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Large Audio-Language Models (LALMs) achieve strong performance on multiple-choice Audio Question Answering (AQA) but often exhibit modality bias, over-relying on textual priors in questions and candidate options rather than grounded acoustic evidence. We present CoRE, a training-free, plug-and-play test-time option re-scoring method. CoRE constructs counterfactual audio via chunk permutation and random segment reversal to disrupt long-range temporal structure while largely preserving short-time acoustics. It estimates option-level evidence gain by contrasting scores from original and counterfactual audio, and applies an adaptive evidence-aware gate for final prediction. Under a unified option-scoring protocol, experiments on DCASE 2025 Task 5 and AIR-Bench SoundQA show consistent gains with Qwen2-Audio and Kimi-Audio.