Decoupling Consistency and Conflict across MLLM Layers for Fake News Detection

Kai Qian; Dongjie Xu; Weijie Shi; Jiaqi Wang; Haolun Ma; Jia Zhu; Jiajie Xu

cs.CVOct 27, 2026

Decoupling Consistency and Conflict across MLLM Layers for Fake News Detection

Authors: Kai Qian, Dongjie Xu, Weijie Shi, Jiaqi Wang, Haolun Ma, Jia Zhu, Jiajie Xu

Organizations: School of Computer Science and Technology, Soochow University, China · Soochow University, China · Hong Kong University of Science and Technology, China · Computer Science and Engineering, The Chinese University of Hong Kong, China ·

Abstract

The trustworthiness of Web content faces a growing threat from multimodal fake news. Real news usually shows aligned image-text semantics, while fake news often contains a few conflicts at varying levels, such as mismatches in objects, scenes, events, and claims. However, existing methods usually treat consistency and conflict in a coarse and static way, limiting their ability to capture sparse conflicts and adapt to varying conflict levels. Inspired by the distinct knowledge encoded across layers in large language models, we propose Multi-Level Consistency and Conflict Decoupling (MLCCD), which captures conflicts at different levels from different layers of multimodal large language models (MLLMs). Specifically, we first use the MLLM's final decision representation to select visual and textual tokens relevant to the task, preventing irrelevant content from diluting sparse conflicts. Then, we preserve tokens whose representations change sharply between adjacent layers, capturing the knowledge each layer adds. Finally, we decouple this knowledge into consistency and conflict evidence and aggregate each across layers with its own router, adapting to varying conflict levels. Experiments on three benchmarks show that MLCCD outperforms existing methods.

Figures & tables

Figure 1. Illustration of two challenges in multimodal fake news detection. (a) Sparse conflicts can be diluted when aggregated with other content. (b) The importance of consistency and conflict evidence can vary across samples and semantic levels.

Figure 2. Overview of MLCCD. The frozen LoRA tuned MLLM provides layer wise hidden states, and the proposed evidence network selects task relevant tokens, extracts discriminative layer knowledge, and adaptively routes consistency and conflict evidence for fake news prediction.

Dataset	Train	Dev	Test	Lang.
Weibo	7461	–	1929	Chinese
Pheme	1412	221	385	English
AMG	3268	501	883	English

Table 1. Dataset statistics.

Variant	Acc.	F1-Ma	F1-R	F1-F
w/o TRTS	92.21	90.45	94.55	86.36
w/o DLKE	92.21	90.70	94.44	86.96
w/o ACCR	92.47	90.70	94.76	86.64
Shared Router	91.69	89.46	94.31	84.62
w/o Dis.	92.73	91.09	94.91	87.27
MLCCD	93.25	91.73	95.27	88.18

Table 2. Ablation results on Pheme. All values are percentages. Best results are in bold. F1-R and F1-F denote real class F1 and fake class F1.

Category	Method	AMG				PHEME				Weibo
Category	Method	Acc.	F1-Ma	F1-R	F1-F	Acc.	F1-Ma	F1-R	F1-F	Acc.	F1-Ma	F1-R	F1-F
General fake news detection methods	CFFN ( Li et al., 2023a )	82.90	82.39	85.38	79.40	82.86	79.22	87.91	70.54	89.22	89.21	89.52	88.90
	MTS ( Sun et al., 2025 )	85.05	84.51	87.40	81.62	89.61	87.72	92.54	82.91	89.58	89.54	90.15	88.94
	MIMoE-FND ( Liu et al., 2025 )	84.03	83.64	86.16	81.12	88.57	86.62	91.73	81.51	89.89	89.88	90.19	89.58
	DDU ( Chen et al., 2026 )	85.39	84.91	87.61	82.21	86.49	83.88	90.37	77.39	90.93	90.93	91.02	90.83
General multimodal methods	DMD ( Li et al., 2023b )	84.60	84.25	86.59	81.91	88.57	85.24	92.25	78.22	89.42	89.41	89.75	89.08

Table 3. Overall results on AMG, PHEME, and Weibo with seed 42. All values are percentages. Acc. and F1-Ma denote accuracy and macro F1, respectively. F1-R and F1-F denote real class F1 and fake class F1, respectively.

Figure 3. Hyperparameter sensitivity of MLCCD. (a) Text keep ratio

\rho_{q}^{t}

on Weibo, AMG, and Pheme, shown on a logarithmic horizontal axis. (b) Image keep ratio

\rho_{q}^{v}

, (c) token keep ratio

\rho_{e}

in DLKE, and (d) number of routed layers on Pheme. Outer circles indicate the default settings. Panels (b)–(d) share the same vertical scale. Each point represents one reported experimental result, and lines connect the evaluated settings.

Sample type	N	Qwen3-VL-8B + LoRA		MLCCD		$\Delta$ (pp)
Sample type	N	Correct	Rate (%)	Correct	Rate (%)	$\Delta$ (pp)
Counterfactual samples: detection as fake
Object conflict	100	23	23.00	50	50.00	+27.00
Scene conflict	100	23	23.00	52	52.00	+29.00
Event conflict	100	14	14.00	34	34.00	+20.00
Claim conflict	100	14	14.00	33	33.00	+19.00

Table 4. Results on the controlled counterfactual evaluation set. N denotes the number of samples, and Correct denotes the number assigned the intended label. Rates are percentages: counterfactual detection rates in the upper block and correct acceptance rates for real samples in the lower block.

\Delta

denotes the rate of MLCCD minus that of the LoRA baseline in percentage points.

Edit ratio	N	LoRA	MLCCD
$r_{\mathrm{edit}}\leq 5\%$	151	23 (15.23)	54 (35.76)
$5\%<r_{\mathrm{edit}}\leq 10\%$	153	29 (18.95)	65 (42.48)
$10\%<r_{\mathrm{edit}}\leq 20\%$	96	22 (22.92)	50 (52.08)
All samples	400	74 (18.50)	169 (42.25)

Table 5. Counterfactual detection results grouped by edit ratio. N denotes the number of samples. Each method entry reports the number of detected counterfactuals, followed by the detection rate (%) in parentheses. LoRA denotes Qwen3-VL-8B + LoRA.

Image replacement	N	Original	Replaced
Uniform gray	400	4.84	0.96
Donor image	256	4.70	1.02

Table 6. Effect of image replacement on MLCCD. N denotes the number of counterfactual and control pairs. Original and Replaced report the mean paired score difference under the respective image conditions, evaluated on the same pairs in each row.

Method	Total training time (s)	Training overhead	Inference time (s)
Qwen3-VL-8B + LoRA	845	–	$\approx 45$
MLCCD	1102	+257 s (+30.4%)	$\approx 48$

Table 7. Runtime comparison on Pheme. MLCCD training time includes both LoRA adaptation and evidence network training. Inference time covers all 385 test samples. Timings are derived from execution logs and include data handling and logging overhead.

Appendix figures & tables5 assets

Supplementary material from the paper’s appendix.

Appendix

$\rho_{q}^{t}$	Weibo				AMG				Pheme
$\rho_{q}^{t}$	Acc.	F1-Ma	F1-R	F1-F	Acc.	F1-Ma	F1-R	F1-F	Acc.	F1-Ma	F1-R	F1-F
0.0125	95.28	95.28	95.39	95.17	88.45	88.26	89.74	86.79	91.43	89.29	94.08	84.51
0.025	95.28	95.28	95.36	95.20	90.37	90.07	91.80	88.34	91.69	89.76	94.20	85.32
0.05	95.33	95.33	95.45	95.22	89.47	89.17	90.96	87.38	92.21	90.24	94.62	85.85
0.1	95.23	95.23	95.29	95.17	88.79	88.57	90.15	86.99	93.25	91.73	95.27	88.18
0.3	95.28	95.28	95.34	95.22	88.67	88.45	90.06	86.84	92.73	91.04	94.93	87.16

Appendix

Table 8. Sensitivity to the text keep ratio

\rho_{q}^{t}

. All performance scores are percentages. The default ratios are

0.3

0.025

, and

0.1

for Weibo, AMG, and Pheme, respectively. F1-R and F1-F denote real class F1 and fake class F1.

Parameter	Value	Acc.	F1-Ma	F1-R	F1-F
$\rho_{q}^{v}$	0.1	92.73	91.04	94.93	87.16
	$0.3^{\dagger}$	93.25	91.73	95.27	88.18
	0.5	91.69	89.52	94.29	84.76
$\rho_{e}$	0.3	91.69	89.46	94.31	84.62
	$0.5^{\dagger}$	93.25	91.73	95.27	88.18
	0.7	92.47	90.75	94.74	86.76

Appendix

Table 9. Sensitivity to the image keep ratio

\rho_{q}^{v}

, the token keep ratio

\rho_{e}

in DLKE, and the number of routed layers on Pheme. All performance scores are percentages.

\dagger

marks the default setting in each group. F1-R and F1-F denote real class F1 and fake class F1.

Method	Acc.	Fake News			Real News
Method	Acc.	Prec.	Rec.	F1	Prec.	Rec.	F1
CFFN	82.90	74.42	85.09	79.40	89.63	81.52	85.38
MTS	85.05	77.93	85.67	81.62	90.34	84.66	87.40
MIMoE-FND	84.03	74.81	88.60	81.12	91.84	81.15	86.16
DDU	85.39	77.81	87.13	82.21	91.20	84.29	87.61
DMD	84.60	75.12	90.06	81.91	92.81	81.15	86.59

Appendix

Table 10. Detailed classification results on AMG.

Method	Acc.	Fake News			Real News
Method	Acc.	Prec.	Rec.	F1	Prec.	Rec.	F1
CFFN	82.86	71.17	69.91	70.54	87.59	88.24	87.91
MTS	89.61	80.17	85.84	82.91	93.94	91.18	92.54
MIMoE-FND	88.57	77.60	85.84	81.51	93.85	89.71	91.73
DDU	86.49	76.07	78.76	77.39	91.04	89.71	90.37
DMD	88.57	88.76	69.91	78.22	88.51	96.32	92.25

Appendix

Table 11. Detailed classification results on Pheme.

Method	Acc.	Fake News			Real News
Method	Acc.	Prec.	Rec.	F1	Prec.	Rec.	F1
CFFN	89.22	88.52	89.28	88.90	89.88	89.16	89.52
MTS	89.58	91.40	86.60	88.94	88.04	92.37	90.15
MIMoE-FND	89.89	89.34	89.82	89.58	90.41	89.96	90.19
DDU	90.93	88.83	92.93	90.83	93.07	89.06	91.02
DMD	89.42	88.98	89.17	89.08	89.84	89.66	89.75

Appendix

Table 12. Detailed classification results on Weibo.

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Multimodal fake news detection is crucial for mitigating societal disinformation. Existing approaches attempt to address this by fusing multimodal features or leveraging Large Language Models (LLMs) for advanced reasoning. However, these methods suffer from serious limitations, including a lack of comprehensive multi-view judgment and fusion, and prohibitive reasoning inefficiency due to the high computational costs of LLMs. To address these issues, we propose LLM-Guided Multi-View Reasoning Distillation for Fake News Detection ( LLM-MRD), a novel teacher-student framework. The Student Multi-view Reasoning module first constructs a comprehensive foundation from textual, visual, and cross-modal perspectives. Then, the Teacher Multi-view Reasoning module generates deep reasoning chains as rich supervision signals. Our core Calibration Distillation mechanism efficiently distills this complex reasoning-derived knowledge into the efficient student model. Experiments show LLM-MRD significantly outperforms state-of-the-art baselines. Notably, it demonstrates a comprehensive average improvement of 5.19% in ACC and 6.33% in F1-Fake when evaluated across all competing methods and datasets. Our code is available at https://github.com/Nasuro55/LLM-MRD

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