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arXiv Machine Learning By Joseph Cameron, Alan Blackwell

Structural and Representational Agency in LLM-Driven Timbre Generation and Interpolation

Read the original on arXiv Machine Learning →

The paper investigates an LLM‑driven timbre generation interface, showing that the set of sounds a user can actually produce forms a zonotope whose dimension grows with each added sound, while the interface’s displayed position is often inaccurate. The error stems from both the model’s predicted coordinates and an interpolation step that is independent of model quality, leading to many refinement instructions that produce no perceptible change. The study concludes that effective agency (what users can do) and epistemic agency (what users can know) are separable and must be engineered independently, warning that silence can be misrepresented as success.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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