Generative creative interfaces routinely report the user's position within a space of possibilities. Such displays imply that the quantities shown were measured from the artefact; in a growing class of systems they are instead predicted by a language model describing its own output, and the two are indistinguishable on screen. Most creative domains lack an agreed measurement against which such a self-report can be checked; audio admits one. In a measurement study of a working LLM-driven timbre interface, what a user can cause proves separable from what the interface reports. The first is exactly characterisable: the reachable set is a zonotope whose dimension increases by one per sound placed, so the space the model may act within is demonstrably the user's own. The second is inaccurate. The displayed position differs from measurement by approximately three times the width of that region, and the error has two independent sources: model-predicted coordinates, whose accuracy varies twofold between the two models evaluated, and the interpolation step, which arises from combining synthesiser parameters and is unaffected by model quality. End to end, 26 of 36 refinement instructions produced no perceptible change, and under the weaker model the facility performed no action while reporting convergence. Effective agency (what a user can cause) and epistemic agency (whether they can know it) are therefore separable and independently engineered: structurally enforced constraints survive model failure, whereas guarantees resting on the model's own report do not. The failure mode that remains is silence presented as success. We close with a short protocol for building and auditing such displays.
Figures & tables
Figure 1: (a) Sounds elicited by text prompts are placed as vertices and connected; the small white markers are beads, whose positions set the mixing weights, and the central glyph marks the audible mix over the three sources. The view is illustrative and is not one of the three canvases analysed below. (b) In perceptual space the attainable blends occupy a small, low-dimensional region; a target proposed by the model typically lies outside it, and the descent terminates at the boundary, as it did on every trial in Section 7 .
Canvas
Display error
Reachable extent
Ratio
Vertex term
Linearity term
A (2 sounds)
0.496
0.159
3.12 ×
0.486
0.149
B (3 sounds)
0.649
0.173
3.75 ×
0.627
0.245
C (4 sounds)
0.485
0.163
2.97 ×
0.145
0.430
Table 1: Display error relative to rendered audio, with its decomposition. The vertex and linearity terms are separately measured distances rather than additive parts of the total.
Capable model
Weaker model
Model target displaced from current position
yes (8.96–13.43)
no (0.000)
Perceptible change in the audio
9 / 18
1 / 18
of these, in the requested direction
8 / 9
1 / 1
No measurable change
9 / 18
17 / 18
Table 2: End-to-end outcomes, thresholded per dimension against the re-render noise floor. Capable model: gemma4:31b-cloud ; weaker model: gpt-oss:20b .
Appendix figures & tables3 assets
Supplementary material from the paper’s appendix.
Appendix
Dimension
Min
Max
Mean
SD
brightness
−7.5
92.6
52.0
20.7
depth
−4.5
80.3
45.6
18.8
hardness
−6.6
91.2
49.5
18.7
roughness
0.0
87.8
45.8
20.9
warmth
8.6
75.0
45.0
16.4
sharpness
−35.4
97.5
43.7
19.1
Appendix
Table 3: Per-dimension calibration used to map AudioCommons outputs to [0,1] , derived from the reference corpus.
Figure 2: Growth of the reachable region with the number of placed sounds.
Audio generation creates speech, music, and sound from text, media, symbolic controls, or reference audio, with growing focus on controllability and realism.
Generative music systems can now produce impressive audio from text prompts, but audio outputs are difficult to inspect, edit, and diagnose as musical structure. We introduce Libretto, an agent-facing framework for symbolic music generation and revision. Libretto uses an LLM-native grammar with explicit onset slots, voices, and bar-level organization, then evaluates each piece in a corpus-calibrated statistical space over rhythm, harmony, melody, texture, form, and variation. The same structural axes support retrieval, diagnosis, copy-risk control, and iterative self-revision. Across gap filling, reference-guided full-piece generation, gradual morphing, and educational music generation, Libretto turns symbolic music from a raw token sequence into a measurable and editable object for language-model agents.
We present an agentic soundscape construction framework for controllable compositional audio generation that makes explicit the scene planning, source selection, temporal layout, and rendering steps typically handled implicitly by single-shot text-to-audio models. An LLM-based agent converts user intent into an executable scene plan, acquires assets through retrieval and on-demand generation, renders controllable multi-event mixtures, and exports aligned scene metadata. The framework also supports human-in-the-loop interaction through user-guided tool selection and editable scene plans. Together, these components provide an inspectable and reusable approach to controllable soundscape synthesis and scalable audio-language data construction. Listener studies and objective metrics demonstrate competitive generation performance against text-to-audio baselines, while models trained with agent-generated data consistently outperform real-only baselines in downstream audio reasoning. Code, demos, and listening-test results are available at https://haozhang6720.github.io/SoundscapeAgentDemoPage/.
Hao Zhang, Yiwen Zhao, Yixuan Zhang +2
Wuhan University, Wuhan, China · Tencent Hunyuan, Bellevue, WA, USA
This paper examines a use of AI in creative practice as an interpretive sounding board for human-generated material, rather than the more familiar pattern of AI generation followed by human curation. Through the lens of resonance as theorized by Hartmut Rosa, I present a first-person case study of songwriting from July 2025 to March 2026, drawing on 16 original pieces in English, French, and other languages along with piano solos. I describe a configuration in which resonance is not located between user and model, but in the author's deepening contact with their own material, mediated through the model. This kind of resonance was supported rather than inhibited by AI when sounding-board behavior was cultivated through sustained calibration by the user. Two failure modes appeared when calibration was absent: sycophantic drift and magical overinterpretation. This account suggests both the potential and the risks of AI as an interpretive partner in creative practice.
Xiao Xiao
Institute for Future Technologies De Vinci Research Center, De Vinci Higher Education Paris, France · MIT Media Lab Cambridge, MA, USA