Mitigating Object Hallucination in Large Vision-Language Models via False Discovery Controlled Visual Data Splitting

Chang Liu; Yu Tian; Rui Xie

cs.CVSep 30, 2026

Mitigating Object Hallucination in Large Vision-Language Models via False Discovery Controlled Visual Data Splitting

Authors: Chang Liu, Yu Tian, Rui Xie

Organizations: School of Data, Mathematical, and Statistical Sciences Institute of Artificial Intelligence University of Central Florida

Abstract

Multiple object hallucination, where large vision-language models (LVLMs) generate objects not supported by the visual input, is a persistent challenge caused by visual uncertainty during decoding. Existing methods reduce hallucinations using contrastive signals, but they rely on heuristics and lack principled control of false positives at the image level. To address this, we propose False Discovery Rate-COntRol of HALlucination (CORAL), a training-free framework that models visual uncertainty using an uncertainty-aware visual data splitting strategy and leverages mirror statistics to quantify visual contrast during decoding. By computing mirror statistics from paired, symmetrically perturbed visual inputs, CORAL estimates spurious object predictions and sets a data-driven threshold to control the expected fraction of false discoveries per image, suppressing hallucinations while retaining high power for truly grounded objects. The framework is flexible, supports multiple LVLMs, and mitigates hallucinations without retraining or supervision. Extensive experiments on multiple benchmarks with several evaluation metrics demonstrate that CORAL consistently outperforms state-of-the-art methods, providing more reliable and robust hallucination control. Code is available at: https://changliu1993-cl.github.io/CORAL/

Figures & tables

Figure 1 : Hallucination under multiple queries. The model may generate plausible but hallucinated objects (e.g., “shrimp” , in red ). CORAL performs image-level selection, rejecting hallucinations while retaining visually grounded objects under False Discovery Rate (FDR) control.

Figure 2 : Illustration of CORAL , which applies visual uncertainty splitting and mirror statistic to control the FDR of hallucinated objects. The hallucinated object “Dog” ( red ) is removed under FDR control.

Figure 3 : Uncertainty-aware visual data splitting . Paired, symmetrically perturbed visual views are generated from a shared noise source, producing contrastive views for the mirror statistic construction and thereby facilitating the separation of visual signal from noise.

Method	LLaVA-OneVision-7B		Qwen2.5-VL-7B		InternVL3-8B		Average
Method	Overall FDR $\downarrow$	Overall Power $\uparrow$	Overall FDR $\downarrow$	Overall Power $\uparrow$	Overall FDR $\downarrow$	Overall Power $\uparrow$	Overall FDR $\downarrow$	Overall Power $\uparrow$
Random
Regular	0.0935 ( $\pm$ 0.0133)	75.36 ( $\pm$ 1.88)	0.0944 ( $\pm$ 0.0114)	77.43 ( $\pm$ 1.22)	0.0946 ( $\pm$ 0.0122)	73.52 ( $\pm$ 1.77)	0.0942 ( $\pm$ 0.0111)	75.44 ( $\pm$ 1.44)
VCD ( 2024 )	0.0892 ( $\pm$ 0.0188)	78.50 ( $\pm$ 1.23)	0.0908 ( $\pm$ 0.0132)	79.14 ( $\pm$ 1.53)	0.0899 ( $\pm$ 0.0109)	78.86 ( $\pm$ 1.34)	0.0900 ( $\pm$ 0.0145)	78.83 ( $\pm$ 1.31)
MARINE ( 2025 )	0.0846 ( $\pm$ 0.0122)	81.25 ( $\pm$ 1.17)	0.0866 ( $\pm$ 0.0116)	80.05 ( $\pm$ 1.60)	0.0889 ( $\pm$ 0.0133)	79.84 ( $\pm$ 1.33)	0.0867 ( $\pm$ 0.0124)	80.38 ( $\pm$ 1.21)
AGLA ( 2025 )	0.0799 ( $\pm$ 0.0223)	84.92 ( $\pm$ 1.11)	0.0821 ( $\pm$ 0.0114)	84.67 ( $\pm$ 1.41)	0.0810 ( $\pm$ 0.0144)	80.92 ( $\pm$ 1.33)	0.0810 ( $\pm$ 0.0123)	83.50 ( $\pm$ 1.50)

Table 1 : Evaluation of overall FDR control and overall power across multiple LVLM architectures on MSCOCO (3000 runs). Overall FDR and power denote averages of per-image FDR and power. Bold indicates the best result and underline indicates the second-best result.

Method	LLaVA-OneVision-7B		Qwen2.5-VL-7B		InternVL3-8B		Average
Method	Accuracy $\uparrow$	F1 Score $\uparrow$	Accuracy $\uparrow$	F1 Score $\uparrow$	Accuracy $\uparrow$	F1 Score $\uparrow$	Accuracy $\uparrow$	F1 Score $\uparrow$
Random
Regular	85.87 ( $\pm$ 1.77)	85.72 ( $\pm$ 1.66)	86.20 ( $\pm$ 1.30)	86.98 ( $\pm$ 1.28)	87.15 ( $\pm$ 1.88)	86.26 ( $\pm$ 1.80)	86.41 ( $\pm$ 1.65)	86.32 ( $\pm$ 1.58)
VCD ( 2024 )	86.33 ( $\pm$ 1.23)	88.86 ( $\pm$ 1.43)	87.35 ( $\pm$ 1.83)	90.06 ( $\pm$ 2.03)	88.15 ( $\pm$ 1.53)	88.05 ( $\pm$ 1.45)	87.28 ( $\pm$ 1.53)	88.99 ( $\pm$ 1.64)
MARINE ( 2025 )	87.21 ( $\pm$ 1.35)	89.09 ( $\pm$ 1.09)	89.72 ( $\pm$ 1.11)	90.33 ( $\pm$ 2.09)	89.23 ( $\pm$ 1.35)	89.15 ( $\pm$ 1.19)	88.72 ( $\pm$ 1.27)	89.52 ( $\pm$ 1.46)
AGLA ( 2025 )	88.15 ( $\pm$ 1.65)	89.97 ( $\pm$ 1.21)	90.01 ( $\pm$ 1.23)	91.17 ( $\pm$ 1.51)	92.53 ( $\pm$ 1.42)	93.54 ( $\pm$ 1.77)	90.23 ( $\pm$ 1.43)	91.56 ( $\pm$ 1.50)

Table 2 : Evaluation with POPE score across multiple LVLM architectures on the MSCOCO dataset. We report individualized Accuracy and F1 score (mean

\pm

std over 3000 runs). Bold indicates the best result and underline indicates the second-best result.

Model	Method	Object		Attribute	Relation		Total $\uparrow$
Model	Method	Existence $\uparrow$	Count $\uparrow$	Color $\uparrow$	Position $\uparrow$	Commonsense $\uparrow$	Total $\uparrow$
LLaVA-OneVision-7B	Regular	190.33 ( $\pm$ 6.50)	145.53 ( $\pm$ 15.20)	170.66 ( $\pm$ 9.10)	160.25 ( $\pm$ 8.30)	70.16 ( $\pm$ 6.50)	736.93 ( $\pm$ 24.80)
	VCD ( 2024 )	186.25 ( $\pm$ 7.22)	147.25 ( $\pm$ 11.44)	175.35 ( $\pm$ 15.58)	165.36 ( $\pm$ 2.55)	79.42 ( $\pm$ 5.74)	753.63 ( $\pm$ 18.76)
	MARINE ( 2025 )	191.26 ( $\pm$ 4.55)	150.44 ( $\pm$ 10.15)	177.45 ( $\pm$ 10.96)	165.36 ( $\pm$ 7.19)	82.33 ( $\pm$ 5.29)	766.84 ( $\pm$ 17.47)
	AGLA ( 2025 )	190.54 ( $\pm$ 6.22)	153.32 ( $\pm$ 15.37)	178.42 ( $\pm$ 3.22)	170.35 ( $\pm$ 4.22)	84.36 ( $\pm$ 5.21)	776.99 ( $\pm$ 15.96)
	CORAL (Ours)	193.67 ( $\pm$ 3.21)	160.43 ( $\pm$ 10.11)	180.35 ( $\pm$ 8.46)	175.24 ( $\pm$ 10.58)	85.52 ( $\pm$ 4.22)	795.21 ( $\pm$ 18.12)

Table 3 : Evaluation with MME score (mean

\pm

std over 3000 runs) across multiple LVLM architectures on MSCOCO. Bold indicates the best result and underline indicates the second-best result.

Method	LLaVA-OneVision-7B	Qwen2.5-VL-7B	InternVL3-8B
Regular	80.77 ( $\pm$ 1.21)	83.50 ( $\pm$ 1.01)	83.40 ( $\pm$ 0.85)
VCD ( 2024 )	82.67 ( $\pm$ 1.00)	85.14 ( $\pm$ 1.32)	86.84 ( $\pm$ 0.99)
MARINE ( 2025 )	85.31 ( $\pm$ 1.09)	87.44 ( $\pm$ 1.17)	88.67 ( $\pm$ 1.03)
AGLA ( 2025 )	85.76 ( $\pm$ 1.11)	87.02 ( $\pm$ 1.07)	90.15 ( $\pm$ 1.00)
CORAL (Ours)	86.70 ( $\pm$ 1.13)	87.90 ( $\pm$ 0.91)	94.50 ( $\pm$ 0.80)

Table 4 : Evaluation with MMBench score (mean

\pm

std over 3000 runs) across multiple LVLM architectures on MSCOCO. Bold indicates the best result and underline indicates the second-best result.

Method	Accuracy $\uparrow$	Precision $\uparrow$	Recall $\uparrow$	F1 Score $\uparrow$
Regular	85.87 ( $\pm$ 1.77)	83.41 ( $\pm$ 2.13)	88.08 ( $\pm$ 1.47)	85.72 ( $\pm$ 1.66)
w/o Visual Uncertainty Splitting	88.12 ( $\pm$ 0.62)	86.78 ( $\pm$ 0.74)	89.07 ( $\pm$ 0.71)	87.90 ( $\pm$ 0.58)
w/o Mirror Statistic	88.76 ( $\pm$ 0.55)	87.07 ( $\pm$ 0.49)	90.33 ( $\pm$ 0.61)	88.67 ( $\pm$ 0.53)
w/o Overall FDR Control	89.36 ( $\pm$ 0.48)	86.42 ( $\pm$ 0.92)	92.37 ( $\pm$ 0.56)	89.27 ( $\pm$ 0.41)
CORAL (Ours)	92.17 ( $\pm$ 0.89)	88.25 ( $\pm$ 1.46)	94.87 ( $\pm$ 1.61)	91.64 ( $\pm$ 1.83)

Table 5 : Ablation study on POPE metrics using the MSCOCO dataset with LLaVA-OneVision-7B. Results are mean

\pm

std over 10 runs. Bold indicates the best result.

Figure 4 : Ablation study on the effect of FDR target level ( $q$ ) on the performance of LLaVA-OneVision-7B, Qwen2.5-VL-7B, InternVL3-8B using POPE metrics with $q=\{0.01,0.03,0.05,0.1,0.2\}$ .

Figure 5 : Hallucination mitigation examples by our method CORAL across multiple tasks. Hallucinated objects are highlighted in red .

Appendix figures & tables22 assets

Supplementary material from the paper’s appendix.

Appendix

Model	Vision encoder	LLM
LLaVA-v1.5 [ 1 ]	CLIP-L-336px [ 57 ]	Vicuna-v1.5-7B [ 58 ]
Qwen-VL [ 53 ]	ViT-based visual encoder	Qwen-7B [ 53 ]
InstructBLIP [ 52 ]	BLIP-2 [ 3 ]	Vicuna-v1.1-7B [ 58 ]
LLaVA-OneVision-7B [ 49 ]	CLIP ViT-L/14 (336px) [ 57 ]	Vicuna-7B [ 58 ]
Qwen2.5-VL-7B [ 50 ]	ViT-based visual encoder	Qwen2.5-7B [ 50 ]
InternVL3-8B [ 51 ]	InternViT (high-resolution ViT)	InternLM2-8B [ 51 ]

Appendix

Table 6 : Details of the LVLM architectures used in our experiments.

Template Type	Prompt Template
POPE task	This image contains only the following objects: <OBJECT_GROUNDING> . Do not assume any objects beyond this list. Based solely on this information, <QUERY> The detected objects in the image are: <OBJECT_GROUNDING> . Answer the question using only these objects. <QUERY> This image shows the following objects: <OBJECT_GROUNDING> . You must answer using only the objects in this list.Given these detected objects, <QUERY> The objects found in this image are limited to: <OBJECT_GROUNDING> . You should rely strictly on this list of objects and make no other guesses. Based on this, <QUERY>
CORAL grounded	This image contains the following visually grounded objects: <OBJECT_GROUNDING> . Based on the image, <QUERY> The following objects are visible in the image: <OBJECT_GROUNDING> . Using only the information from the image, <QUERY> This image shows: <OBJECT_GROUNDING> . Please answer the following question based on the image content: <QUERY>
CORAL -restricted	The objects visible in this image are limited to: <OBJECT_GROUNDING> . Do not assume any objects beyond this list. <QUERY> Only the following objects appear in the image: <OBJECT_GROUNDING> . Answer the question using only visual evidence from the image. <QUERY> Based strictly on the objects shown in the image: <OBJECT_GROUNDING> . Do not infer any additional objects. <QUERY>
CORAL -complementary	The same image is analyzed using multiple internally constructed visual representations, including the original view and two mirrored views. Original view detects the following objects: <OBJECT_GROUNDING> Mirror view (+) detects the following objects: <OBJECT_GROUNDING_A> Mirror view (-) detects the following objects: <OBJECT_GROUNDING_B> Using the visual information above from the same image, <QUERY> Multiple complementary visual representations are derived from the same image. Original view objects: <OBJECT_GROUNDING> Mirrored view (+) objects: <OBJECT_GROUNDING_A> Mirrored view (-) objects: <OBJECT_GROUNDING_B> Based on the image, <QUERY>

Appendix

Table 7 : Details of the LVLM architectures that we used in our paper.

Figure 6 : Empirical validation of mirror symmetry for mirror statistic $\Delta_{t}$ . The figure shows overlaid histograms of $\Delta_{t}$ and its sign-flipped counterpart $-\Delta_{t}$ computed from negative POPE queries, where the queried object is guaranteed to be absent. The strong overlap between the two distributions and their near symmetry around zero indicate that mirror statistic for visually ungrounded objects are approximately symmetric, supporting their use as reference quantities for FDR estimation.

Figure 7 : Quantile-Quantile (QQ) plot comparing the empirical quantiles of $\Delta_{t}$ and $-\Delta_{t}$ for negative POPE queries. The close alignment with the identity line across the full range of quantiles indicates approximate distributional symmetry of the mirror statistic, providing empirical support for the mirror-symmetry assumption used in Eq. ( 8 ).

Parameters	Value
Amplification Factor $\alpha$	1
Adaptive Plausibility Threshold $\beta$	0.1
Diffusion Noise Step	500

Appendix

Table 8 : VCD [ 24 ] Hyperparameter Settings.

Parameters	Value
Guidance Strength	0.7
Score Threshold for DERT	0.95
Detect Threshold for RAM++	0.68

Appendix

Table 9 : MARINE [ 22 ] Hyperparameter Settings.

Parameters	Value
Weighting Factor $\alpha$	2
Adaptive Plausibility Constraint Factor $\beta$	0.5

Appendix

Table 10 : AGLA [ 23 ] Hyperparameter Settings.

Parameters	Value
Data Splitting Factor $\tau$	0.1
FDR Thresholding $q$ (false positive level in an image)	0.1

Appendix

Table 11 : CORAL Hyperparameter Settings. The settings are fixed depending on the question-answer tasks.

Model	Batch Size
LLaVA-v1.5	4
Qwen-VL	16
InstructBLIP	16
LLaVA-OneVision-7B	4
Qwen2.5-VL-7B	16
InternVL3-8B	8

Appendix

Table 12 : Batch size settings for LVLM generation across different models. Unless otherwise noted, the batch size is fixed for each model throughout all experiments. To improve evaluation efficiency, we adopt batched generation. When the LVLM does not explicitly specify a padding strategy for inference, we apply left padding to avoid potential negative effects of batched generation.

Method	LLaVA-v1.5			Qwen-VL			InstructBLIP			LLaVA-OneVision-7B			Qwen2.5-VL-7B			InternVL3-8B
Method	$C_{S}\downarrow$	$C_{I}\downarrow$	Power $\uparrow$	$C_{S}\downarrow$	$C_{I}\downarrow$	Power $\uparrow$	$C_{S}\downarrow$	$C_{I}\downarrow$	Power $\uparrow$	$C_{S}\downarrow$	$C_{I}\downarrow$	Power $\uparrow$	$C_{S}\downarrow$	$C_{I}\downarrow$	Power $\uparrow$	$C_{S}\downarrow$	$C_{I}\downarrow$	Power $\uparrow$
Regular	9.2	5.1	94.8	9.5	19.2	80.7	8.7	4.9	95.2	8.8	4.6	95.4	8.2	18.1	81.9	5.0	3.2	96.8
VCD ( 2024 )	7.8	4.5	95.3	7.4	18.5	81.3	7.3	4.1	95.9	7.3	4.1	95.9	6.8	17.4	82.6	2.4	1.5	98.5
MARINE ( 2025 )	6.9	3.8	96.1	6.3	14.5	84.7	6.2	3.0	97.0	6.2	3.0	97.0	5.9	13.8	86.2	2.2	1.3	98.7
AGLA ( 2025 )	7.5	4.2	95.8	6.1	12.4	87.2	7.0	3.8	96.2	7.0	3.8	96.2	5.6	11.2	88.8	2.3	1.6	98.4
CORAL (Ours)	5.8	3.1	96.9	4.5	11.0	88.9	5.0	2.6	97.4	5.0	2.6	97.4	3.8	10.8	89.2	1.8	1.3	98.7

Appendix

Table 13 : Evaluation with CHAIR score across multiple LVLM architectures. Lower

C_{S}

and

C_{I}

indicate fewer hallucinated objects, while higher power indicates better retention of visually grounded objects. Bold indicates the best result.

Model	Method	Attribute			Relation	Total $\uparrow$
Model	Method	Existence $\uparrow$	Count $\uparrow$	Color $\uparrow$	Position $\uparrow$	Total $\uparrow$
LLaVA-v1.5	Regular	175.67 ( $\pm$ 7.51)	124.67 ( $\pm$ 19.59)	151.00 ( $\pm$ 10.45)	114.00 ( $\pm$ 9.32)	565.33 ( $\pm$ 33.92)
	VCD ( 2024 )	184.66 ( $\pm$ 6.81)	138.33 ( $\pm$ 15.68)	153.00 ( $\pm$ 7.58)	128.67 ( $\pm$ 7.21)	604.66 ( $\pm$ 18.76)
	MARINE ( 2025 )	190.53 ( $\pm$ 7.26)	154.43 ( $\pm$ 16.01)	166.34 ( $\pm$ 6.96)	130.28 ( $\pm$ 8.01)	641.58 ( $\pm$ 17.47)
	AGLA ( 2025 )	195.00 ( $\pm$ 7.32)	153.89 ( $\pm$ 16.32)	167.67 ( $\pm$ 6.42)	129.44 ( $\pm$ 7.81)	646.00 ( $\pm$ 15.96)
	CORAL (Ours)	194.43 ( $\pm$ 8.38)	157.41 ( $\pm$ 15.11)	167.34 ( $\pm$ 7.33)	131.19 ( $\pm$ 7.58)	650.37 ( $\pm$ 18.12)

Appendix

Table 14 : Evaluation with MME score across multiple LVLM architectures on MSCOCO with 3000 random replications. We report the mean

\pm

standard deviation. Bold indicates the best result and underline indicates the second-best result.

Figure 8 : Inference latency comparisons. All measurements are conducted on a single NVIDIA RTX 6000 GPU with batch size 1, using identical input images and prompts.

Method	LLaVA-v1.5		Qwen-VL		InstructBLIP		Average
Method	Overall FDR $\downarrow$	Overall Power $\uparrow$	Overall FDR $\downarrow$	Overall Power $\uparrow$	Overall FDR $\downarrow$	Overall Power $\uparrow$	Overall FDR $\downarrow$	Overall Power $\uparrow$
Random
Regular	0.0985 ( $\pm$ 0.0144)	77.32 ( $\pm$ 1.41)	0.0913 ( $\pm$ 0.0122)	76.08 ( $\pm$ 1.23)	0.0957 ( $\pm$ 0.0102)	74.35 ( $\pm$ 1.63)	0.0952 ( $\pm$ 0.0122)	75.92 ( $\pm$ 1.42)
VCD ( 2024 )	0.0965 ( $\pm$ 0.0200)	82.50 ( $\pm$ 1.13)	0.0898 ( $\pm$ 0.0111)	78.10 ( $\pm$ 1.12)	0.0941 ( $\pm$ 0.0119)	76.80 ( $\pm$ 1.22)	0.0935 ( $\pm$ 0.0143)	79.13 ( $\pm$ 1.16)
MARINE ( 2025 )	0.0935 ( $\pm$ 0.0112)	89.20 ( $\pm$ 1.07)	0.0852 ( $\pm$ 0.0119)	79.05 ( $\pm$ 1.13)	0.0944 ( $\pm$ 0.0114)	77.14 ( $\pm$ 1.35)	0.0910 ( $\pm$ 0.0115)	81.80 ( $\pm$ 1.18)
AGLA ( 2025 )	0.0918 ( $\pm$ 0.0107)	92.80 ( $\pm$ 1.15)	0.0818 ( $\pm$ 0.0132)	82.67 ( $\pm$ 1.10)	0.0921 ( $\pm$ 0.0122)	78.92 ( $\pm$ 1.21)	0.0886 ( $\pm$ 0.0120)	84.80 ( $\pm$ 1.15)

Appendix

Table 15 : Evaluation of overall FDR control and overall power across multiple LVLM architectures on MSCOCO with 3000 random replications. Overall FDR and power denote averages of per-image FDR and power across images. Bold indicates the best result and underline indicates the second-best result in each column.

Method	LLaVA-v1.5		Qwen-VL		InstructBLIP		Average
	FDR $\downarrow$	Power $\uparrow$	FDR $\downarrow$	Power $\uparrow$	FDR $\downarrow$	Power $\uparrow$	FDR $\downarrow$	Power $\uparrow$
Random
Regular	0.0957 ( $\pm$ 0.0131)	77.57 ( $\pm$ 1.28)	0.0953 ( $\pm$ 0.0133)	79.53 ( $\pm$ 1.52)	0.0987 ( $\pm$ 0.0107)	75.34 ( $\pm$ 1.26)	0.0966 ( $\pm$ 0.0124)	77.48 ( $\pm$ 1.35)
VCD ( 2024 )	0.0930 ( $\pm$ 0.0100)	84.10 ( $\pm$ 1.22)	0.0892 ( $\pm$ 0.0121)	82.30 ( $\pm$ 1.13)	0.0967 ( $\pm$ 0.0112)	80.40 ( $\pm$ 1.15)	0.0930 ( $\pm$ 0.0111)	82.27 ( $\pm$ 1.17)
MARINE ( 2025 )	0.0920 ( $\pm$ 0.0101)	85.02 ( $\pm$ 1.35)	0.0835 ( $\pm$ 0.0202)	86.64 ( $\pm$ 1.05)	0.0920 ( $\pm$ 0.0100)	84.93 ( $\pm$ 1.08)	0.0892 ( $\pm$ 0.0134)	85.53 ( $\pm$ 1.16)
AGLA ( 2025 )	0.0892 ( $\pm$ 0.0112)	86.42 ( $\pm$ 1.42)	0.0832 ( $\pm$ 0.0158)	87.01 ( $\pm$ 1.11)	0.0923 ( $\pm$ 0.0123)	87.43 ( $\pm$ 1.11)	0.0882 ( $\pm$ 0.0131)	86.95 ( $\pm$ 1.21)

Appendix

Table 16 : Evaluation on overall FDR control and overall power across multiple LVLM architectures on A-OKVQA with 3000 random replications. Bold indicates the best result and underline indicates the second-best result in each column.

Method	LLaVA-v1.5		Qwen-VL		InstructBLIP		Average
	FDR $\downarrow$	Power $\uparrow$	FDR $\downarrow$	Power $\uparrow$	FDR $\downarrow$	Power $\uparrow$	FDR $\downarrow$	Power $\uparrow$
Random
Regular	0.0933 ( $\pm$ 0.0132)	76.55 ( $\pm$ 1.32)	0.0914 ( $\pm$ 0.0188)	81.66 ( $\pm$ 1.01)	0.0889 ( $\pm$ 0.0188)	83.88 ( $\pm$ 1.52)	0.0912 ( $\pm$ 0.0169)	80.69 ( $\pm$ 1.28)
VCD ( 2024 )	0.0924 ( $\pm$ 0.0098)	80.92 ( $\pm$ 0.68)	0.0924 ( $\pm$ 0.0090)	80.24 ( $\pm$ 1.00)	0.0872 ( $\pm$ 0.0145)	85.47 ( $\pm$ 0.62)	0.0907 ( $\pm$ 0.0111)	82.21 ( $\pm$ 0.77)
MARINE ( 2025 )	0.0894 ( $\pm$ 0.0128)	86.92 ( $\pm$ 0.40)	0.0902 ( $\pm$ 0.0101)	84.22 ( $\pm$ 0.93)	0.0837 ( $\pm$ 0.0144)	87.39 ( $\pm$ 0.55)	0.0878 ( $\pm$ 0.0124)	86.18 ( $\pm$ 0.63)
AGLA ( 2025 )	0.0802 ( $\pm$ 0.0164)	90.42 ( $\pm$ 1.02)	0.0872 ( $\pm$ 0.0100)	85.11 ( $\pm$ 1.09)	0.0855 ( $\pm$ 0.0148)	89.43 ( $\pm$ 0.41)	0.0843 ( $\pm$ 0.0137)	88.99 ( $\pm$ 0.84)

Appendix

Table 17 : Evaluation on overall FDR control and overall power across multiple LVLM architectures on GQA with 3000 random replications. Bold indicates the best result and underline indicates the second-best result in each column.

Decoding	LLaVA-v1.5				Qwen-VL
Decoding	Accuracy $\uparrow$	Precision $\uparrow$	Recall $\uparrow$	F1 Score $\uparrow$	Accuracy $\uparrow$	Precision $\uparrow$	Recall $\uparrow$	F1 Score $\uparrow$
Random
Regular	83.29 ( $\pm$ 0.35)	92.13 ( $\pm$ 0.54)	72.80 ( $\pm$ 0.57)	81.33 ( $\pm$ 0.41)	84.73 ( $\pm$ 0.36)	95.61 ( $\pm$ 0.45)	72.81 ( $\pm$ 0.38)	82.67 ( $\pm$ 0.41)
VCD ( 2024 )	87.73 ( $\pm$ 0.40)	91.42 ( $\pm$ 0.55)	83.28 ( $\pm$ 0.42)	87.16 ( $\pm$ 0.41)	88.63 ( $\pm$ 0.10)	94.64 ( $\pm$ 0.25)	81.91 ( $\pm$ 0.19)	87.81 ( $\pm$ 0.11)
MARINE ( 2025 )	85.01 ( $\pm$ 0.24)	88.27 ( $\pm$ 0.83)	80.73 ( $\pm$ 0.12)	84.33 ( $\pm$ 0.31)	82.07 ( $\pm$ 0.14)	89.27 ( $\pm$ 0.13)	89.33 ( $\pm$ 0.24)	85.83 ( $\pm$ 0.87)
AGLA ( 2025 )	88.54 ( $\pm$ 0.64)	94.41 ( $\pm$ 0.50)	82.08 ( $\pm$ 0.47)	87.71 ( $\pm$ 0.51)	84.60 ( $\pm$ 0.76)	98.23 ( $\pm$ 0.29)	70.47 ( $\pm$ 0.41)	82.07 ( $\pm$ 0.11)

Appendix

Table 18 : Evaluation with POPE score across multiple LVLM architectures on MSCOCO dataset comparing our method with several baselines. Bold indicates the best result and underline indicates the second-best result in each column.

Decoding	LLaVA-v1.5				Qwen-VL
	Accuracy $\uparrow$	Precision $\uparrow$	Recall $\uparrow$	F1 Score $\uparrow$	Accuracy $\uparrow$	Precision $\uparrow$	Recall $\uparrow$	F1 Score $\uparrow$
Random
Regular	83.45 ( $\pm$ 0.48)	87.24 ( $\pm$ 0.68)	78.36 ( $\pm$ 0.54)	82.56 ( $\pm$ 0.50)	86.67 ( $\pm$ 0.48)	93.16 ( $\pm$ 0.55)	79.16 ( $\pm$ 0.59)	85.59 ( $\pm$ 0.53)
VCD ( 2024 )	86.15 ( $\pm$ 0.23)	85.18 ( $\pm$ 0.34)	87.53 ( $\pm$ 0.14)	86.34 ( $\pm$ 0.21)	89.22 ( $\pm$ 0.14)	90.77 ( $\pm$ 0.04)	87.32 ( $\pm$ 0.34)	89.01 ( $\pm$ 0.16)
MARINE ( 2025 )	86.72 ( $\pm$ 0.14)	87.71 ( $\pm$ 0.53)	87.53 ( $\pm$ 0.34)	86.34 ( $\pm$ 0.56)	89.17 ( $\pm$ 0.45)	89.87 ( $\pm$ 0.42)	88.13 ( $\pm$ 0.54)	88.93 ( $\pm$ 0.18)
AGLA ( 2025 )	89.28 ( $\pm$ 0.33)	93.18 ( $\pm$ 0.43)	84.76 ( $\pm$ 0.42)	88.77 ( $\pm$ 0.56)	86.77 ( $\pm$ 0.43)	95.02 ( $\pm$ 0.11)	77.60 ( $\pm$ 0.72)	85.43 ( $\pm$ 0.98)

Appendix

Table 19 : Evaluation with POPE score across multiple LVLM architectures on A-OKVQA dataset comparing our method with several baselines with 3000 random replications. Bold indicates the best result and underline indicates the second-best result in each column.

Decoding	LLaVA-v1.5				Qwen-VL
	Accuracy $\uparrow$	Precision $\uparrow$	Recall $\uparrow$	F1 Score $\uparrow$	Accuracy $\uparrow$	Precision $\uparrow$	Recall $\uparrow$	F1 Score $\uparrow$
Random
Regular	83.73 ( $\pm$ 0.27)	87.16 ( $\pm$ 0.39)	79.12 ( $\pm$ 0.35)	82.95 ( $\pm$ 0.28)	80.97 ( $\pm$ 0.32)	88.07 ( $\pm$ 0.34)	71.64 ( $\pm$ 0.57)	79.01 ( $\pm$ 0.40)
VCD ( 2024 )	86.65 ( $\pm$ 0.45)	84.58 ( $\pm$ 0.59)	89.24 ( $\pm$ 0.34)	86.99 ( $\pm$ 0.41)	85.59 ( $\pm$ 0.38)	86.88 ( $\pm$ 0.44)	83.84 ( $\pm$ 0.36)	85.33 ( $\pm$ 0.38)
MARINE ( 2025 )	86.33 ( $\pm$ 0.14)	85.02 ( $\pm$ 0.63)	88.23 ( $\pm$ 0.15)	87.24 ( $\pm$ 0.11)	85.54 ( $\pm$ 0.52)	87.35 ( $\pm$ 0.25)	87.26 ( $\pm$ 0.22)	86.63 ( $\pm$ 0.22)
AGLA ( 2025 )	86.46 ( $\pm$ 0.34)	85.84 ( $\pm$ 0.25)	87.31 ( $\pm$ 0.15)	86.57 ( $\pm$ 0.15)	83.90 ( $\pm$ 0.43)	93.05 ( $\pm$ 0.09)	73.26 ( $\pm$ 0.82)	81.98 ( $\pm$ 0.13)

Appendix

Table 20 : Evaluation with POPE score across multiple LVLM architectures on GQA dataset, comparing our method with several baselines with 3000 random replications. Bold indicates the best result and underline indicates the second-best result in each column.

Figure 9 : Ablation study on the effect of FDR target level ( $q$ ) on the performance of LLaVA-v1.5, Qwen-VL, InstructBLIP using POPE metrics with $q=\{0.01,0.03,0.05,0.1,0.2\}$ .

Figure 10 : Sensitivity analysis of FDR and power with respect to the perturbation scale $\tau_{v}$ . We report empirical FDR and power under different values of $\tau_{v}\in\{0.01,0.05,0.10,0.20,1.00\}$ while fixing the target FDR level at $q=0.1$ . The results show that CORAL achieves a favorable trade-off at intermediate perturbation scales and remains robust across a wide range of $\tau_{v}$ values.

Figure 11 : Sensitivity analysis of POPE accuracy and F1 with respect to the perturbation scale $\tau_{v}$ . Performance is evaluated under different values of $\tau_{v}\in\{0.01,0.05,0.10,0.20,1.00\}$ while fixing the target FDR level at $q=0.1$ . The results indicate that CORAL is not sensitive to precise tuning of $\tau_{v}$ and achieves stable performance across a broad range of perturbation scales.

Figure 12 : Representative failure cases of CORAL on LLaVA-v1.5. Each example compares responses with and without CORAL to the same image and query. The cases illustrate remaining errors in object recognition and activity description, including failure to recognize a present cat, an unsupported traffic-light prediction, an inconsistent response concerning a knife, and confusion involving a surfboard and parasailing. Red and green text highlight incorrect and correct response components, respectively.

Explore similar work

Same Attention, Different Truths: Put Logit-Lens over Visual Attention to Detect and Mitigate LVLM Object Hallucination
Aug 7, 2026Zichuan Wang, Songlin Yang, Bo Peng +4Object Hallucination Citation Hallucination
School of Artificial Intelligence, University of Chinese Academy of Sciences · 3Hong Kong University of Science and Technology · 2New Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Science
Test-Time Hallucination Control in Large Vision-Language Models
Aug 11, 2026Mehran Tamjidi, Hamidreza Dastmalchi, Ali Cheraghian +3Large Language Model Hallucination Hallucination Mitigation
University of Technology Sydney · York University, Canada · Australian National University +2
Mitigating Object Hallucinations in Vision-Language Models through Region-Aware Attention Recalibration
May 24, 2026Yuanzhi Xu, Qian Gao, Jun Fan +4Object Hallucination Hallucination Mitigation
Qilu University of Technology (Shandong Academy of Sciences) Jinan, China · China Telecom Digital Intelligence Technology Co, Ltd Jinan, China · Shenyang Aerospace University Shenyang, China +1

Aug 7, 2026, cs.CVJ/K move · Enter open · S save

Same Attention, Different Truths: Put Logit-Lens over Visual Attention to Detect and Mitigate LVLM Object Hallucination

Zichuan Wang, Songlin Yang, Bo Peng, Zhenchen Tang+3

School of Artificial Intelligence, University of Chinese Academy of Sciences · 3Hong Kong University of Science and Technology · 2New Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Science

Object Hallucination Citation Hallucination Logit Lens

Open paper AskPDF arXiv

Large Vision-Language Models (LVLMs) often suffer from object hallucination, generating objects that are absent from the image. Prior work largely attributes this to insufficient visual attention. However, we find that both real and hallucinated objects receive equally strong visual attention in the model's mid-to-late layers, suggesting that the key issue may not be how much the model attends, but what it attends to and why. To this end, we decode the visual features of high-attention regions using Logit Lens, and observe that regions corresponding to real objects can be correctly decoded to the target object tokens, whereas those for hallucinated objects cannot. Building on this, we identify two hallucination mechanisms: (i) visual uncertainty, triggered by semantically similar or confusable regions; masking these regions eliminates the hallucination. (ii) contextual prior, triggered by strong co-occurrence priors; even when the initially attended region is masked, the hallucination persists and attention drifts to other regions. Based on these findings, we propose a simple yet effective training-free Detect-Mitigate framework comprising a Logit-Lens Consistency Check to detect hallucination and targeted remedies: High-Attention Regions Masking (HARM) for visual uncertainty hallucination, and Visual Evidence Enhanced Decoding (VEED) for contextual prior hallucination. Our approach achieves state-of-the-art results on multiple hallucination benchmarks. Code will be available.

Abstract

Explore similar work

Same Attention, Different Truths: Put Logit-Lens over Visual Attention to Detect and Mitigate LVLM Object Hallucination

Test-Time Hallucination Control in Large Vision-Language Models

Mitigating Object Hallucinations in Vision-Language Models through Region-Aware Attention Recalibration