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arxiv: 2601.05563 · v3 · submitted 2026-01-09 · 💻 cs.CV · cs.SI

Recognition: 1 theorem link

· Lean Theorem

What's Left Unsaid? Detecting and Correcting Misleading Omissions in Multimodal News Previews

Fanxiao Li , Jiaying Wu , Tingchao Fu , Dayang Li , Herun Wan , Wei Zhou , Min-Yen Kan
Authors on Pith no claims yet

Pith reviewed 2026-05-16 16:23 UTC · model grok-4.3

classification 💻 cs.CV cs.SI
keywords misleading omissionsmultimodal news previewsOMGuardMM-Misleading benchmarkLVLM detectionheadline correctioninterpretation drift
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The pith

OMGuard lets an 8B vision-language model detect misleading omissions in news previews at the accuracy of a 235B model while improving corrections.

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

News previews pairing images with headlines can lead readers to form incorrect judgments by omitting key context, even when every stated fact is accurate. The paper builds the MM-Misleading benchmark through a pipeline that contrasts what a reader infers from the preview alone with the fuller picture in the article. Current open-source large vision-language models show clear weaknesses at spotting these subtle omissions. OMGuard adds interpretation-aware fine-tuning to improve detection and uses explicit rationales to guide headline rewriting for correction. The result is that an 8B model reaches the detection level of a 235B model and produces stronger overall fixes.

Core claim

By constructing the MM-Misleading benchmark via a multi-stage pipeline that simulates preview-based versus context-based understanding, the work shows that open-source LVLMs have pronounced blind spots for omission-based misleadingness. OMGuard addresses this through Interpretation-Aware Fine-Tuning for detection and Rationale-Guided Misleading Content Correction that uses explicit rationales to rewrite headlines, allowing an 8B model to match a 235B LVLM on detection accuracy while delivering stronger end-to-end correction.

What carries the argument

OMGuard, which pairs Interpretation-Aware Fine-Tuning for misleadingness detection with Rationale-Guided Misleading Content Correction that rewrites headlines using explicit rationales derived from full context.

If this is right

  • Most misleading omissions arise from local narrative shifts such as missing background details rather than global frame changes.
  • Text-only headline correction fails in image-driven cases, requiring visual interventions.
  • Targeted fine-tuning allows smaller 8B models to reach detection performance comparable to much larger 235B models.
  • Open-source LVLMs exhibit systematic blind spots when detecting omission-based misleadingness in multimodal previews.

Where Pith is reading between the lines

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

  • The same pipeline could be applied to video or audio news previews to test whether local omissions remain the dominant pattern.
  • Integrating visual correction steps into OMGuard would likely improve results on the image-driven cases identified in the analysis.
  • Deploying OMGuard on live social-media feeds could reduce interpretation drift before readers form judgments from previews alone.

Load-bearing premise

The multi-stage pipeline that contrasts preview understanding with full-article understanding produces a benchmark that matches real-world misleading omissions in multimodal news.

What would settle it

A human study in which participants read actual news previews and full articles, rate misleadingness, and show interpretations that diverge substantially from the MM-Misleading labels would falsify the benchmark's validity.

Figures

Figures reproduced from arXiv: 2601.05563 by Dayang Li, Fanxiao Li, Herun Wan, Jiaying Wu, Min-Yen Kan, Tingchao Fu, Wei Zhou.

Figure 1
Figure 1. Figure 1: Illustration of misleading omissions in mul￾timodal news previews. Social media users typically see only a news preview (image–headline pair), while the full context becomes available only after clicking through. When key information is omitted or selectively presented, the preview can induce misinterpretations that diverge from those supported by the full article. is intensified on social media, where use… view at source ↗
Figure 2
Figure 2. Figure 2: Overview of OMGUARD: the upper section shows the multi-stage annotation pipeline described in § 4.2; the lower section presents OMGUARD, where the model is first fine-tuned with interpretation-aware supervision using misleadingness rationales, and then applied to rationale-guided correction of misleading previews. Stage 3: Misleading Omission Judgment. We detect misleadingness by comparing the semantic div… view at source ↗
Figure 3
Figure 3. Figure 3: Quantifying error propagation via oracle sub [PITH_FULL_IMAGE:figures/full_fig_p006_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Frame-shift analysis comparing stylistic [PITH_FULL_IMAGE:figures/full_fig_p008_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Statistic of different misleading types. [PITH_FULL_IMAGE:figures/full_fig_p015_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Human annotation guidelines for misleading content detection and an analysis of annotation disagreements. [PITH_FULL_IMAGE:figures/full_fig_p017_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: Human annotation guidelines for misleading content correction and an analysis of annotation disagree [PITH_FULL_IMAGE:figures/full_fig_p017_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: A case study of misleading content detection across different models. [PITH_FULL_IMAGE:figures/full_fig_p018_8.png] view at source ↗
Figure 9
Figure 9. Figure 9: Examples of correction via visual image replacement. We show that replacing the original image with a [PITH_FULL_IMAGE:figures/full_fig_p019_9.png] view at source ↗
Figure 10
Figure 10. Figure 10: Prompt for selecting high-quality news instances. [PITH_FULL_IMAGE:figures/full_fig_p019_10.png] view at source ↗
Figure 11
Figure 11. Figure 11: Prompt for LLM-based preview understanding simulation. [PITH_FULL_IMAGE:figures/full_fig_p020_11.png] view at source ↗
Figure 12
Figure 12. Figure 12: Prompt for LLM-based news context understanding simulation. [PITH_FULL_IMAGE:figures/full_fig_p020_12.png] view at source ↗
Figure 13
Figure 13. Figure 13: Prompt for misleading omission judgment. [PITH_FULL_IMAGE:figures/full_fig_p021_13.png] view at source ↗
Figure 14
Figure 14. Figure 14: Prompt for misleading headline correction. [PITH_FULL_IMAGE:figures/full_fig_p021_14.png] view at source ↗
Figure 15
Figure 15. Figure 15: Prompt for frame analysis. Fine-grained Misleading Attribution # Task You are a misleading attribution classifier, designed to evaluate the reasons why an image–headline pair may be misleading compared to the full news context. Your task is to determine which category of misleading type the given reason belongs to. # Input - Image: You will be provided. - News Headline: {NEWS_HEADLINE} - Full NEWS Context… view at source ↗
Figure 16
Figure 16. Figure 16: Prompt for fine-grained misleading attribution. [PITH_FULL_IMAGE:figures/full_fig_p022_16.png] view at source ↗
Figure 17
Figure 17. Figure 17: Prompt for modality attribution. Visual Prototyping You will receive a news preview (including an image and a headline) and the corresponding news context. It is known that this news preview is misleading compared to the news context. We have rewritten the headline based on the identified original misleading rationale. However, the rewritten headline is still misleading. We believe this is mainly because … view at source ↗
Figure 18
Figure 18. Figure 18: Prompt for visual prototyping [PITH_FULL_IMAGE:figures/full_fig_p023_18.png] view at source ↗
read the original abstract

Even when factually correct, social-media news previews (image-headline pairs) can induce interpretation drift: by selectively omitting crucial context, they lead readers to form judgments that diverge from what the full article supports. This covert harm is subtler than explicit misinformation, yet remains underexplored. To address this gap, we develop a multi-stage pipeline that simulates preview-based and context-based understanding, enabling construction of the MM-Misleading benchmark. Using MM-Misleading, we systematically evaluate open-source LVLMs and uncover pronounced blind spots in omission-based misleadingness detection. We further propose OMGuard, which combines (1) Interpretation-Aware Fine-Tuning for misleadingness detection and (2) Rationale-Guided Misleading Content Correction, where explicit rationales guide headline rewriting to reduce misleading impressions. Experiments show that OMGuard lifts an 8B model's detection accuracy to the level of a 235B LVLM while delivering markedly stronger end-to-end correction. Further analysis shows that misleadingness usually arises from local narrative shifts, such as missing background, instead of global frame changes, and identifies image-driven cases where text-only correction fails, underscoring the need for visual interventions.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

1 major / 2 minor

Summary. The paper addresses misleading omissions in multimodal news previews (image-headline pairs) that induce interpretation drift without factual errors. It introduces a multi-stage simulation pipeline to construct the MM-Misleading benchmark by generating preview-based interpretations and context-based corrections. Using this benchmark, it evaluates open-source LVLMs, identifies detection blind spots, and proposes OMGuard combining Interpretation-Aware Fine-Tuning for detection with Rationale-Guided Misleading Content Correction for headline rewriting. Experiments claim that OMGuard raises an 8B model's detection accuracy to match a 235B LVLM while improving end-to-end correction, with analysis showing omissions typically stem from local narrative shifts rather than global frame changes.

Significance. If the simulation pipeline produces omissions that faithfully match real-world reader misjudgments, the work fills an important gap in detecting subtle multimodal misinformation on social media. The dual focus on detection and rationale-guided correction, plus the identification of image-driven cases requiring visual interventions, offers practical value for content moderation tools. The release of a new benchmark and the performance lift on an 8B model are notable strengths if benchmark validity holds.

major comments (1)
  1. [§3] §3 (Benchmark Construction): The MM-Misleading benchmark is built exclusively via the multi-stage simulation pipeline with no reported human validation study, inter-annotator agreement, or comparison to a held-out set of real misleading previews. Since all detection and correction results (including the 8B-to-235B parity claim) are measured only on this synthetic data, the absence of external validation is load-bearing for the central claims.
minor comments (2)
  1. [Abstract] Abstract: Lacks concrete details on benchmark size, exact evaluation metrics (e.g., accuracy definition), data sources for the full articles, and any controls for simulation artifacts, which hinders immediate assessment of the experimental claims.
  2. [§5] §5 (Analysis): The claim that 'misleadingness usually arises from local narrative shifts' would benefit from quantitative breakdown (e.g., percentage of cases by shift type) and explicit comparison to global frame changes to strengthen the interpretive conclusion.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for the constructive feedback and for emphasizing the need for external validation of the MM-Misleading benchmark. We address the concern point by point below and outline the revisions we will make.

read point-by-point responses

  1. Referee: [§3] §3 (Benchmark Construction): The MM-Misleading benchmark is built exclusively via the multi-stage simulation pipeline with no reported human validation study, inter-annotator agreement, or comparison to a held-out set of real misleading previews. Since all detection and correction results (including the 8B-to-235B parity claim) are measured only on this synthetic data, the absence of external validation is load-bearing for the central claims.

    Authors: We agree that the absence of human validation is a substantive limitation, as the benchmark relies entirely on the simulation pipeline. The pipeline is constructed to mirror real reader processes by generating preview-based interpretations (simulating omission-induced drift) and then context-based corrections using the full article, with explicit steps to ensure the omissions are local narrative shifts rather than global frame changes. This design draws on prior LLM-based synthetic data generation methods for misinformation tasks. Nevertheless, we acknowledge that direct comparison to human judgments is necessary to confirm fidelity. In the revised manuscript we will add a human validation study on a held-out subset of 200 samples. Independent annotators will rate (1) the presence and severity of misleading omissions in the preview and (2) the quality of the pipeline-generated corrections, with inter-annotator agreement reported via Cohen’s kappa. We will also compare pipeline labels against these human annotations and include the results in §3 and the experiments section to support the reported performance claims, including the 8B-to-235B parity. A dedicated limitations paragraph will further discuss the synthetic construction and the assumptions underlying the simulation. revision: yes

Circularity Check

0 steps flagged

No significant circularity detected

full rationale

The paper's derivation chain constructs the MM-Misleading benchmark through an explicit multi-stage simulation pipeline (preview-based interpretation followed by context-based correction labeling) and then applies standard fine-tuning (Interpretation-Aware Fine-Tuning) plus rationale-guided rewriting to existing LVLMs. Performance metrics are reported on this benchmark, but the evaluation uses conventional train/test splits and does not reduce any claimed prediction or result to its own inputs by construction. No equations equate a fitted parameter to a downstream prediction, no load-bearing self-citations justify uniqueness, and no ansatz is smuggled via prior work. The approach remains self-contained empirical work on a newly created dataset rather than a tautological renaming or re-derivation of its own supervision signals.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

This is an applied empirical machine-learning paper whose claims rest on the validity of the simulated benchmark and the effectiveness of fine-tuning rather than on explicit mathematical axioms or new postulated entities.

pith-pipeline@v0.9.0 · 5533 in / 1064 out tokens · 47472 ms · 2026-05-16T16:23:27.890959+00:00 · methodology

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Reference graph

Works this paper leans on

14 extracted references · 14 canonical work pages · 3 internal anchors

  1. [1]

    Arnav Arora, Srishti Yadav, Maria Antoniak, Serge Belongie, and Isabelle Augenstein

    Quantifying the impact of misinformation and vaccine-skeptical content on facebook.Science, 384(6699):eadk3451. Arnav Arora, Srishti Yadav, Maria Antoniak, Serge Belongie, and Isabelle Augenstein. 2025. Multi- modal framing analysis of news.arXiv preprint arXiv:2503.20960. Alimohammad Beigi, Bohan Jiang, Dawei Li, Zhen Tan, Pouya Shaeri, Tharindu Kumarage...

    work page arXiv 2025

  2. [2]

    LLMs-as-Judges: A Comprehensive Survey on LLM-based Evaluation Methods

    Llms-as-judges: a comprehensive survey on llm-based evaluation methods.arXiv preprint arXiv:2412.05579. Fuxiao Liu, Yinghan Wang, Tianlu Wang, and Vicente Ordonez. 2021. Visual news: Benchmark and chal- lenges in news image captioning. InProceedings of the 2021 conference on empirical methods in natural language processing, pages 6761–6771. Haotian Liu, C...

    work page internal anchor Pith review Pith/arXiv arXiv 2021

  3. [3]

    Peng Qi, Zehong Yan, Wynne Hsu, and Mong Li Lee

    Judge anything: Mllm as a judge across any modality.arXiv preprint arXiv:2503.17489. Peng Qi, Zehong Yan, Wynne Hsu, and Mong Li Lee

    work page arXiv

  4. [4]

    Qwen3 Technical Report

    Sniffer: Multimodal large language model for explainable out-of-context misinformation detec- tion. InProceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 13052–13062. Qwen Team. 2025. Qwen3 technical report.Preprint, arXiv:2505.09388. Patrick R Rich and Maria S Zaragoza. 2016. The con- tinued influence of implied and e...

    work page internal anchor Pith review Pith/arXiv arXiv 2025

  5. [5]

    Stop Overthinking: A Survey on Efficient Reasoning for Large Language Models

    Stop overthinking: A survey on efficient rea- soning for large language models.arXiv preprint arXiv:2503.16419. S Shyam Sundar, Eugene Cho Snyder, Mengqi Liao, Junjun Yin, Jinping Wang, and Guangqing Chi. 2025. Sharing without clicking on news in social media. Nature Human Behaviour, 9(1):156–168. Yixuan Tang, Jincheng Wang, and Anthony Kum Hoe Tung. 2025...

    work page internal anchor Pith review Pith/arXiv arXiv 2025

  6. [6]

    Economic

    annotate the same instances independently and apply agreement filtering. The two models pro- duce consistent labels for over 84% of the samples. Because our goal is to identify omission-based mis- leading content, we retain only high-confidence instances where both models agree, prioritizing label precision over coverage of borderline cases. A.2.5 Details...

    work page 2025

  7. [7]

    Analyze the Misleading Cause - Based on the provided data, identify the main reasons why the original headline is misleading, including any factual, contextual, or expressive distortions

    work page

  8. [8]

    Suggestions on Improvement - Consider what kinds of information or phrasing should be included in the headline to prevent misleading readers and accurately convey the core message of the news

    work page

  9. [9]

    Misleading_Cause

    Generate the Headline - Based on the above analysis, produce a non-misleading headline that is factually accurate, semantically clear, and maintains a neutral tone. # Rewriting requirements: [This can be replaced according to different rewritten types] Minimal-Edit:- The rewritten news headline may contain at most {limit_words} additional words compared t...

    work page

  10. [10]

    Because this context is missing, readers are likely to form an incomplete or distorted overall impression

    Missing background and conditions: - The reason mainly points out that the image–headline pair omits essential background or conditions needed to correctly understand the event (for example, prior context, policy constraints, key actors, follow-up developments, or outcomes). Because this context is missing, readers are likely to form an incomplete or dist...

    work page

  11. [11]

    It only shows isolated or local cases, or uses extreme examples in a way that underplays or exaggerates the true scale, prevalence, or impact described in the full news context

    Misleading scale and representativeness: - The reason mainly emphasizes that the image–headline pair misleads about how large, frequent, or systemic the event is. It only shows isolated or local cases, or uses extreme examples in a way that underplays or exaggerates the true scale, prevalence, or impact described in the full news context

    work page

  12. [12]

    Omission of perspectives and controversy: - The reason mainly highlights that the image–headline pair hides important viewpoints or controversy. It presents only one side (for example, an official or dominant narrative) while omitting affected groups, opposition voices, counter-arguments, or social conflict that are present in the full news context, leadi...

    work page

  13. [13]

    Misleading causality and temporality: - The reason mainly concerns incorrect or misleading suggestions about cause–effect relations, event sequence, or current status. The image–headline pair may imply that one action directly caused an outcome, that an event is still ongoing, or that a past event is current, in ways that are not supported by the full new...

    work page

  14. [14]

    attribution_class

    Others: - Use this category if the reason does not clearly fall into any of the four types above, or if you are not confident which category is most appropriate. # Output Return the output in standard JSON format with the following fields: { “attribution_class”: “Only the most possible class”, “attribution_reason”: “Explain in detail why it belongs to thi...

    work page