Same-class controls isolate local audio evidence in model answers
Different-class edits change answers 49.7 to 60.9 points more often than matched same-class replacements.
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Different-class edits change answers 49.7 to 60.9 points more often than matched same-class replacements.
On ten held-out personas, fine-tuning and reinforcement learning improved personality, style, and music responses.
Human annotators score high on binary temporal-change classification while models stay near chance.
Study finds most audio language models do not align heard and read features, except voicing in Qwen2.5-Omni.
FRAUDSkill adapts frozen audio-language models for telecom fraud detection via external skill programs, reaching 73.50% Macro-F1.
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