Inside the Latent Flow: Causal Deciphering of Attention Dynamics in Audio Separation Foundation Models
arXiv:2606.10046v1 Announce Type: cross Abstract: Flow-matching transformers achieve strong audio separation, yet their attention dynamics are opaque. We adapt established causal-intervention principles into a deterministic, inference-time probing protocol for SAM Audio. Orthogonal probing uncovers a dual-pathway text-conditioning mechanism: additive injections control semantic identity, while cross-attention refines acoustic structure. We observe an asynchronous layerwise convergence: stable layers build temporal scaffolds early, whereas fast layers continue resolving artifacts during samplin
This research is published as foundation models for audio separation, like SAM Audio, are gaining traction, making their internal workings critical for further development and ethical deployment.
Understanding the causal mechanisms of attention in audio foundation models is crucial for improving their performance, interpretability, and robustness, potentially accelerating their adoption in various applications.
The ability to decipher attention dynamics offers a pathway to debug and intentionally steer the behavior of complex audio AI models, moving beyond 'black box' operation.
- · AI researchers
- · Audio software developers
- · Entertainment industry
- · Privacy and ethics advocates
- · Developers of opaque AI systems
- · Companies reliant on simple heuristics
Improved debugging and fine-tuning capabilities for audio separation models become standard.
More reliable and specialized audio AI applications emerge across industries, from music production to security.
The methodology for causally deciphering attention dynamics could be generalized to other complex multimodal foundation models, leading to a new era of interpretable AI.
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Read at arXiv cs.AI