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REVIEW 3 major objections 4 minor 1 cited by

"I made this (sort of)": Negotiating authorship, confronting fraudulence, and exploring new musical spaces with prompt-based AI music generation

T0 review · 3 major / 4 minor · reviewed 2026-08-06 · deepseek-v4-flash

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Pith's one-line read This paper claims that state-of-the-art prompt-based AI music platforms, trained on commercially released recordings, cannot generate music that is not practiced, polished, and produced, and that human imperfection is a musical space they…

desk verdict A candid practice-based paper whose central generalization about AI music platforms outruns its undocumented evidence. read the letter →

arxiv 2507.23365 v1 pith:LDFDY2UA submitted 2025-07-31 cs.SD cs.AIeess.AS

Bob L. T. Sturm This is my paper
classification cs.SDcs.AIeess.AS
keywords AImusicgenerationauthorshipfraudulencepromptengineeringunpolishedSunoUdioLLM-mediatedself-reflection
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This paper is an artist-researcher's account of two albums made with Suno and Udio, prompt-based AI music generators. The first album, Music from the Spam Folder, treats junk mail as lyrics and celebrates what the machines can do; the second, A Difficult Christmas, is built around what the author claims they cannot do. The paper's central claim is that current state-of-the-art prompt-based AI music platforms cannot generate music that is not practiced, polished, and produced: the novice-violin practicer he prompted Udio to produce came out sounding competent. That claim matters because it locates a genuine expressive limit in the technology, and it gives human imperfection a positive role as a space AI has not yet colonized. The paper also develops a method, an LLM conducting an interview of the author, as a way of reflecting on authorship and fraudulence.

What carries the argument

The load-bearing object is A Difficult Christmas's first track: a prompt-based attempt to simulate novice violin practice, contrasted with the author's own unpolished violin playing in the following tracks. The mechanism invoked to explain the ceiling is the training-data distribution: commercial platforms are fitted to released, professionally produced music, so unpracticed, anxious, unproduced performance sits outside their typical output. A secondary machinery is the author's term "pseudoplunderphonics"—plundering at one remove through a machine-learning pipeline trained on plundered recordings—which frames his reuse of generated audio as a distinctive authorial act.

What would settle it

Generate a large set of outputs from Udio or Suno using systematically varied prompts that describe a novice, unpracticed, anxious performer (ideally including audio conditioning with an actual beginner's practice session), and ask experienced listeners to identify convincing unpolished performances; if a substantial share of outputs are mistaken for real beginner recordings, the claimed expressive ceiling is not a platform limitation.

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Extended reading notes

Core claim

On the author's own terms, the discovery is an expressive ceiling: despite prompt flexibility, Udio and Suno are trained on commercially released, professionally recorded music, so their outputs reliably land in a practiced, polished, produced register. The first track of A Difficult Christmas is offered as evidence—each prompt was an attempt to make Udio generate something sounding like a novice violin student practicing, and the generated audio did not sound unskilled. The ten tracks that follow are the author playing solo violin unpolished, presented as sounds not possible with Udio. Working from this limitation, the author reframes authorship: prompt authoring alone is weak authorship, curation is editing, and remixing, sampling, or live performance can justify a claim to authorship; the album also invites machines to listen to non-expert music so future training can include it.

Load-bearing premise

The central claim rests on the assumption that the prompts and settings used in A Difficult Christmas are a fair test of what Udio can do; the paper does not report the exact prompt text, platform version, parameters, or number of trials.

Editorial extensions

If this is right

  • If the polished-produced ceiling is real, prompting alone cannot reach the expressive register of amateur, beginner, or deliberately unpolished music, so artists seeking that register must play, record, or process audio themselves.
  • Human imperfection becomes a distinguishing resource: unpolished performance is an authenticity signal that AI-generated music currently lacks.
  • Platforms trained on future datasets that include unpolished music, such as A Difficult Christmas, could lose this limitation, so the ceiling is a contingent property of training data rather than a permanent law.
  • Authorship claims based only on writing prompts are weak under this account; curation, remixing, sampling, and live performance carry the authorial weight.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • If the limitation is real, it suggests a testable asymmetry: commercial generators may model "good" performance far better than "bad" performance, and the same could hold for other aesthetic negatives such as out-of-tune, clumsy, or amateurish output, which could be probed with controlled perceptual studies.
  • The author's LLM-mediated interview method could be extended to other creative practitioners as a structured self-reflection protocol, with the caveat that the LLM's flattering, uncited responses shape what gets articulated.
  • An unstated corollary is that the unpolished register is itself a scarce and increasingly valuable resource in the AI era; as synthetic music floods distribution channels, deliberately imperfect human performance may gain cultural and economic value.
  • The paper implicitly predicts that if such albums enter training data, platforms will begin to imitate non-expert performance, which would erase the current boundary; one could monitor releases for tell-tale amateurish artifacts.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

3 major / 4 minor

Summary. This paper is a first-person, practice-based reflection on two albums made with the commercial prompt-based music generation platforms Suno and Udio. The first album, "Music from the Spam Folder", turns junk mail into prompts and curates the outputs; the second, "A Difficult Christmas", is built around the author's claim that these platforms cannot generate music that is unpracticed, unpolished, and unproduced. The middle of the paper is a transcript of an interview conducted by ChatGPT 4o, seeded with the album liner notes and paper text, on authorship, fraudulence, and new creative spaces; the author then edits his answers and adds a reflection on the method.

Significance. Within the creative-practice-research genre, the paper has real strengths: it is unusually candid about the limits of authorship and about the author's own musical limitations; it connects the work to relevant literature on authorship, plunderphonics, and music-performance modeling; and it is transparent about the LLM's role, including its tendency to flatter and its failure to cite literature. The central empirical claim—that current prompt-based AI music generators cannot produce unpolished, unpracticed, unproduced music—would be a significant observation about the training-data bias of commercial music-generation systems if it were established. As it stands, however, it is a single undocumented negative result generalized to a class of systems, and the paper does not provide the evidence needed to support that generalization.

major comments (3)
  1. [Abstract; §2.3; §1] The paper's load-bearing empirical claim is stated as a general platform limitation in the Abstract and in §2.3 ('beyond today's most powerful prompt-based AI music generation technology'), but the evidence is limited to the author's own trial-and-error with Udio, described in §1 only as "an attempt to make Udio generate something that sounds like a novice violin student practicing." No prompt texts, platform versions, model parameters, number of attempts, or criteria for judging 'convincing' are reported. Because prompt-conditioned generative systems are highly sensitive to wording and stochastic sampling, a small set of undocumented failures cannot establish a capability ceiling; the observed failures could be a prompt-engineering artifact. The claim should either be reframed as a first-person experiential report or be backed by a systematic, reproducible search protocol with released prompt logs.
  2. [Abstract; §2.3] The claim is about "today's most powerful prompt-based AI music generation technology" and the paper names both Suno and Udio, but the §2.3 evidence concerns Udio only. No dates, service versions, or comparative tests with Suno or other state-of-the-art systems are given. If the assertion is meant to cover a class of systems, it needs comparative evidence or a narrowed scope; otherwise the reader cannot tell whether the limitation is specific to one platform, one version, or one prompting style.
  3. [§1; §3] The LLM-mediated interview is presented as the paper's method, but the protocol is underspecified: §1 says the author "explored different LLM model versions, prompts and trajectories of thought" and settled on the final one, yet none of those alternatives or the selection criteria are described, and §3 acknowledges the LLM "was not able to cite any literature" and "bordered on flattering." These admissions are honest, but they undermine the final section's methodological claims unless the protocol and its limitations are documented, for example in an appendix, or the conclusions about the method are tempered.
minor comments (4)
  1. [§1] There is a missing space in "Music from the Spam Foldermarvels" in the second paragraph of §1.
  2. [§2.3] In the list of absent descriptors, "uskilled" should be "unskilled".
  3. [References] The in-text citation "Agarwal and Greer (2023)" does not match the reference entry "Agarwal, M. and Geer, R."; please align the spelling.
  4. [§2.3] The claim that studies of non-expert musicians "number far less" than studies of experts would benefit from a systematic count or at least a clearer scope, since the sentence currently reads as an impression rather than a finding.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the platform-limitation claim is an empirical, trial-and-error observation, not a derivation from its own inputs or from load-bearing self-citations.

full rationale

This is a reflective practice-based paper, not a derivation chain with equations or fitted parameters. The central assertion, that state-of-the-art prompt-based AI music generation platforms cannot produce music that is unpracticed, unpolished, and unproduced, is presented as an empirical observation from the author's own trial-and-error work with Udio and Suno (Sections 1 and 2.3). The claim is supported by the creation of A Difficult Christmas, whose first track was an attempt to prompt Udio to generate a novice violin student practicing; the paper reports that this attempt failed. Nothing in the paper defines the target property into existence or fits a parameter and then calls the fit a prediction. The self-citations to Sturm (2024a, 2024b) document the albums and their liner notes, but they do not carry the logical weight of the inability claim; the claim stands or falls on the reader's willingness to accept the author's undocumented negative prompting results. The LLM interview is seeded with the author's own framing, abstract, and introduction, but the paper transparently identifies this as a method for self-reflection and does not use the interview as evidence for the platform-limitation claim. The lack of prompt texts, platform versions, and number of attempts is a genuine evidential weakness relevant to correctness and falsifiability, but it is not circularity. No self-citation chain, imported uniqueness theorem, or ansatz-via-citation is used to force a conclusion. The paper therefore contains no circular step by the standards of this review.

Assumptions & free parameters 0 free parameters · 3 assumptions · 0 invented entities

No numerical free parameters are fitted and no new ontological entities are posited. The loaded assumptions are qualitative: representatives of the platforms, fairness of the prompts, and validity of LLM-mediated self-reflection as a method.

assumptions (3)
  • domain assumption Suno and Udio are representative of state-of-the-art prompt-based AI music generation platforms.
    The paper generalizes from two commercial services to the whole category; Section 1 names only these platforms.
  • ad hoc to paper The specific prompts used in A Difficult Christmas were reasonable attempts to generate novice violin music.
    Prompt text and parameters are not disclosed; the conclusion treats failure as a platform limit rather than a prompt artifact.
  • domain assumption The LLM interview responses are treated as a valid basis for self-reflection.
    The author acknowledges flattery and lack of listening, but still uses the transcript as the paper's core content; Section 3.

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Cite this review

Pith. "Pith review of "I made this (sort of)": Negotiating authorship, confronting fraudulence, and exploring new musical spaces with prompt-based AI music generation." pith.science (2026). https://pith.science/paper/LDFDY2UA

@misc{pith2026250723365,
  author       = {Pith},
  title        = {Pith review of: "I made this (sort of)": Negotiating authorship, confronting fraudulence, and exploring new musical spaces with prompt-based AI music generation},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/LDFDY2UA}},
  note         = {Machine review of arXiv:2507.23365}
}
read the original abstract

I reflect on my experience creating two music albums centered on state-of-the-art prompt-based AI music generation platforms. The first album explicitly poses the question: What happens when I collide my junk mail with these platforms? The second album is a direct response to the first, and toys with the inability of state-of-the-art prompt-based AI music generation platforms to generate music that is not ``practiced'', ``polished'', and ``produced''. I seed a large language model (LLM) with information about these albums and have it interview me, which results in the exploration of several deeper questions: To what extent am I the author? Where am I in the resulting music? How is my musical identity changing as I am faced with machines that are in some ways far more talented than I? What new musical spaces does my work open, for me or anyone/thing else? I conclude by reflecting on my reflections, as well as LLM-mediated self-reflection as method.

Figures

Figures reproduced from arXiv: 2507.23365 by the authors.

Figure 1
Figure 1. The digital audio workstation view of the track “Peace is the Way” (track 1) from “Music [PITH_FULL_IMAGE:figures/full_fig_p004_1.png] view at source ↗

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Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. Finding the noise: Zero-shot AI Music Detection

    cs.SD 2026-07 conditional novelty 6.0 of 10

    A zero-shot method based on fakeprints, NMF and a blur-based reconstruction error detects unknown AI-music generators in one-class and clustering setups, working for most services but missing Mubert and pre-v9 Mureka.

Reference graph

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