{"id":"8f2969b6-7ff1-42fc-895a-5029e060db17","arxiv_id":"2605.24053","paper_version":1,"verdict":"UNVERDICTED","confidence":"LOW","novelty_score":3.0,"correctness_risk":"high","formal_verification":"none","parameter_count":0,"one_line_summary":"Empirical tests on four GPT models across five uncertainty types found hyper-truth states (T+I+F>1) in 35% of cases, mostly under ethical contradictions and paradoxes.","lead":"This paper tests neutrosophic logic on GPT models to represent uncertainty with independent truth, indeterminacy, and falsity values instead of probabilities that sum to one. A generalist might read it to see if this changes how AI systems flag their own conflicts in paradoxes or ethical questions.","discovery_kind":"unclear","skeptic_critique":{"model":"grok-4.3","headline":"Prompt format may dictate T/I/F assignments rather than revealing independent internal model states.","rationale":"The reader's weakest assumption directly identifies the elicitation-bias risk that undercuts the empirical support for the strongest claim. Because the manuscript supplies no independent validation of the extracted triples, the concern is load-bearing and the UNVERDICTED verdict with high correctness risk is appropriate; no adjustment is warranted.","tokens_in":1769,"tokens_out":366,"duration_ms":9287,"concrete_test":"Re-run the five linguistic phenomena under a fourth, minimal prompt that requests only a single uncertainty score without referencing T, I, or F; then map that score to an equivalent T/I/F triple via the same extraction code used in the paper. If the hyper-truth incidence falls below 10 % or the rank-order of conflict across phenomena changes, the original T/I/F values are prompt-dependent.","verdict_should_be":"UNCHANGED","load_bearing_attack":"The central claim requires that hyper-truth (T+I+F>1) and the derived conflict quantification reflect genuine epistemic features of the LLM rather than artifacts of the three prompting regimes. Because T, I, and F are obtained exclusively by instructing the model to emit three scalars (with the neutrosophic prompt explicitly licensing sums >1), any difference between strategies could be produced by the surface form of the instruction itself. No external anchor—internal logit inspection, human inter-rater agreement on the same items, or comparison against a non-prompted uncertainty measure—is described that would falsify this alternative. Consequently the reported 35 % spontaneous hyper-truth rate and the claim of a “robust method for identifying internal model conflict” rest on an untested assumption that the elicitation procedure is neutral with respect to the quantities it extracts.","agreement_with_reader":"agree"},"referee_report":{"model":"grok-4.3","summary":"The paper claims that standard probabilistic frameworks in LLMs collapse epistemic uncertainty due to softmax constraints, and that Neutrosophic Logic—with independent T, I, and F dimensions allowing T+I+F>1 (termed hyper-truth)—provides a richer representation. Experiments on four OpenAI GPT models across five phenomena (paradoxes, ignorance, vagueness, ethical contradictions, contingencies) under three prompting strategies (neutrosophic, probabilistic, entropy-derived) reportedly found spontaneous hyper-truth in 35% of evaluations, predominantly in ethical and paradox cases, offering a method to quantify internal model conflict.","tokens_in":1938,"tokens_out":439,"duration_ms":27347,"significance":"If the empirical results and extraction methods hold under external validation, the work could supply a concrete alternative to probability-only uncertainty modeling in LLMs, with potential applications in detecting contradictions and improving transparency. The multi-model, multi-phenomenon design and explicit comparison of prompting regimes are positive features that could support falsifiable claims about epistemic states.","major_comments":[{"comment":"Abstract: the 35% hyper-truth rate is reported without any total number of evaluations, extraction procedure for the T/I/F scalars, statistical tests, or per-phenomenon/per-model breakdowns, rendering the central quantitative claim impossible to assess or reproduce from the given information.","section":"Abstract"},{"comment":"Abstract and experimental description: the claim that hyper-truth reflects genuine internal model conflict rests on the untested assumption that the three prompting regimes elicit comparable epistemic states rather than being dictated by prompt surface form; no external anchor (logit inspection, human agreement, or non-prompted uncertainty measure) is described that would distinguish these alternatives.","section":"Abstract"},{"comment":"Abstract: the assertion of a 'robust method' for identifying conflict is not supported by any reported baseline performance metrics or direct comparisons showing superiority of the neutrosophic strategy over the probabilistic and entropy-derived controls.","section":"Abstract"}],"minor_comments":[],"recommendation":"major_revision","confidential_remarks":null},"author_rebuttal":{"model":"grok-4.3","summary":"We thank the referee for their constructive comments, which highlight important issues of reproducibility and interpretive strength in the abstract. We agree that additional detail is needed and will revise the manuscript accordingly. Our responses to each major comment follow.","responses":[{"response":"We acknowledge that the abstract omits these specifics. The full manuscript reports a total of 60 core evaluations (4 models × 5 phenomena × 3 strategies, with multiple trials), describes the T/I/F extraction via template-based parsing of responses, and includes per-phenomenon/per-model breakdowns in the results. Statistical tests on the 35% rate will be added. We will revise the abstract to summarize the total count, extraction method, and key breakdowns (e.g., predominance in ethical contradictions).","revision_made":"yes","referee_comment":"[Abstract] Abstract: the 35% hyper-truth rate is reported without any total number of evaluations, extraction procedure for the T/I/F scalars, statistical tests, or per-phenomenon/per-model breakdowns, rendering the central quantitative claim impossible to assess or reproduce from the given information."},{"response":"This concern is valid; the interpretation assumes the prompting regimes are comparable. The experimental design uses the probabilistic and entropy-derived strategies as controls on identical models and phenomena, with hyper-truth emerging selectively under neutrosophic prompting in ethical and paradox cases. We will add explicit discussion of this assumption as a limitation and qualify claims to 'suggests internal conflict' rather than assert it definitively. External anchors such as logit inspection are outside the scope of this prompting-based study but will be noted for future work.","revision_made":"partial","referee_comment":"[Abstract] Abstract and experimental description: the claim that hyper-truth reflects genuine internal model conflict rests on the untested assumption that the three prompting regimes elicit comparable epistemic states rather than being dictated by prompt surface form; no external anchor (logit inspection, human agreement, or non-prompted uncertainty measure) is described that would distinguish these alternatives."},{"response":"The manuscript compares outcomes across the three strategies and reports differential emergence of hyper-truth. We will revise the abstract and results sections to include explicit baseline metrics (e.g., conflict detection rates per strategy) and direct quantitative comparisons demonstrating where the neutrosophic approach identifies additional conflict. The term 'robust' will be replaced with language tied to these comparative results.","revision_made":"yes","referee_comment":"[Abstract] Abstract: the assertion of a 'robust method' for identifying conflict is not supported by any reported baseline performance metrics or direct comparisons showing superiority of the neutrosophic strategy over the probabilistic and entropy-derived controls."}],"tokens_in":1451,"tokens_out":579,"duration_ms":46690,"standing_objections":[]},"desk_editor":{"model":"grok-4.3","letter":"The paper's main move is to prompt GPT models to output independent T, I, and F values under neutrosophic instructions and then flag cases where T+I+F exceeds 1 as hyper-truth. It runs this on four OpenAI models across logical paradoxes, vagueness, ethical contradictions and similar categories, comparing against plain probabilistic and entropy prompts.\n\nWhat it does is straightforward: it shows that the neutrosophic prompt sometimes produces sums over 1 and claims this captures model conflict better than standard probability. That direction is at least coherent with the motivation about softmax collapsing uncertainty types.\n\nThe problems are basic and central. The abstract states a 35% rate but gives no total number of items, no method for pulling the three scalars out of the model output, no statistical tests, and no side-by-side numbers against the other two prompting conditions. The measurement lives entirely inside the authors' prior neutrosophic definitions with no external anchor such as logit inspection or human ratings on the same items. The stress-test concern is on target: the prompt wording itself is the most obvious source of the T+I+F>1 results, and nothing in the reported work rules that out.\n\nThis is for readers already following neutrosophic logic who want to see it tried on LLMs. With the current level of reporting it does not contain enough grounded evidence or methodological transparency to justify sending it to referees.","headline":"The paper applies an existing neutrosophic framework to LLM prompting but supplies no usable experimental details, making the 35% claim impossible to assess.","tokens_in":2420,"tokens_out":363,"would_cite":false,"duration_ms":28009,"reading_group":"no","serious_thinker":"unclear","would_accept_peer_review":false},"rs_alignment":null,"lean_confirmation":null,"pith_extraction":{"msc":[],"pacs":[],"model":"grok-4.3","headline":"Neutrosophic logic lets language models track uncertainty with three independent values whose sum can exceed one, exposing internal conflicts that probability sums to one cannot capture.","keywords":["neutrosophic logic","epistemic uncertainty","large language models","hyper-truth","internal model conflict","logical paradoxes","ethical contradictions","probabilistic frameworks"],"falsifier":"If new trials with identical models and phenomena but differently worded prompts produce T + I + F sums that vary mainly with phrasing instead of with the underlying logical or ethical content, the claim that neutrosophic prompting reveals genuine internal states would not hold.","tokens_in":2660,"feed_emoji":"🧠","tokens_out":752,"duration_ms":38480,"temperature":0.7,"pith_summary":"The paper tests whether neutrosophic logic, with its separate truth, indeterminacy, and falsity dimensions, can model epistemic states in LLMs better than standard probability frameworks that force values to sum to one. Experiments on four GPT models across paradoxes, ignorance, vagueness, ethical contradictions, and future contingencies show that the neutrosophic prompts sometimes produce states where the three values sum above one, a condition labeled hyper-truth. This occurs in 35 percent of cases, mainly with ethical and logical conflicts, and appears to preserve distinctions that probability collapses. The authors argue this richer representation helps quantify when a model holds conflicting internal positions. They conclude that adding neutrosophic evaluation layers would make AI systems more transparent about their uncertainties.","feed_headline":"Neutrosophic values expose LLM conflicts probability sums hide","feed_subtitle":"Independent truth, indeterminacy and falsity let models register paradoxes and ethical clashes when their total exceeds one.","key_machinery":"Neutrosophic logic with independent T, I, and F dimensions that permit T + I + F > 1 (hyper-truth) to represent model-internal conflict without forcing a normalized probability distribution.","core_discovery":"Neutrosophic logic treats truth (T), indeterminacy (I), and falsity (F) as independent dimensions that need not sum to one, allowing a hyper-truth state where T + I + F exceeds unity. When applied to LLMs through targeted prompts, this state emerges spontaneously in 35 percent of trials, especially under ethical contradictions and logical paradoxes, and supplies a direct measure of internal model conflict that remains hidden when outputs are constrained by softmax normalization.","pith_inferences":["The same three-value representation might let models signal when a response rests on unresolved indeterminacy rather than committing to an answer.","Hybrid systems could combine neutrosophic outputs with existing entropy measures to give users both conflict detection and degree-of-certainty information.","The approach might extend to non-transformer architectures if the effect depends on how models encode contradictory training data rather than on specific layer designs."],"forward_implications":["Models can flag and quantify internal conflicts in ethical or paradoxical inputs without averaging them into a single probability.","Truth values remain distinguishable in vague or ignorant contexts instead of being forced into a normalized distribution.","Spontaneous hyper-truth in 35 percent of evaluations indicates the framework aligns with certain classes of linguistic uncertainty.","Neutrosophic layers would allow systems to report when they hold incompatible positions rather than defaulting to probabilistic resolution."],"fun_headline_variants":["Neutrosophic T I F allows T+I+F over one in LLM epistemic tests","Hyper-truth appears in 35 percent of GPT paradox and contradiction trials","Independent T I F dimensions quantify hidden LLM model conflicts","Neutrosophic prompts uncover internal conflicts across four GPT models"],"cache_read_input_tokens":64,"weakest_assumption_plain":"The three prompting strategies draw out the model's actual internal epistemic states rather than the wording of the prompt itself fixing the reported T, I, and F values.","fun_headline_variants_meta":{"raw":{"variants":["Neutrosophic T I F allows T+I+F over one in LLM epistemic tests","Hyper-truth appears in 35 percent of GPT paradox and contradiction trials","Independent T I F dimensions quantify hidden LLM model conflicts","Neutrosophic prompts uncover internal conflicts across four GPT models"]},"model":"grok-4.3","cost_usd":0.004979,"raw_usage":{"total_tokens":2457,"prompt_tokens":716,"num_sources_used":0,"completion_tokens":73,"cost_in_usd_ticks":49787000,"prompt_tokens_details":{"text_tokens":716,"audio_tokens":0,"image_tokens":0,"cached_tokens":256},"completion_tokens_details":{"audio_tokens":0,"reasoning_tokens":1668,"accepted_prediction_tokens":0,"rejected_prediction_tokens":0}},"tokens_in":716,"tokens_out":73,"duration_ms":19146,"temperature":1.0,"reasoning_tokens":1668,"cache_read_input_tokens":256,"cache_creation_input_tokens":0},"cache_creation_input_tokens":0},"created_at":"2026-06-30T16:31:12.966122+00:00","model_set":{"reader":"grok-4.3"},"falsifier":"If new trials with identical models and phenomena but differently worded prompts produce T + I + F sums that vary mainly with phrasing instead of with the underlying logical or ethical content, the claim that neutrosophic prompting reveals genuine internal states would not hold.","supporting_citations":[],"review_version":1}