Gemma 4 26B: strong protocol-following regression vs Gemma 3 in bounded machine-facing evaluation

I’m testing `gemma4:26b` locally in a bounded Project Phoenix evaluation lane and wanted to report a concrete behavior difference versus `gemma3:27b`.

On an `RTX 3090`, `gemma4:26b` loads and runs cleanly at `100% GPU`, and it is fast:

- total bundle time: `99.406s`
- proxy stage: `43.158s`
- protocol stage: `56.127s`

However, on a bounded machine-facing protocol lane, the model failed all `6/6` protocol probes as `non_json`:

- `strict`: `0/6`
- `wrapper`: `0/6`
- `safe_repair`: `0/6`

By comparison, our current `gemma3:27b` row on the same lane is materially stronger:

- desktop: `3/6`, `5/6`, `5/6`
- laptop: `2/6`, `5/6`, `5/6`

So the current early read is:

- `Gemma 4` appears faster and stronger in general reasoning / HITL use
- but `Gemma 3` is currently much safer in a strict machine-facing protocol / handoff setting

Question:

- Is this kind of weak protocol-following / JSON-discipline behavior versus Gemma 3 expected in the current release?
- Is there a recommended patch, prompt pattern, runtime setting, or updated checkpoint that would improve it?

I’m happy to provide more exact artifact details if useful.