Tracing Audio Grounding and Answer Selection in Audio LLMs

This paper presents methods for tracing how audio language models ground their outputs in source audio and select answers, improving interpretability of audio-based AI systems. The research addresses the 'black box' problem in multimodal LLMs by enabling enterprises to understand model reasoning in audio-dependent applications.

This paper presents methods for tracing how audio language models ground their outputs in source audio and select answers, improving interpretability of audio-based AI systems. The research addresses the 'black box' problem in multimodal LLMs by enabling enterprises to understand model reasoning in audio-dependent applications. For regulated sectors handling sensitive audio data (healthcare, financial services, legal), such transparency is critical for compliance, auditability, and risk management.

Legal AI must preserve privilege, satisfy ethics rules, and keep client data within a defensible perimeter. QuettaMinds helps law firms and legal departments deploy AI that is structurally compliant, not just policy-compliant.