{"id":"09b8d939-3e49-47fd-8aca-1e69b94d1c4e","arxiv_id":"2506.17808","paper_version":1,"verdict":"CONDITIONAL","confidence":"HIGH","novelty_score":3.0,"correctness_risk":"medium","formal_verification":"none","parameter_count":0,"one_line_summary":"Machine-generated creative contents lack grounding in reality unless humans interpret them, so attributing creativity or imagination to the machine is an overclaim.","lead":"This essay argues that machine-generated art and text only seem creative because humans supply the meaning and real-world grounding. It proposes a conceptual framework that separates what machines can do, exploring symbolic spaces, from what they cannot do, connecting those spaces to lived experience.","discovery_kind":"review","skeptic_critique":{"model":"deepseek-v4-flash","headline":"P4 equivocates on the represented phenomenon: if the target is E2, LLM text is trivially grounded; if the target is E1, the premise is asserted without a bridge from P1.","rationale":"The paper is a position paper, not an empirical study, so I evaluate the internal logic of the conceptual argument. It has real strengths: the representation-spiral figure is clear, P1-P3 are reasonable, and the closing discussion of invisible human labor (training data, RLHF, prompts, interpretation) is a fair corrective to naive attributions of machine creativity. The reader identified P4 as the weakest premise; I agree it is load-bearing, but I would sharpen the concern. P4 is not exactly circular; it is ambiguous and overgeneralized. The level of representation shifts between E2 and E1, and no principle connects 'representation is incomplete' to 'newly inferred instances are not grounded.' Because the paper's conclusion is normative about the word 'creativity', the missing piece is a defended definition of creative grounding, not an empirical discovery. This does not make the paper worthless; it makes the central claim conditional on that definition. The reader's CONDITIONAL verdict remains appropriate, so I recommend no change.","tokens_in":3217,"tokens_out":5300,"duration_ms":60155,"concrete_test":"Formalize the argument in a two-sorted first-order theory with S (the LLM representation space), T (the target phenomenon), a grounding relation G, and an inference rule R. Premises: G holds for the spanning instances of S, and R preserves symbolic authenticity. Then test whether the sentence ∀x (New(x) ∧ Valid_Inference(x) → ¬G(x)) is derivable. Construct a countermodel: let S contain grounded sentences about Canberra, let R combine them into a new grounded sentence about Australia, and let G hold for that new sentence. If the countermodel is consistent, P4 is not a logical consequence of the stated premises. Separately, require the author to fix the intended target of P4 as either E2 or E1 and re-run the derivation for both readings.","verdict_should_be":"UNCHANGED","load_bearing_attack":"The central claim—that attributing creativity or imagination to LLMs is an overclaim because machine-generated contents lack grounding without human interpretation—rests on P4. P4 is load-bearing and under-specified. The phrase 'the phenomenon it represents' shifts between E2 and E1. If the target is E2 (language/art), then an LLM-generated sentence is automatically an instance of language, so it grounds in E2 and P4 is false or vacuous. If the target is E1 (reality/human experience), P4 does not follow from P1: incompleteness of a representation implies that not all newly inferred instances are grounded, but not that none are. Some newly inferred instances, even novel ones, can be true and referential (e.g., a model-generated description of a real place), and would be grounded in E1. To rescue P4, the paper would have to define grounding as something like 'grounded in lived experience via human interpretation'; but that definition is a normative stipulation close to the conclusion, not a derived property. The 'groundlessness limit' is therefore asserted rather than established. This is the soft spot: the argument either proves too much, applying equally to any abstract representation, or relies on an unargued definition of what counts as creative grounding.","agreement_with_reader":"partial"},"referee_report":{"model":"deepseek-v4-flash","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.","tokens_in":3475,"tokens_out":4188,"duration_ms":39858,"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":[{"comment":"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.","section":"P4 [Grounding authenticity] and 'Second, despite the benefit...'"},{"comment":"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.","section":"P4 applied to E2; 'Second, despite the benefit...'"},{"comment":"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.","section":"Introduction of P1–P4 and 'The groundlessness limit'"}],"minor_comments":[{"comment":"The product names 'chatGPT' and 'deepseek' should be capitalized as 'ChatGPT' and 'DeepSeek' for consistency and accuracy.","section":"Abstract and Section 1"},{"comment":"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.","section":"Figure 1 and its caption"},{"comment":"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.","section":"P2 [Valid representation]"},{"comment":"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.","section":"References"},{"comment":"The phrasing 'The seemingly \"imagination\" and \"creativity\"' is awkward; consider rewording to 'The seemingly imaginative and creative qualities'.","section":"Abstract"},{"comment":"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.","section":"Throughout"}],"recommendation":"major_revision","confidential_remarks":"This is a short philosophical position paper rather than an empirical study. The editor may wish to weigh whether the conceptual contribution is sufficiently novel for the journal's readership; the argument's central definitional move (P4) is likely to draw referee scrutiny. The reference list is thin but adequate for a position paper."},"author_rebuttal":null,"desk_editor":{"model":"deepseek-v4-flash","letter":"Dear colleague,\n\nThe paper to know: it is a conceptual argument that when LLM outputs look creative or imaginative, the credit belongs to humans too. The author introduces a 'representation spiral' (E1 reality/experience, E2 language/art, E3 LLMs) and four properties. The best part is the explicit account of how humans 'close the loop': choosing training data, giving preference feedback, writing prompts, and interpreting outputs. That framing is genuinely useful for people talking about 'AI creativity' loosely.\n\nWhat is actually new is the vocabulary — symbolic vs. grounding authenticity, the groundlessness limit — not the underlying idea. The claim that machines do not automatically create meaning without human interpretation is already in the literature the paper cites, and in older work on computational creativity. But restating it with a clear framework is a legitimate contribution for a position paper.\n\nThe soft spot is P4. The argument's weight sits on 'newly inferred instances do not automatically establish grounding with the phenomenon it represents,' and that premise is asserted, not derived. The stress-test equivocation is real: if the target is E2 (language/art), an LLM's output is a genuine instance of language, so it is trivially grounded. If the target is E1 (reality/human experience), P4 does not follow from P1 — incompleteness of a representation means some new instances may not ground, but not that none do. A model can generate a novel, true description of a real place. The paper even hedges once with 'may not,' then later claims LLM outputs 'lack their roots' in living experience. That jump needs an argument, not a stipulation. Without it, the groundlessness limit is more a normative definition than a discovery.\n\nThe paper is internally consistent and honestly written; it just undersells the load-bearing nature of P4. There is no empirical or formal derivation, so its soundness is conditional on the reader accepting the framework. If the author tightens P4 — say, by defining grounding explicitly as grounded in lived human experience and then arguing why that matters for the 'creativity' attribution — the piece would be much stronger.\n\nI would send this to peer review rather than desk reject it: it is clear, relevant, and the vocabulary will probably get used. A serious referee should ask for a revision that fixes the equivocation and engages with prior computational creativity literature.","headline":"Clear conceptual position on AI creativity, but the load-bearing premise P4 is asserted rather than established, and the central claim is not new.","tokens_in":3958,"tokens_out":2099,"would_cite":false,"duration_ms":20749,"reading_group":"maybe","serious_thinker":"yes","would_accept_peer_review":true},"rs_alignment":null,"lean_confirmation":null,"pith_extraction":{"msc":[],"pacs":[],"model":"deepseek-v4-flash","headline":"Machine-generated content becomes meaningful through human interpretation, so calling LLMs creative is an overclaim.","keywords":["large language models","generative AI","creativity","imagination","symbolic authenticity","grounding authenticity","anthropomorphism","human-AI collaboration"],"falsifier":"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.","tokens_in":3002,"feed_emoji":"💭","tokens_out":7762,"duration_ms":68430,"temperature":0.7,"pith_summary":"This article argues that the apparent imagination and creativity in LLM outputs belong to humans, not machines. It sets up a three-level representation chain—reality and human experience, language and art, then LLMs as abstract representations of those—and claims that each level is a lossy abstraction of the one below. From this, the paper derives a groundlessness limit: newly generated LLM content can be valid inside the model's symbolic space without being connected to actual reality or human experience. Therefore, machine-generated texts and images only become meaningful, and only count as grounded creative works, when humans interpret them. The point matters because it reframes debates over AI creativity and clarifies what human value remains in an era of generative AI.","feed_headline":"Machines expand imagination; humans supply the meaning","feed_subtitle":"LLM outputs stay ungrounded until humans interpret them, so calling the model creative is an overclaim.","key_machinery":"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.","core_discovery":"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.","pith_inferences":["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."],"forward_implications":["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."],"supporting_citations":[{"why":"It supplies the epistemological framing the paper uses to analyze what we know about reality and how we know it.","marker":"[Crasnow and Intemann, 2024]"},{"why":"They jointly support P1 irreducibility, the claim that a complex phenomenon cannot be fully captured by a representation, which underlies the groundlessness limit.","marker":"[Thurner et al., 2018, Cilliers, 2016]"},{"why":"It supplies the claim that LLMs are information agents whose valid representation depends on a human-created epistemic basis.","marker":"[Jin et al., 2025]"},{"why":"They support the paper's conclusion that attributing imagination or creativity to LLMs is an anthropomorphic overclaim.","marker":"[Ibrahim and Cheng, 2025, Altmeyer et al., 2024]"}],"fun_headline_variants":["LLM creativity is a myth without human grounding","Machines expand possibility; humans supply meaning","AI imagination is borrowed, not possessed","Uninterpreted AI output stays ungrounded","Machine creativity: an overclaim without human ties"],"cache_read_input_tokens":3200,"weakest_assumption_plain":"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.","fun_headline_variants_meta":{"raw":{"variants":["LLM creativity is a myth without human grounding","Machines expand possibility; humans supply meaning","AI imagination is borrowed, not possessed","Uninterpreted AI output stays ungrounded","Machine creativity: an overclaim without human ties"]},"model":"deepseek-v4-flash","effort":"low","cost_usd":0.000126,"raw_usage":{"total_tokens":1011,"prompt_tokens":744,"completion_tokens":267,"prompt_tokens_details":{"cached_tokens":384},"prompt_cache_hit_tokens":384,"prompt_cache_miss_tokens":360,"completion_tokens_details":{"reasoning_tokens":199}},"tokens_in":360,"tokens_out":267,"duration_ms":2867,"temperature":1.0,"reasoning_tokens":199,"cache_read_input_tokens":384,"cache_creation_input_tokens":0},"cache_creation_input_tokens":0},"created_at":"2026-08-15T18:59:43.748527+00:00","model_set":{"reader":"deepseek-v4-flash"},"falsifier":"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.","supporting_citations":[{"cited_title":"Feminist epistemology and philosophy of science: an introduction","cited_arxiv_id":null,"evidence_quote":"It supplies the epistemological framing the paper uses to analyze what we know about reality and how we know it."}],"review_version":1}