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LogitLens analyses and a targeted LM head intervention support the conclusion that acoustic underuse is not explained solely by encoder-side information loss and that readout alignment can be a dominant bottleneck.
SNAP, a speaker-nulling framework, is introduced that reduces speaker entanglement and encourages detectors to focus on artifact-related patterns, leading to state-of-the-art performance.
OmniEvaluator connects existing inference engines and curated evaluation libraries at a higher level, exposing four inference backends, four evaluation frameworks, and over a thousand benchmarks through a single interface.
OmniACBench, a benchmark for evaluating context-grounded acoustic control in omni-modal models, is introduced and three common failure modes are identified-weak direct control, failed implicit inference, and failed multimodal grounding-providing insights for developing models that can verbalize responses effectively.
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