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Research PaperResearchia:202610.28059

Beyond Answer Changes: Matched Same-Class Controls for Local Audio Evidence

Yunuo Wang

Abstract

For audio-language models, an answer change after a local sound-event edit can reflect a response to the event's class, sensitivity to waveform rewriting, or both. We compare matched same-class and different-class replacements of a queried event. Both use the same interval, level estimate, donor fitting, and crossfade, while the same-class replacement preserves the correct answer. Across 600 synthetic soundscapes and three models from two architecture families, different-class replacements produ...

Submitted: October 28, 2026Subjects: AI; Artificial Intelligence

Description / Details

For audio-language models, an answer change after a local sound-event edit can reflect a response to the event's class, sensitivity to waveform rewriting, or both. We compare matched same-class and different-class replacements of a queried event. Both use the same interval, level estimate, donor fitting, and crossfade, while the same-class replacement preserves the correct answer. Across 600 synthetic soundscapes and three models from two architecture families, different-class replacements produce 49.7 to 60.9 percentage points more answer changes than same-class replacements. Same-class accuracy remains within 5.2 percentage points of original accuracy. Among trials with correct original and same-class answers, soundscape-weighted selective accuracy for the inserted class is 73.7 to 81.9%, averaging eligible trials within each soundscape and then weighting eligible soundscapes equally. This protocol tests how model answers respond to local changes in event class.


Source: arXiv:2610.37605v1 - http://arxiv.org/abs/2610.37605v1 PDF: https://arxiv.org/pdf/2610.37605v1 Original Link: http://arxiv.org/abs/2610.37605v1

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Submission Info
Date:
Oct 28, 2026
Topic:
Artificial Intelligence
Area:
AI
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