Hallucination scores fell because the models said less, not because they knew more

Six inference-time hallucination-mitigation methods were tested across three vision-language models and four benchmarks, MMStar among them. The authors report that lower hallucination scores came coupled with reduced object recall, reduced visual coverage and less detailed responses.

A method that makes a model say less will score better on a metric that only counts what it got wrong.

The mechanism is in what the benchmarks count. A wrong assertion is penalised and a withheld assertion is not, so shifting a model toward hedging moves the score in the right direction while its grip on the image is unchanged. The authors also report that gains on hallucination benchmarks did not reliably transfer to fine-grained perception and reasoning.

No magnitude is published. The paper reports the direction of the trade and not its size, which is the number a team choosing between these six methods would actually need, and it proposes hallucination be scored as a faithfulness, informativeness and capability trade-off instead.