Latent Space Disentanglement via Activation Steering for Interpretable Attribute Control in Symbolic Music Generation
arXiv:2605.31295v1 Announce Type: cross Abstract: Transformer-based architectures have significantly advanced the generation of complex symbolic sequences, yet a significant gap remains in achieving fine-grained, interpretable control over discrete signal attributes. This paper investigates the mechanistic interpretability of the Multitrack Music Transformer (MMT) and proposes a framework for deterministic attribute modulation without retraining to bridge this gap via inference-time activation steering. Utilizing the Difference-in-Means (DiffMean) methodology, we isolate latent directions for
The paper leverages the rapid advancements in transformer architectures for sequence generation and addresses the growing need for interpretable and controllable AI systems.
This research offers a method for fine-grained, interpretable control over AI outputs without retraining, which is crucial for safety, reliability, and adoption of generative AI in sensitive applications.
The ability to deterministically modify discrete signal attributes post-training opens new avenues for AI deployment where precise control over generated content is paramount.
- · AI developers
- · Generative AI platforms
- · Music composition industry
- · Creative industries
- · AI models lacking interpretability
- · Black-box AI systems
Increased real-world applicability of generative AI due to enhanced control and interpretability.
Development of new AI-powered tools and services that allow users to precisely manipulate AI-generated content at inference time.
Shift in AI ethics and governance frameworks to prioritize and standardize interpretable attribute control in AI systems.
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Read at arXiv cs.LG