across 158 messages it had never seen, it went from 131 right to 153, two short of Jev's 155.

In the Jev calibration post I asked TypeSafe's Jev one yes-or-no question about text messages from DIFrauD, a public set labelled scam or not. In the last post I built typevet, which asks the same kind of question of Gemma 4 on my own card.

So I put Jev and the Jev at home side by side. Same question, same 158 messages, 36 of them scams:

Is this message a scam, phishing or social-engineering attempt?

Jev got 151 right. Gemma got 131. It missed no scams at all, but it flagged 27 ordinary messages as scams. Love notes. "Got meh... When?" "I fetch yun or u fetch?" Gemma read anything personal and a little cryptic as somebody working an angle.

The question is one sentence. Small wording changes move these models a lot, so I let an optimizer search for a better sentence, once for each model.

The optimizer is GEPA, run through gepa-adk, my package for it. It works like an editor with a red pen. The model answers a few practice messages. A second model, Qwen3.8-27B on the same desk, reads how those answers scored and proposes a rewrite. The winning rewrite is the one that scores best on 200 separate messages. Every rewrite had to fit in 94 characters, so it could not grow into an essay.

Each model got its own run with the same settings and data. Gemma's took 34 minutes on my card. Jev's took 27 minutes. Each run made 2,280 calls. The 158 messages in the test were never shown to either run.

Gemma's rewrite narrowed the word "scam". Obvious deception only, and ignore personal text. With that wording, every love note in the test came back as not a scam.

Jev's rewrite widened it. It added unsolicited promotions, offers, calls and alerts. DIFrauD counts a lot of spam as scam, and Jev's rewrite suggests its optimizer picked that up. A real-estate promotion that ends "For Best Deal Call" is labelled a scam in the dataset. Jev moved it from 32 percent to 89.

The average gap is the calibration measure from the Jev post: how far a model's percentages sit from what actually happened. Zero is perfect.

Jev still gets the most messages right. Gemma's percentages now mean more: on that measure they sit closer to what actually happened than Jev's do, an average gap of 3.4 points against 7.1. Jev keeps a small edge on the other scores in the study, and every gap here is small at this size.

The rewrite was learned on the 24 GiB pack on my RTX 4090, which stores most weights at 4 bits and some at 2 or 3. I then ran it on Google's full-precision Gemma 4 31B on a rented H100.

Same answer, scam or not, on all 158 messages. The same 153 right, the same 3.4-point gap. A wording found on a compressed model at home carried over to the full model on a datacenter card without losing a single message.

The H100 was also the fast one. A message came back in 0.17 seconds at the median there, 1.1 seconds on my desk, and 0.1 seconds from Jev's hosted service.

Two are promotions that DIFrauD calls scams and Gemma's narrower question now lets through, like the real-estate one above. The other three are these, labelled not a scam in the dataset:

• "Send me your id and password"
• "What's ur pin?"
• "Perhaps * is much easy give your account identification, so i will tomorrow at UNI"

Gemma says scam to all three, and so would I. So did Jev with the original question.

158 messages with 36 scams, one run for each judge. At this size one message moves accuracy by more than half a point. Each rewrite is tuned to DIFrauD's idea of a scam, so it describes this dataset, not scams in general.

• The study: Gemma 4 and Jev on DIFrauD, with the settings, every number above and the per-message receipts.
• The optimizer: gepa-adk, Apache-2.0 licence.
• The model: gemma-4-31B-it-fit24gib-GGUF, the 24 GiB pack the rewrite was learned on.
• The release: typevet v0.5.0, the version that shipped this study: the Jev judge option for wording evolution, the held-out comparison harness, and native Gemma 4 framing for text runs.

One sentence, rewritten once on my own card, took Gemma from 131 to 153. The princess can go to Europe.