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REVIEW 5 major objections 5 minor 11 references

This paper claims that omission and fabrication hallucinations in multimodal LLMs have distinct mechanisms, and that its training-free VPFC method reduces omissions without adding fabrications.

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Omission and fabrication hallucinations in MLLMs are claimed to have distinct causes, and the new VPFC method reduces omissions without adding fabrications.

T0 review reviewed 2026-08-05 challenge →

load-bearing objection Useful two-cause framing and a solid plug-and-play decoder intervention, but the load-bearing spatial-concentration heuristic is never quantitatively tested. the 5 major comments →

arxiv 2509.00371 v1 pith:G67ARZZC submitted 2025-08-30 cs.CV

Two Causes, Not One: Rethinking Omission and Fabrication Hallucinations in MLLMs

Guangzong Si , Hao Yin , Xianfei Li , Qing Ding , Wenlong Liao , Tao He , Pai Peng This is my paper
classification cs.CV
keywords object hallucinationmultimodal large language modelsomission hallucinationfabrication hallucinationvisual attention interventionvisual-semantic attention potential fieldtraining-free mitigationvisual potential field calibration
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.

The reading

Multimodal large language models sometimes fail to mention objects that are present (omission) and sometimes mention objects that are absent (fabrication). This paper argues that these two failure modes do not share one cause, as most prior mitigation methods assume. It claims omissions come from low confidence when the model maps visual features it already captured into words, while fabrications come from spurious associations between visual and textual concepts learned from co-occurrence statistics. The paper introduces a training-free intervention, Visual Potential Field Calibration (VPFC), that increases attention to spatially concentrated visual regions and steers hidden states accordingly, reporting reductions in omission errors without triggering new fabrication errors on several benchmarks. If right, it means hallucination mitigation should be split into two targeted problems instead of one.

Core claim

The paper's central claim is that omission hallucinations and fabrication hallucinations have fundamentally different roots. Using attention maps from localization heads, the paper shows that when the model omits an object, it often has already attended to the correct visual region and captured the object's features; the failure is low confidence in mapping those features to the linguistic expression. In contrast, fabrication hallucinations occur when the model confidently maps visual features to a wrong semantic concept, such as interpreting part of a toilet as a sink, driven by co-occurrence biases in training data. Attention intervention experiments show that boosting attention to high-at

What carries the argument

The Visual-Semantic Attention Potential Field (VSAPF) is a conceptual map in which each visual token sits in a landscape: High-Credibility Visual Regions (HCVRs), the top-attention tokens, sit at potential wells where the model readily affirms presence, and Low-Credibility Visual Regions (LCVRs) sit at peaks where the model tends toward negation. VPFC uses the observation that HCVRs are spatially concentrated when the queried object is present and dispersed when it is absent. It computes the centroid of HCVRs, enhances a square region around that centroid, computes a hidden-state steering direction from a mild attention boost, and applies this direction to selected attention heads chosen by

Load-bearing premise

The method's mechanism rests on the heuristic that when the queried object is actually present, the high-attention regions are spatially concentrated, and when it is absent they are dispersed; the paper reports no quantitative evidence for this relationship.

What would settle it

Measure the spatial dispersion (e.g., area of the convex hull or sum of pairwise distances) of the top-attention visual tokens for present versus absent objects across a dataset with ground-truth object presence. If present-object queries do not yield significantly more concentrated HCVRs than absent-object queries on average, the centroid-enhancement mechanism loses its rationale.

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

If this is right

  • VPFC is a plug-and-play, training-free method, so it can be applied to existing multimodal LLMs without retraining or fine-tuning.
  • Unified mitigation methods that indiscriminately boost high-attention regions should be expected to reduce omissions at the cost of increasing fabrications, because they amplify confidence in object presence regardless of whether the object is actually there.
  • Evaluation of hallucination mitigation should separate omission and fabrication errors rather than relying on aggregate accuracy or F1 scores, since a method can improve one while worsening the other.
  • A targeted fabrication-suppression strategy remains an open problem; VPFC only avoids adding fabrications while primarily addressing omissions.
  • The observed failure of contrastive-decoding baselines on the POPE Adversarial subset is explained as a consequence of treating both hallucination types with a single intervention.

Where Pith is reading between the lines

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

  • Beyond the paper: the HCVR-concentration heuristic could be tested directly by measuring the spatial dispersion of top-attention tokens on present versus absent object queries; if it does not hold on out-of-distribution objects or long-tail categories, VPFC would need a learned presence estimator.
  • Beyond the paper: the two-cause account suggests that benchmarks should report omission and fabrication rates separately at the item level, since aggregate accuracy can hide the exact tradeoff the paper identifies.
  • Beyond the paper: the same potential-field lens might extend to attribute-level hallucinations (color, position, count), which the MME results hint at but the paper does not analyze mechanistically.
  • Beyond the paper: the hidden-state steering direction computed by VPFC could be repurposed as a probe for object-presence confidence, enabling confidence-aware decoding that does not rely on centroid geometry.
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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

5 major / 5 minor

Summary. The paper argues that omission and fabrication hallucinations in MLLMs have distinct causes: omissions arise from insufficient confidence when mapping already-captured visual features to language, while fabrications arise from spurious cross-modal associations driven by training-set statistical biases. It introduces the Visual-Semantic Attention Potential Field (VSAPF) as a conceptual account of how visual evidence is weighted, and proposes Visual Potential Field Calibration (VPFC), a training-free, plug-and-play method that enhances attention in a centroid-centered region of HCVR tokens to reduce omissions without increasing fabrications. Experiments on POPE, CHAIR, MME, and LLaVA-Bench report consistent improvements over VCD, SID, and MemVR.

Significance. If the mechanistic claims hold, the paper would be a valuable correction to the common assumption that all object hallucinations share one cause, and VPFC is a simple, effective training-free intervention. The benchmark results are promising: VPFC improves over strong baselines on multiple datasets and two backbones. However, the paper's central causal story and the mechanism of VPFC rest on qualitative, single-image evidence and on an unquantified dispersion heuristic. The contribution is therefore conditionally significant: the method may work, but the paper as written does not yet establish the proposed explanation, and the ablation evidence is too thin to separate the mechanism from generic smoothing effects.

major comments (5)
  1. [Section 4, 'Focused Region for Visual Potential Calibration'] The central premise of VPFC is the 'consistent pattern' that HCVRs are spatially concentrated when the queried object is present and dispersed when it is absent. This premise determines whether the centroid square enhances HCVRs or partially overlaps LCVRs, and it is never quantitatively validated. No dispersion metric, no distributions for present vs. absent objects, no head/layer aggregation details, and no statistics are provided. The paper must supply quantitative support across a representative sample of images and queries, including failure cases (e.g., small objects, multiple objects, salient distractors). Without this, the mechanism collapses even if the benchmark numbers improve.
  2. [Section 3.3, Figure 4] The intervention results that justify the HCVR/LCVR confidence account are described only qualitatively ('enhancing attention to the HCVRs leads the model to increasingly judge that the queried object is present'). No numbers, sample sizes, or consistency rates are reported. This is foundational evidence for the VSAPF concept and for the design of VPFC. The authors should report the distribution of model answers under HCVR enhancement, LCVR enhancement, and baseline, separately for present and absent objects, and ideally for multiple models and layers.
  3. [Section 3.4, Equations (2)-(3)] The claim that SID's contrastive decoding is 'essentially equivalent' to enhancing attention to HCVRs is not established. The two equations have similar algebraic forms, but they involve different quantities: SID contrasts the original logits with logits from low-importance visual tokens vlow, while the proposed enhancement contrasts original logits with logits from high-importance visual tokens vhigh. No derivation shows that these become equivalent for any α and β, and the 'appropriately set' condition is not specified. The paper needs either a precise mathematical equivalence or an empirical comparison showing that the two decoding distributions are interchangeable in effect.
  4. [Section 5.2, Tables 1-4] The abstract and Section 5.2 repeatedly claim that VPFC 'reduces omission hallucinations without introducing additional fabrication hallucinations.' However, none of the reported metrics (accuracy, F1, CHAIR, MME scores) decompose errors into omission vs. fabrication. POPE, for instance, is a binary yes/no task; the paper should report the false-'no' rate (omissions) and false-'yes' rate (fabrications) separately for each method. Figure 1 provides such counts for VCD but the corresponding decomposition for VPFC and the other baselines is missing. Without this, the central claim is not directly evidenced.
  5. [Section 5.3, Figure 7] The ablation 'VPFC w/o Centroid' is presented only as a bar chart without numeric values, error bars, or statistical tests. More importantly, it does not isolate the dispersion heuristic: replacing the centroid square with the raw top-25% token set changes both the spatial support and the smoothing of the intervention, confounding the hypothesis. A proper ablation should vary the dispersion criterion directly (e.g., using a dispersion threshold to choose between enhancing HCVRs vs. LCVRs) and report outcomes for images where the heuristic succeeds and fails.
minor comments (5)
  1. [Appendix D] The limitation section honestly states that VPFC does not explicitly suppress fabrications. This should be reflected in the abstract and conclusion, where the phrase 'reduces omission hallucinations without introducing additional fabrication hallucinations' is currently presented as a complete solution rather than as a balanced trade-off.
  2. [Section 5.1 / Tables] Several tables have inconsistent formatting: Table 1 repeats the 'Random Popular Adversarial' header in the text, and Table 4 lacks column alignment. Please standardize all tables and include error bars or confidence intervals where feasible.
  3. [Figures 9 and 10] The ablation figures in Appendix C have no captions and no axis labels in the main text. Additionally, the ablation results are only reported on COCO-Random; report results on Popular and Adversarial subsets as well.
  4. [References / Section 5.2] The claim that 'fabrications significantly outnumber omissions' in the Adversarial subset is supported only by the authors' own prior work (Yin et al. 2025). Provide an independent analysis or additional citation to make this load-bearing statement stronger.
  5. [Throughout] Minor typographical issues: 'LLaV A-v1.5' is inconsistently spaced, 'Preprint,Underreview' appears in the header, and 'Kening Zheng' and 'Ken ing Zheng' are inconsistent in the references. Please proofread the manuscript.

Circularity Check

0 steps flagged

No significant circularity: the two-cause proposal and VPFC are empirically constructed, with only a minor non-load-bearing self-citation.

full rationale

The paper's derived claims do not reduce to their inputs. The two-cause hypothesis (Secs. 3.1–3.2) is supported by attention-map inspection (using the external localization-head method from Kang et al.) and by targeted intervention experiments (Sec. 3.3); the VPFC design (Sec. 4) follows from those interventions plus a stated dispersion heuristic. The dispersion heuristic is under-evidenced, but it is an empirical premise, not a renaming or fitting of the reported outcomes. The only self-citation is Yin et al. 2025, used in Sec. 5.2 to assert that fabrications outnumber omissions on the POPE Adversarial subset; this supports an interpretation of why VPFC helps there, but it does not support the central causal derivation or the construction of VPFC, so it is minor. Separate methodological concern: hyperparameters α and γ were selected via POPE ablation and then evaluated on POPE, which weakens the 'state-of-the-art on POPE' claim via test-set tuning, but this is not equation-level circularity. Therefore score 2.

Axiom & Free-Parameter Ledger

4 free parameters · 4 axioms · 1 invented entities

The central method depends on several hand-set thresholds and hyperparameters, none of which are independently justified; the key spatial dispersion heuristic is asserted, not demonstrated. The VSAPF is an explanatory metaphor rather than a measurable entity.

free parameters (4)
  • steering coefficient alpha = 4
    Tuned on POPE COCO-Random; ablation shows 3 < alpha < 6 stable (Appendix C, Figure 10).
  • attention head selection ratio gamma = 25%
    Tuned on POPE COCO-Random; ablation shows gamma < 50% preferred (Appendix C, Figure 9).
  • HCVR threshold = top 25% of visual tokens
    Hand set as the subset that 'generally captures the majority of the target object' (Section 4); no sensitivity reported.
  • mild enhancement factor for steering direction = 0.05
    Hand-picked 'mild enhancement' used to compute delta in Equation 4; no ablation.
axioms (4)
  • domain assumption Attention maps of localization heads (Kang et al. 2025) faithfully reflect which visual features the model has encoded.
    Used in Section 3.1 to conclude the spoon's features were captured despite the omission.
  • domain assumption Amplifying attention to HCVRs and LCVRs reveals the model's inherent confidence assignment.
    Section 3.3; the mapping from attention manipulation to 'confidence' is an interpretation.
  • ad hoc to paper When an object is present, HCVRs are spatially concentrated; when absent, dispersed.
    Load-bearing heuristic for VPFC's centroid design in Section 4; stated without quantitative evidence.
  • ad hoc to paper SID's contrastive decoding is effectively equivalent to enhancing HCVR attention.
    Argued in Section 3.4 via Equations 2 and 3, but the two equations are not algebraically equivalent.
invented entities (1)
  • Visual-Semantic Attention Potential Field (VSAPF) no independent evidence
    purpose: Conceptual analogy to explain how visual tokens support or oppose object presence judgments.
    Illustrative framework (Figure 5); no measurable predictions.

reviewed 2026-08-05 · how reviews work

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Cite this review

Pith. "Pith review of Two Causes, Not One: Rethinking Omission and Fabrication Hallucinations in MLLMs." pith.science (2026). https://pith.science/paper/G67ARZZC

@misc{pith2026250900371,
  author       = {Pith},
  title        = {Pith review of: Two Causes, Not One: Rethinking Omission and Fabrication Hallucinations in MLLMs},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/G67ARZZC}},
  note         = {Machine review of arXiv:2509.00371}
}
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read the original abstract

Multimodal Large Language Models (MLLMs) have achieved impressive advances, yet object hallucination remains a persistent challenge. Existing methods, based on the flawed assumption that omission and fabrication hallucinations share a common cause, often reduce omissions only to trigger more fabrications. In this work, we overturn this view by demonstrating that omission hallucinations arise from insufficient confidence when mapping perceived visual features to linguistic expressions, whereas fabrication hallucinations result from spurious associations within the cross-modal representation space due to statistical biases in the training corpus. Building on findings from visual attention intervention experiments, we propose the Visual-Semantic Attention Potential Field, a conceptual framework that reveals how the model constructs visual evidence to infer the presence or absence of objects. Leveraging this insight, we introduce Visual Potential Field Calibration (VPFC), a plug-and-play hallucination mitigation method that effectively reduces omission hallucinations without introducing additional fabrication hallucinations. Our findings reveal a critical oversight in current object hallucination research and chart new directions for developing more robust and balanced hallucination mitigation strategies.

Figures

Figures reproduced from arXiv: 2509.00371 by Guangzong Si, Hao Yin, Pai Peng, Qing Ding, Tao He, Wenlong Liao, Xianfei Li.

Figure 1
Figure 1. Figure 1: Effects of Visual Contrastive Decoding on the Miti [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: The Cause Behind Omission Hallucinations. [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: The Cause Behind fabrication Hallucinations. [PITH_FULL_IMAGE:figures/full_fig_p003_3.png] view at source ↗
Figure 5
Figure 5. Figure 5: Illustration of the Visual Potential Field. [PITH_FULL_IMAGE:figures/full_fig_p004_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Illustration of Visual Potential Calibration. [PITH_FULL_IMAGE:figures/full_fig_p005_6.png] view at source ↗
Figure 8
Figure 8. Figure 8: Case Study on Object Hallucination Mitigation on [PITH_FULL_IMAGE:figures/full_fig_p007_8.png] view at source ↗
Figure 9
Figure 9. Figure 9: Ablation Study on Head Selection Ratio. C Additional Ablation Studies We performed an ablation study on the attention head selec￾tion ratio, using LLaVA-v1.5-7B as the MLLM backbone on the COCO-Random dataset from the POPE benchmark. The objective was to evaluate how different selection ratios impact prediction performance. As illustrated in [PITH_FULL_IMAGE:figures/full_fig_p009_9.png] view at source ↗
Figure 10
Figure 10. Figure 10: Ablation Study on Steering Coefficient. We conducted an ablation study on the steering coefficient, [PITH_FULL_IMAGE:figures/full_fig_p009_10.png] view at source ↗

discussion (0)

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Reference graph

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This paper was first reviewed by deepseek-v4-flash on August 5, 2026.