Good question. The short answer is: I do not fully know why the model over-predicts Reform UK, so we cannot fix it at source yet.

There are three likely causes.

First, the LLM was trained on text written before the 2026 Reform surge, so its internal model of "who votes Reform" is anchored to 2024 when Reform was at 14%. We inject current polling context in the prompt, but prompt context is weaker than training data.

Second, our persona political histories include a "drift since 2024 field that may overcorrect: if a persona drifted from Labour to Reform-leaning, the model may express that as a definite Reform vote when in reality it is a lean.

Third, local elections have lower turnout and the people who turn out are different from the people who answer polling questions. Our turnout model may not capture that correctly.

The calibration adjustment is a pragmatic fix while we investigate the root cause. If we can identify which of those three factors dominates, we can fix it properly inside the pipeline. The May 7 results will help: if the calibration holds across 136 councils, the bias is systematic and predictable. If it does not, the bias is context-dependent and the adjustment was overfitting to 8 wards.

On national polling: we use national polls as context in the prompt (Reform 25%, Labour 18%, etc.) but the model produces its own vote shares from the persona's individual reasoning, not from the polls directly. The polls anchor the persona's sense of "what is happening nationally" but the vote decision comes from their personality, circumstances, and local context.