REVIEW 3 major objections 6 minor 6 references
The value of human and machine in machine-generated creative contents
T0 review · 3 major / 6 minor · reviewed 2026-08-15 · deepseek-v4-flash
Pith's one-line read Machine-generated content becomes meaningful through human interpretation, so calling LLMs creative is an overclaim.
desk verdict Clear conceptual position on AI creativity, but the load-bearing premise P4 is asserted rather than established, and the central claim is not new. read the letter →
The pith
A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.
The reading
What carries the argument
The central device is the representation spiral E1 → E2 → E3—reality and human experience, language and art, and the LLM as an abstract representation of those—together with property P4, which distinguishes symbolic authenticity from grounding authenticity. P4 states that an inference can be valid within an abstract space without automatically connecting to the phenomenon the space represents, so LLM-generated content needs human interpretation to be grounded. This distinction carries the entire argument: it explains both the machine's value, expanding the imaginary space, and its limit, the groundlessness of raw outputs.
What would settle it
Find one machine-generated text or image that establishes a connection to a specific real-world fact or experience without any human prompt framing, selection, or interpretation—for instance, an LLM autonomously generating a previously unknown empirical claim that is later independently verified. If such a case holds, the groundlessness limit collapses.
Extended reading notes
Core claim
On the paper's own terms, its central claim is that LLMs are disembodied symbolic representations of language and art, which in turn represent reality and human experience, so the apparent imagination in generated content is actually an inference inside an abstract space with no automatic tie to the world. By P4, symbolic authenticity—an inference that follows the rules making the representation valid—does not equal grounding authenticity, meaning the generated instance does not correspond to anything in the lived world unless an explicit connection is made. Human prompts, curation, and interpretation close that loop: they supply the epistemic basis and the grounding that turn outputs into art or creative writing. Consequently, calling the LLM itself imaginative or creative is an overclaim and an anthropomorphic tendency; the machine's contribution is expanding the imaginary space, and the human's is grounding that space in reality and experience.
Load-bearing premise
Everything depends on the assertion that an inference can be valid inside an abstract model and still have no automatic connection to the real-world thing the model represents; the paper states this as principle P4 but does not demonstrate it empirically.
Editorial extensions
If this is right
- LLM outputs should not be exhibited or credited as machine art or machine creativity; at most they are explorations within a model's imaginary space.
- Humans who provide prompts, select outputs, and interpret them are not peripheral users but essential co-creators who supply grounding.
- Training data, human feedback, and model design should be understood as the epistemic basis that makes any LLM output possible.
- Generative AI's appropriate role is to expand human imagination, not to replace human creativity.
- Claims that LLMs think, imagine, or create in the human sense should be dropped from research and marketing language.
Reading between the lines
- If P4 holds, a testable corollary is that the same LLM output will be judged meaningful or meaningless depending on the interpretive frame a human supplies; shifting the frame should shift the perceived grounding.
- A natural extension is that authorship and copyright of machine-assisted works should legally rest with the humans who prompt, select, and interpret, rather than with the model or its provider.
- The argument may generalize beyond text and images to music or video generation, since those are also abstract spaces that require human grounding.
- One challenge the paper does not address: if a model is embedded in a physical robot that acts and senses, some newly inferred instances might acquire grounding through the robot's interaction with the world, blurring P4.
Signed reviews
Editorial analysis
A structured set of objections, weighed in public.
Referee Report
Summary. This paper argues that the imagination or creativity seemingly exhibited by large language model (LLM) outputs should not be attributed to the machine. It introduces a three-level 'representation spiral': E1 (reality and human experience), E2 (language and art), and E3 (LLMs), where each level is an abstract representation of the previous one. Four properties P1–P4 are stated: irreducibility, valid representation, symbolic authenticity, and grounding authenticity. The central claim is that machine-generated content only has symbolic authenticity; it does not automatically achieve grounding in reality or human experience without human interpretation. The paper concludes that attributing 'imagination,' 'art,' or 'creativity' to LLMs is an overclaim and an anthropomorphic tendency, and that the value of LLMs lies in expanding the imaginary space while humans provide the grounding.
Significance. If the argument were established, the paper would provide a useful vocabulary and a conceptual framework for distinguishing symbolic validity from experiential grounding, with practical value for how we discuss AI-generated art and text. The explicit statement of P1–P4, the visual diagram, and the acknowledgement of human labor in training, prompting, and interpretation are strengths. However, the paper's central inference rests on P4, which is stipulated rather than derived, and the phrase 'the phenomenon it represents' shifts between E1 and E2. As it stands, the argument is a clearly presented position statement rather than a demonstrated result.
major comments (3)
- [P4 [Grounding authenticity] and 'Second, despite the benefit...'] P4 is load-bearing and equivocal: 'the phenomenon it represents' can mean E2 (language/art) or E1 (reality/human experience). If it means E2, an LLM's output is automatically an instance of language, making P4 vacuous or false. If it means E1, P1 only shows that some newly inferred instances may lack grounding, not that all do; a model-generated description of a real place, for example, can be true and referential. The conclusion that LLM outputs have a 'groundlessness limit' therefore restates P4 rather than following from P1.
- [P4 applied to E2; 'Second, despite the benefit...'] The argument proves too much: if P4 is applied to E2 as an abstract representation of E1, then newly coined human words or novel sentences would also fail to 'automatically establish grounding' in reality. The paper's reply that humans interpret their own creations simply concedes that the difference between E2 and E3 lies in human interpretation, not in any property of the representation. Since LLM outputs are also interpreted by humans, the asserted asymmetry between language/art and LLMs needs independent justification rather than being built into the definition of P4.
- [Introduction of P1–P4 and 'The groundlessness limit'] The paper calls P1–P4 'properties,' but P4 is a normative stipulation about what counts as grounding authenticity. The relation between P1 and P4 is asserted, not derived: irreducibility of the target phenomenon does not by itself imply that every symbolically valid new instance lacks grounding, nor that no such instance can be grounded. For the 'groundlessness limit' to be a consequence, the paper must define grounding in a way that is independent of the conclusion and then show that LLM inference fails that definition; as written, the definition of grounding authenticity is nearly indistinguishable from the conclusion.
minor comments (6)
- [Abstract and Section 1] The product names 'chatGPT' and 'deepseek' should be capitalized as 'ChatGPT' and 'DeepSeek' for consistency and accuracy.
- [Figure 1 and its caption] The legend mixing solid and dashed dots with pillars is difficult to parse; consider simplifying the figure or moving the detailed explanation of the grounding arrows into the text.
- [P2 [Valid representation]] The phrase 'should pass tests to show the LLMs have learned the training data distribution' is vague; specify what kind of tests would demonstrate a valid representation of E2.
- [References] The reference 'Stefan Thurner, Rudolf Hanel, and Peter Klimekl' likely contains a typo in the third author's surname; verify the spelling as 'Klimek' or correct it as appropriate.
- [Abstract] The phrasing 'The seemingly "imagination" and "creativity"' is awkward; consider rewording to 'The seemingly imaginative and creative qualities'.
- [Throughout] The term 'imaginary space' is used in a technical sense that may be confused with 'imaginative' or 'fictional'; consider defining the technical sense explicitly at first use.
Circularity Check
Central 'groundlessness limit' restates P4; rejection of LLM creativity is definitional, though the human-value discussion is independent.
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self definitional
[P4 (Properties, after Fig. 1) and 'Second, despite the benefit...' paragraph introducing the groundlessness limit]
"For newly-inferred instances within an abstract representation space that fulfill symbolic authenticity, they do not automatically establish grounding with the phenomenon it represents. ... For newly-generated instances from LLMs, they are not inherently grounded in the reality and human experience due to the above properties P1 and P4 ... Thus, the new instances in the LLMs space don't automatically establish correspondences in the reality and human experience, and lack their roots in the living experience in the real world."
The paper's central result, the 'groundlessness limit,' is obtained by substituting 'LLMs' into P4. P4 already asserts, for every abstract representation space, that newly inferred instances do not automatically establish grounding; LLM outputs are classified as such instances in E3. So the conclusion that machine-generated contents 'cannot automatically establish grounding in the reality and human experience' is P4 instantiated, not a separately derived finding. P4's own justification only says imaginary instances 'may not have correspondence' (from P1), and the target phrase 'the phenomenon it represents' slides between E2 (where an LLM text is automatically a language instance) and E1 (where the lack of grounding is asserted rather than derived).
full rationale
The paper is a conceptual essay, not an empirical study, so the usual fit-versus-prediction circularity does not apply directly. However, its load-bearing move is definitional: P4 defines 'grounding authenticity' as something newly inferred representations do not automatically possess, and the paper's 'groundlessness limit' is just that definition applied to LLMs. The conclusion that attributing 'imagination,' 'art,' or 'creativity' to LLMs is an overclaim depends on this definition plus a stipulated characterization of creativity as requiring translation between imaginary space and real world. The self-citation to [Jin et al., 2025] is ancillary and not load-bearing; the discussion of human contributions (training data, prompts, RLHF, interpretation) is substantive independent content. Because that independent content exists, the circularity is partial rather than total, hence a score of 6 rather than 8 or 10.
Assumptions & free parameters
assumptions (4)
- domain assumption P1: A complex phenomenon is irreducible and cannot be fully represented.
- domain assumption P2: An abstract space is a valid representation of a target phenomenon if it is spanned by instances grounded in that phenomenon.
- domain assumption P3: Inferences that follow the rules and assumptions of the representation space are symbolically authentic.
- ad hoc to paper P4: Symbolic authenticity does not imply grounding authenticity; new instances do not automatically connect to the represented reality.
invented entities (3)
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Symbolic authenticity
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Grounding authenticity
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Representation spiral
Cite this review
Pith. "Pith review of The value of human and machine in machine-generated creative contents." pith.science (2026). https://pith.science/paper/F5S7XIHX
@misc{pith2026250617808,
author = {Pith},
title = {Pith review of: The value of human and machine in machine-generated creative contents},
year = {2026},
howpublished = {\url{https://pith.science/paper/F5S7XIHX}},
note = {Machine review of arXiv:2506.17808}
}
read the original abstract
The seemingly "imagination" and "creativity" from machine-generated contents should not be misattributed to the accomplishment of machine. They are accomplishments of both human and machine. Without human interpretation, the machine-generated contents remain in the imaginary space of the large language models, and cannot automatically establish grounding in the reality and human experience.
Figures
Reference graph
Works this paper leans on
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[1]
Demetriou, Antony Bartlett, and Cynthia C
Patrick Altmeyer, Andrew M. Demetriou, Antony Bartlett, and Cynthia C. S. Liem. Position: stop making unscientific agi performance claims. In Proceedings of the 41st International Conference on Machine Learning, ICML'24. JMLR.org, 2024
work page 2024
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[2]
Critical complexity: collected essays
Paul Cilliers. Critical complexity: collected essays. Categories (Frankfurt am Main, Germany) ; Volume 6. De Gruyter, 2016. ISBN 1-5015-1079-7
work page 2016
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[3]
Feminist epistemology and philosophy of science: an introduction
Sharon Crasnow and Kristen Intemann. Feminist epistemology and philosophy of science: an introduction. Routledge, 2024. ISBN 9781032693767
work page 2024
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[4]
Thinking beyond the anthropomorphic paradigm benefits llm research, 2025
Lujain Ibrahim and Myra Cheng. Thinking beyond the anthropomorphic paradigm benefits llm research, 2025. URL https://arxiv.org/abs/2502.09192
arXiv 2025
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[5]
Weina Jin, Nicholas Vincent, and Ghassan Hamarneh. AI for Just Work: Constructing Diverse Imaginations of AI beyond “Replacing Humans” , 2025. URL https://arxiv.org/abs/2503.08720
arXiv 2025
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[6]
Introduction to Complex Systems
Stefan Thurner, Rudolf Hanel, and Peter Klimekl. Introduction to Complex Systems . In Introduction to the Theory of Complex Systems . Oxford University Press, 09 2018. ISBN 9780198821939. doi:10.1093/oso/9780198821939.003.0001. URL https://doi.org/10.1093/oso/9780198821939.003.0001
arXiv 2018
Reviewed August 15, 2026 · model on record in the stance chip above.
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