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In LLM-assisted VR scene editing, users systematically underestimate confabulations, and under high error load their awareness saturates so that perceived error counts become uncorrelated with actual ones—meaning human oversight cannot serv

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2026-08-01 03:15 UTC pith:6O3CTU2M

load-bearing objection Useful empirical taxonomy and a real construct, but the headline claim about saturated user vigilance is partly built on a mismatched questionnaire, and the 'actual' ground truth needs reliability evidence. the 4 major comments →

arxiv 2607.23213 v1 pith:6O3CTU2M submitted 2026-07-25 cs.HC

A Taxonomy of Confabulations and the Perception-Reality Gap in LLM-Assisted Immersive Scene Editing

Junlong Chen , Per Ola Kristensson This is my paper
classification cs.HC
keywords confabulationhallucinationLLMvirtual reality3D scene editingperception-reality gaphuman-AI collaborationerror detection
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved

The pith

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

This paper argues that when people edit 3D scenes in virtual reality with an LLM-based assistant, the system frequently produces confabulations—plausible but incorrect edits—and, crucially, that users cannot be relied on to notice them. In a study of 24 non-expert users across three VR scenes, 27 percent of interaction turns contained at least one confabulation, and users consistently under-reported them: in the highest-error scene, the median perceived count was 4.0 while the median actual count was 10.5, and individual perceived counts were not significantly correlated with actual counts. The paper concludes that confabulation awareness saturates under load, making human vigilance structurally unreliable as a detection mechanism and putting the burden of mitigation on the system. It also contributes a nine-type taxonomy of confabulations and a construct, the perception-reality gap, for measuring this divergence.

Core claim

The paper's central discovery is an empirical demonstration that user awareness of LLM errors in immersive editing is bounded: in low-error conditions people track confabulations well (Spearman rho=0.71), but in high-error conditions perceived counts stop tracking actual counts (office rho=0.15; city rho=0.30, n.s.), a pattern the authors call saturation. They arrive at this by building a JSON-in/JSON-out LLM scene-editing workflow, logging 1,612 interaction turns from 24 users, manually coding every turn for nine confabulation types and 24 subtypes (437 confabulation turns total), and comparing these actual counts with users' post-experience questionnaire reports. The discovered asymmetry i

What carries the argument

The load-bearing construct is the perception-reality gap: the discrepancy between the number and types of confabulations users believe they encountered (measured by post-experience questionnaire) and those that actually occurred (measured by manual log coding). The companion mechanism is saturation: as confabulation load rises, perceived counts plateau and lose correlation with actual counts, rendering human detection ineffective. The taxonomy of nine confabulation types (spatial placement errors, reference frame ambiguity, orientation errors, command misinterpretation without disclosure, false scene claims, edit scope errors, material/style errors, missing information without disclosure, an

Load-bearing premise

The reference counts of 'actual' confabulations come from a single researcher's manual coding of all 1,612 interaction turns, with no second coder, no inter-rater reliability, and no formal protocol beyond inductive open coding, so every saturation and perception-reality result depends on that labeling being correct.

What would settle it

Re-code the 1,612 interaction turns with two independent coders following a pre-registered protocol, or automatically detect confabulations by mechanical checks (e.g., geometric overlap detection, out-of-region placement, comparison of claimed vs. applied edits). If a second coding pass yields materially different counts—or if the automatic check argues with the manual labels—the saturation and uncorrelated-counts results could vanish. A simpler check: a direct replication with twice the participants plus instrumented ground-truth logging would show whether perceived counts again plateau near

Watch this falsifier. Get emailed when new claim-graph text bears on it.

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If this is right

  • System-side verification becomes the primary safeguard: scene state should be re-checked against the LLM's prior claims before each new interaction round, rather than trusting either the model's text or the user's memory.
  • Confident textual outputs from the LLM should not be treated as evidence that an edit succeeded; independent structural validation is needed for claimed deletions, undos, and placements.
  • Mitigation can exploit the perceptual asymmetry: spatial errors are self-revealing in VR, so effort should concentrate on semantic interpretation errors and silent failures, which users rarely notice.
  • Task framing and the presence of a reference state shape error tolerance: stylistic tasks without a clear reference induced higher trust and lower perceived disruption, suggesting task design can be a mitigation lever.
  • Out-of-vocabulary internal tokens (like the '_st' suffix) should be flagged as unknown rather than silently mapped to a plausible meaning, since such silent misreadings dominated the stylistic task.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The saturation finding likely generalizes beyond VR to any human-AI interface where error rates are high, implying that user vigilance can be modeled as a limited-capacity channel; a quantitative model of that capacity could predict when monitoring will fail.
  • The 'actual' counts could be obtained automatically in future work—using geometric collision checks and out-of-region tests to flag confabulations by rule—which would let the perception-reality gap be studied at scale without manual labeling and would test whether the manual coding explains the results.
  • The taxonomy, being task-derived rather than model-derived, may transfer to other structured-data LLM agents (scene graphs, CAD, code) where silent misinterpretation and false completion claims are likely to appear in similar proportions.
  • The 46 percent false-alarm rate for object omission in the city scene, driven by false 'already done' claims, hints that users infer failure from system denial; studying denial-based inference could turn misleading output into a diagnostic signal.

Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

4 major / 5 minor

Summary. The paper presents an exploratory user study (N=24) of LLM-assisted immersive 3D scene editing, using a JSON-in/JSON-out GPT-5.2 pipeline in three VR scenes (office, loft, city) with distinct task types. Based on manual review of 1,612 interaction turns, the authors construct a nine-type taxonomy of confabulations, report their prevalence and perceived disruptiveness, and introduce the construct of a perception–reality gap by comparing log-based actual confabulation counts with post-experience questionnaire reports. The central claim is that under high confabulation load, user awareness saturates and becomes uncorrelated with actual counts, so human oversight is structurally unreliable and mitigation must be system-side. The paper also derives four design implications (DI1–DI4) and acknowledges its own limitations, including a footnote admitting a structural blind spot in the questionnaire.

Significance. If the empirical foundation holds, the paper makes a timely contribution: it is one of the few studies to characterize LLM confabulations in immersive scene editing from real interaction data, and it offers a concrete taxonomy plus a measurable construct (perception–reality gap) that can be reused by other XR/LLM researchers. The design implications are actionable and grounded in observed failure modes. The authors are also transparent about the questionnaire mismatch and about the limited power for type-level analyses. However, the two load-bearing pillars—the manual coding of ground-truth confabulations and the comparability of perceived versus actual counts—are not yet sufficiently validated. The central conclusion about saturation therefore requires additional evidence before it can be accepted at face value.

major comments (4)
  1. [§4.1] The ground-truth actual confabulation counts rest entirely on manual coding by the authors: 'Each interaction turn was manually reviewed ... to identify whether confabulation was present,' and labels were assigned via inductive open coding. No inter-rater reliability, second coder, codebook, or shared annotation dataset is reported. Since every prevalence percentage, every actual-count median, and every perception–reality statistic is computed from this coding, a systematically lenient or strict coder would change the headline results. The authors should report at least a reliability check (e.g., Cohen's kappa on a random subset, dual coding of a sample) and make the coding protocol or annotated data available. This is a necessary validity check for the central claim, not a stylistic suggestion.
  2. [§4.3, footnote 3] The perceived-confabulation instrument is systematically incomplete relative to the taxonomy. Footnote 3 states that FSC, AEF, and MSE 'did not correspond to questionnaire items, which resulted in a structural blind spot.' Yet actual counts include those categories: FSC accounts for 8% of confabulation turns in the city scene, FSC+AEF account for 10% in the office scene, and MSE accounts for 25% in the loft scene. Because the perceived-count measure cannot register these types by construction, the observed underestimation and the non-significant correlations in office/city may be inflated by instrument asymmetry rather than by a genuine perceptual ceiling. The paper acknowledges this in a footnote but still uses the result to claim that 'user vigilance is structurally unreliable as a detection mechanism' (Section 5). The authors should provide a sensitivity analysis that excludes or sepa
  3. [§4.3] The statistical evidence for 'saturation' and 'unrelated' counts is weaker than the prose suggests. The office (ρ=0.15, p=.47) and city (ρ=0.30, p=.16) Spearman correlations are non-significant, but N=24 gives low power, and ρ=0.30 is a moderate effect; non-significance does not establish that perceived and actual counts are unrelated. Moreover, no formal test of saturation is presented—the claim rests on comparing medians and correlations across scenes, with no model of a plateau or a nonlinearity. The authors should either fit a saturation model, test for a difference in correlation magnitudes across high/low-load conditions, or soften the 'structurally unreliable' conclusion to 'not reliably correlated in this sample.'
  4. [§3.4 / §4.3] The operationalization of the perceived confabulation count is underspecified. The post-experience questionnaire asked about 'the type, frequency level of disruption, and a short description of the confabulation,' but it is not stated exactly how these answers were converted into a numeric perceived count, how multi-type reports were handled, or whether participants were forced to choose from predefined categories. Because the perception–reality gap is the paper's central construct, the exact instrument wording, the mapping rules from questionnaire categories to taxonomy types, and the aggregation rule should be reported in enough detail for replication.
minor comments (5)
  1. [§4.2] Typo: 'χ2 =32,8' should presumably be 'χ2 =32.8'.
  2. [§3.4 / §4.2] The disruption question uses the term 'hallucination' ('When a hallucination occurred, it disrupted my task'), while the paper deliberately defines and uses 'confabulation.' Please make the terminology consistent, or explain why the questionnaire used a different term.
  3. [Figure 5] The legend for hatched cells combines two different conditions ('category absent in this scene' and 'questionnaire type could not be mapped to a taxonomy type'). These should be separated visually or in the caption, because they carry different implications for interpretability.
  4. [§4.1] Table 1 notes that counts do not sum to totals because interaction turns can receive multiple labels. It would help to also report the number of turns with only one label versus multiple labels, since multi-label coding affects how the prevalence percentages should be read.
  5. [§5.2] The Limitations section mentions the small sample and single-system design, but does not mention the absence of inter-rater reliability or the questionnaire blind spot. Adding both to the limitations would help calibrate reader expectations.

Circularity Check

0 steps flagged

No significant circularity; the perception-reality gap is an empirical comparison of independent measurements, not a construction, and the only self-citations are background.

full rationale

The paper's derivation chain is empirical rather than formal. The taxonomy (Section 4.1) is built by inductive open coding of 1,612 logged interaction turns; perceived confabulations (Section 4.3) come from a separate post-experience questionnaire. The perception-reality gap is the difference between these two independently collected measurements, and no parameter is fitted from one to predict the other. The saturation claim (Section 5) is therefore not circular: it is an interpretation of observed counts and correlations, and it does not reduce to the definition of either measure. The acknowledged 'structural blind spot' (footnote 3: FSC, AEF, and MSE did not correspond to questionnaire items) is a genuine validity limitation that should temper the strength of the perceived-actual comparison, but it is disclosed and it does not make the central gap equivalent to an input by construction; the largest actual category (SPE) is present in the questionnaire, and the gap persists in the count comparison. Self-citations ([6], [7]) support background claims about LLM+XR editing and reference-frame ambiguity; they are not load-bearing for the taxonomy, the gap analysis, or the design implications. Accordingly, no circular step is identified.

Axiom & Free-Parameter Ledger

0 free parameters · 4 axioms · 2 invented entities

The paper does not fit any numeric parameters to data; its contributions are qualitative/empirical categories and comparative statistics. The main burden is therefore not parametric but definitional: the 'actual confabulations' anchor is author-based manual coding, justified as inductive open coding without inter-rater reliability, and the perceived-report instrument was designed before the taxonomy existed, creating a known structural blind spot for three confabulation types.

axioms (4)
  • domain assumption The manual log-labeling of 1,612 interaction turns into confabulation presence/type is treated as an objective ground truth.
    All prevalence counts, detection rates, and perception-reality-gap analyses (Section 4.1, Table 1, Figure 5) depend on these labels, yet no inter-rater reliability or independent coding is reported.
  • domain assumption The specific GPT-5.2 model with the authors' prompt and Unity pipeline is a representative testbed for general LLM-assisted immersive scene editing.
    Generalization from a single system and model to other workflows is asserted in the introduction and discussion, but only one model/pipeline is tested (Section 3.1, 5.2).
  • domain assumption Post-hoc questionnaire categories can be mapped to the inductively derived taxonomy for comparing actual vs perceived confabulation types.
    Section 4.3 Footnote 3 states that several observed types (FSC, AEF, MSE) had no questionnaire counterpart, producing a 'structural blind spot' that biases type-level comparisons.
  • domain assumption The three scenes and three task types span the relevant design space of LLM-assisted immersive 3D scene editing.
    The paper claims the scenes are designed to reflect many use cases (Section 3.3), but the conclusions about task-type-specific confabulations rest on one indoor object-placement scene, one stylistic scene, and one outdoor functional scene.
invented entities (2)
  • Perception-reality gap independent evidence
    purpose: A construct quantifying the discrepancy between the number/types of confabulations users believe they encountered versus those that actually occurred.
    It is operationally defined through two independent measurements (questionnaire reports of perceived confabulations vs manual log annotation of actual confabulations) and the paper offers a falsifiable handle: it predicts underestimation that saturates under load. This is an empirical construct, not an unobserved entity.
  • Nine-type confabulation taxonomy independent evidence
    purpose: Classifies LLM system failures in immersive scene editing into clusters/types/subtypes (SPE, RFA, FSC, CMD, ESE, ORI, MSE, MID, AEF).
    The taxonomy is grounded in logged interaction data and could be applied by independent coders to new datasets, making it falsifiable, though the paper itself provides no reliability evidence.

reviewed 2026-08-01 · how reviews work

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Cite this review

Pith. "Pith review of A Taxonomy of Confabulations and the Perception-Reality Gap in LLM-Assisted Immersive Scene Editing." pith.science (2026). https://pith.science/paper/6O3CTU2M

@misc{pith2026260723213,
  author       = {Pith},
  title        = {Pith review of: A Taxonomy of Confabulations and the Perception-Reality Gap in LLM-Assisted Immersive Scene Editing},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/6O3CTU2M}},
  note         = {Machine review of arXiv:2607.23213}
}
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read the original abstract

Large language models (LLMs) are being increasingly integrated into immersive environments and design workflows, providing application prospects in areas such as rapid scene prototyping for non-expert users and scene understanding capabilities for accessibility design. While many workflows that incorporate LLMs in immersive spaces are proposed, such systems can exhibit errors, potentially resulting in frustration, loss of user trust, and compromised user safety. This paper studies the underexplored area of LLM confabulations in immersive 3D scene editing contexts. Through an exploratory study with 24 non-expert users, we construct a taxonomy of the different types of confabulation observed in LLM-assisted immersive 3D scene editing. We report their prevalence and disruptiveness, and define the construct perception-reality gap to help understand the gap between the actual and perceived occurrence of confabulations. We highlight the observed saturation of confabulation awareness under load and conclude by discussing design implications for confabulation mitigation in future LLM-assisted systems in immersive 3D scenes.

Figures

Figures reproduced from arXiv: 2607.23213 by Junlong Chen, Per Ola Kristensson.

Figure 1
Figure 1. Figure 1: Examples of different types of confabulations identified through an empirical user study of LLM-assisted immersive scene [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Workflow of the LLM-assisted immersive scene editing system adopted in the study. [PITH_FULL_IMAGE:figures/full_fig_p006_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: User interface of the LLM-assisted immersive scene editing system with the main panel containing selected objects and [PITH_FULL_IMAGE:figures/full_fig_p006_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Violin plots of the user perceived trust ratings on a scale of 1 to 7 (left), unweighted NASA-TLX overall load from a scale of 1 to [PITH_FULL_IMAGE:figures/full_fig_p010_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Actual and perceived confabulation counts in both trials of all participants in the office, loft, and city scenes (left) with [PITH_FULL_IMAGE:figures/full_fig_p012_5.png] view at source ↗

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Reference graph

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This paper was first reviewed by deepseek-v4-flash on August 1, 2026.