Why might DiffusionGemma be better at tool calls than its benchmark quality suggests

Most of the talk on this is the 4x speed. Google themselves say it's lower quality than Gemma 4 and to use Gemma 4 for production. Fair. But the speed is not really what's on my mind. It generates a 256 token block in parallel with bidirectional attention, so it can revise tokens it already placed before it finalizes the block. Autoregressive decoding can't do that. Once an AR model emits a brace or a field name it's committed, and if it went wrong the only paths are to fail or to bolt a repair layer and a retry in front of it. Structured output is exactly where that matters. A malformed tool call is usually one bad token in an otherwise fine sequence, and a model that can look back over the whole block and self correct has a structural shot at fixing it that a left to right model never gets. Which makes the decoding shape more interesting than the quality score here. The thing worth testing is whether bidirectional self correction buys a higher valid tool call rate, even though the base quality is lower than Gemma 4. Has anyone actually benched this for tool calling to see if the bidirectional canvas fixes broken JSON, or does the lower base quality mean it just generates well structured garbage?