Detecting Misbehaviors of Large Vision-Language Models by Evidential Uncertainty Quantification
Authors: Tao Huang
Rui Wang
Xiaofei Liu
Yi Qin
Li Duan
Liping Jing
Venue: ICLR 2026 Poster (2026)
Keywords: Misbehavior Detection
Large Vision-Language Model
Evidential Theory
Uncertainty
Primary area: alignment, fairness, safety, privacy, and societal considerations

Abstract: Large vision-language models (LVLMs) have achieved substantial advances in multimodal understanding. However, when presented with \textcolor{black}{challenging or distribution-shifted inputs}, they frequently produce unreliable or even harmful content, \textcolor{black}{such as hallucinations or toxic responses. We refer to such misalignments with human expectations as \emph{misbehaviors} of LVLMs, which} raise serious concerns for their deployment in critical applications. \textcolor{black}{Existing research have disclosed that such misbehaviors are closely linked to model uncertainty. We find they primarily stem from two distinct sources of epistemic uncertainty: internal contradictions (conflict) and the absence of supporting information (ignorance).} While existing uncertainty quantification methods typically capture only total predictive uncertainty, they struggle to distinguish between these underlying causes. To address this gap, we propose Evidential Uncertainty Quantification (EUQ), \textcolor{black}{a training-free framework that explicitly decomposes epistemic uncertainty into conflict (CF) and ignorance (IG)}. Specifically, we interpret features from the model output head as either supporting (positive) or opposing (negative) evidence. Leveraging Dempster-Shafer Theory of belief functions, we aggregate this evidence to quantify internal conflict and knowledge gaps within a single forward pass. We extensively evaluate EUQ across four misbehavior categories, including hallucinations, jailbreaks, adversarial vulnerabilities, and out-of-distribution (OOD) failures using state-of-the-art LVLMs. Experimental results demonstrate that EUQ consistently outperforms strong baselines, \textcolor{black}{achieving relative improvements of up to 10.5\% in AUROC.} \textcolor{black}{Our evaluation further reveals} that hallucinations correspond to high internal conflict and OOD failures to high ignorance. \textcolor{black}{Furthermore, a layer-wise evidential uncertainty dynamics analysis provides a novel perspective on the evolution of internal representations.} The source code is available at \url{https://github.com/HT86159/EUQ}.

Published as a conference paper at ICLR 2026
DETECTING MISBEHAVIORS OF LARGE VISION- LANGUAGE MODELS BY EVIDENTIAL UNCERTAINTY QUANTIFICATION
Tao Huang 1.23+ Rui Wangl.4; Xiaofei Li. Yi Qin Li Duan Liping Jing J.23
State Key Laboratory of Advanced Rail Autonomous Operation, China
2 Beijing Key Laboratory of Traffic Data Mining and Embodied Intelligence, China
3 School of Computer Science and Technology. Beijing Jiaotong University, China
"School of Automation and Intelligence, Beijing Jiaotong University, China
$ Beijjig Key Laboratory of Security and Privacy in Intelligent Transportation, China
(thuang, rui. wang, xiaofeiliu, yeeqin, duanli, 1pjing) @bjtu. edu. en
ABSTRACT
Large vision-language models (LVLMs) have achieved substantial advances in mul- timodal understanding. However, when presented with challenging distribution- shifted inputs, they frequently produce unreliable or even harmful content, such hallucinations or toxic responses. We refer to such misalignments with human expectations as misbehaviors of LVLMs, which raise serious concerns for their deployment in critical applications. Existing research have disclosed that such mis- behaviors are closely linked to model uncertainty. We find they primarily stem from two distinct sources of epistemic uncertainty: internal contradictions (conflict) and the absence of supporting information (ignorance). While existing uncertainty quan- tification methods typically capture only total predictive uncertainty, they struggle to distinguish between these underlying causes. To address this gap, propose Evidential Uncertainty Quantification (EUQ), training- -free framework that ex- plicitly decomposes epistemic uncertainty into conflict (CF) and ignorance (IG). Specifically, we interpret features from the model output head as either supporting (positive) opposing (negative) evidence. Leveraging Dempster-Shafer Theory of belief functions, we aggregate this evidence to quantify internal conflict and knowledge gaps within a single forward pass. We extensively evaluate EUQ across four misbehavior categories, including hallucinations, jailbreaks, adversarial vul- nerabilities, and diitrr-diittiittionn (OOD) failures using state-of-the--ar L.V.Ms. Experimental results demonstrate that EUQ consistently outperforms strong base- lines, achieving relative improvements of up to 10.5% in AUROC. Our evaluation further reveals that hallucinations correspond to high internal conflict and OOD fail- ures to high ignorance. Furthermore, layer- wise evidential uncertainty dynamics analysis provides a novel perspective the evolution of internal representations. The source code is available at httpt:::///// github...bbb... wom Cwwm//T86159//000
INTRODUCTION
Large Vision-Language Models (LVLMs) (Liu et al. 2024c Bai et al. 2025 Wu et al. 2024b) have demonstrated remarkable capabilities in multimodal understanding and context-aware reasoning across a variety of vision-language tasks (Ngiam et al., 2011 Chen et al. 2020). Nevertheless, their outputs can become unreliable or even harmful when faced with challenging, distribution-shiftee or adversarial inputs. Such challenges often lead to issues such as unfaithful hallucinations (Biten jet al., 2022. Li et al. 2023b), security risks through jailbreaks (Qi et al. 2024; Gong al., 2025). adversarial vulnerabilities (Fang et al.) 2024 Ge et al.) 2023). and failures to generalize out-of- distribution (OOD) (Yang et al. 2024
Xu et al. 2025).
These misbehaviors indicate that current
"Equal contribution.
"orresponning author.

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Published as a conference paper at ICLR 2026
LVLMs not yet fully aligned with human expectations (Bengio et al. 2025 Feng et al. 2026).
As a result, such failures significantly hinder their deployment in critical applications, such as identity authentication (Li et al. 2023a), autonomous driving (Grigorescu et al. 2020), and medical diagnosis (Kumar et al. 2023). This underscores the urgent need for effective detection and mitigation methods enhance model trustworthiness.
The connection between such misbehaviors and model uncertainty has been widely recognized (Far quhar et al., 2024; Liao et al.) [SO] 2022). Our focus mainly lies on a significant and reducible component, epistemic uncertainty, which is the limitation in model knowledge captured by its param- eters. This uncertainty has long been understood to originate from two primary sources (Denoeux iet al. 2020): the presence of conflicting information and the absence of supporting information. For instance, the top case in Figure |
illustrates the former; the model correctly identifies both the text. and the background image, yet their semantic inconsistency leads to response that casts doubt on the input. In contrast, the bottom example shows the latter, with the model perceiving color and shape but expressing "cannot immediately identify" and resorting to "guessing" due to missing information.
While misbehaviors in LVLMs often arise from internal conflicts or knowledge ab- sence, existing uncertainty quantification (UQ) approaches focus the total predic- tive uncertainty, but fail to explicitly cap- ture such underlying causes. Most classical uncertainty quantification (UQ) methods, such as Bayesian approaches (Ma 1992, Blundell et al. (2015) and their vari-
Input
LVLM Output
Error
inticc Text siating andwer
observe goldfihh
bedkground
Conflict between text background image.
This
for
1. Althoagh geldriin pant [Bssen)
(Ground truth: Goldfish)
Lecoeroon and casts (dee8)) con by nessonable
[s, The onswer (aspee
IRl tea a orrved ebject with orange Nnsiinl stapps.
inmediottlyy iterriyy
Semeen 100 due to missing information.
Ignorance
ants (Gal & Ghahramani 20166; Laksh
it sould be part of i toy er sene kind of cazerstion.
3. oder pottern loooina restmbles robiaw
minarayanan et al. |017 MMddox et al. 2019), as well as internal methods of deter- ministic models (Sensoy et al. 20188 Ma-
4.5 gusssing this might be partoon
(Ground truth: Paraglider)
The smawer nambont try..
linin& & Gales, 2019). are challenging to
The example shows how our evidential uncertainty, visualized as loken-level heatmap over the Saaain-f--hhuueee (CoT) (Kojima et al. (2022) traces, shows that the misbchavior may stem from internal conflict and a lack of knowledge.
Figure 1:
apply to LVLMs due to their substantial computational overhead. As a result, re- cent efforts predominantly adopt test-timm time sampling strategies. A typical strategy estimates overall epistemic uncertainty from token-level proba- bilities of single output (Kadavath et al. 2022 Malinin & Gales] 2021), while subsequent extensions evaluate semantic variability across multiple generations (Farquhar et al., 2024) MMnakul et al. 2023 Chen al. 2025). Another line of work encourages models to verbalize their confidence (Xiong ct al.] Lin et a..). However, such uncertainty is often unstable and uncalibrated, as LVLMs lack strong metacognitive capabilities, i.e., they struggle to reliably recognize and express their own uncertainty.
To this end, we propose Evidential Uncertainty Quantification (EUQ), which enables effective and computationally efficient detection of model misbehaviors. To the best of our knowledge. this the first attempt to explicitly characterize two types of epistemic uncertainty in LVLMs, conflict (CF) and ignorance (IG). CF quantifies the degree of contradiction among evidence in model predictions, while IG measures the lack of information available to the model. Specifically, we draw inspiration from the interpretation of linear projection as evidence fusion [Deneuu 2019 Huang et al. (025). Evidence is then constructed from the pre-logits features of the LVLM output head, which provide high-level signals directly linked to the model's 's decisions (Zhao et al. 2024), to quantify uncertainty. We then apply basic belief assignment (BBA), which distributes belief masses over hypotheses, to convert them into evidence weights. These weights are then decomposed into positive and negative components, which represent support and contradiction the model's decision. The refined evidence weights are fused using Dempster's 's rule of combination (Shafer 1976). yielding CF from the conflict between positive and negative evidence and IG from the missing information in the fused evidence. As shown in Figure П CF primarily highlights concrete objects (c.g.. diaper). whereas IG captures both objects and modifiers (e.g., vaguely). Tokens corresponding to the final decision exhibit high CF and IG values, indicating that these measures effectively capture the sources of uncertainty. We evaluate our method on misbehavior detection across four scenarios, encompassing hallucinations, jailbreaks, adversarial vulnerabilities, and OOD failures. Comprehensive experiments DeepSeek- VL2 FL2-Tiny (Wu et al. 2024b), Qwen2.5-VL- (Bai et al. 2025). IntermVL2.5--8 VL2.5-8B (Chen et al. 2024), and Models Models-7B 7B (Tong et al. 2024) show that CF and IG consistently outperform strong
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baselines, achieving relative improvements of 10.4% AUROC and 5.3%/5.55% AUPR. Empirical analysis further reveals that hallucinations correspond to high internal conflict, whereas OOD failures correspond to high ignorance. Our contributions are summarized as follows:
We identify that diverse misbehaviors in LVLMs primarily stem from two types of epistemic uncertainty: internal contradictions and missing supporting information. To address this, propose a computationally efficient detection method based on Dempster-Shant Shafer Theory (DST). captures these fine-grained uncertainties in a single forward pass.
We conduct a layer-wise dynamic analysis that offers a novel perspective for interpreting the evolution of internal representations in LVLMs. This analysis also enables certain layers to distinguish among all four misbehavior categories.
Extensive experiments on four advanced L.V.Ms using our proposed Mishehavior- demon- strate that the method consistently outperforms strong baselines. It yields improvements of 10.4% in AUROC and 5.3%/5.5% AUPR.
2
RELATED WORK
In this section, we first review four typical categories of misbehaviors observed in LVLMs (Sec- tion P. and then discuss UQ methods that can leveraged for detection (Section 2.2
2.1 MISBEHAVIORS IN LVLMS
This section provides an overview of key misbehaviors observed in LVLMs, including hallucinations, jailbreaks, adversarial vulnerabilities, and failures caused by OOD inputs.
Hallucination in LVLMs denotes mismatches between visual inputs and generated text (Liu et al. 2024b). It can be categorized into three types: object hallucination, describing nonexistent ob- jects (Biten et al., 2022] Hu et al. [i et al. 2023b); relation hallucination, misrepresenting spatial or semantic relations (Wu et al. 2024a); attribute ballucination, assigning wrong properties visual entities (Liu et al. 2024a). Jailbreak refers to eliciting harmful behaviors misaligned with human intent, often triggered by visual perturbations (Carlini al. 2023), exposing vulnerabilities beyond methods, which iteratively modify inputs via gradients or search strategies (Qi et al. 2024 Wang et al.) 2024b) Bailey et al. (2244). and generation bbsed methods, which embed harmful typography on the clean images (Gong al. 2025 Li et al. 2024a Goh et al. 2021; Shayegani et al.). Adversarial vulner- ability in vision models stems from imperceptible adversarial perturbations that induce incorrect
typical prediction errors. Such attacks are broadly categorized into optimization-base
predictions (Szegedy et al. 2014 Li et al. 2023a Fang et al. 2024 Ge et al. 20233 Qin et al. 2025).
Recent work shows that LVLMs inherit this weakness (Sheng et al. 2021 1j Zhao et al. 2023
Wang
ct al. 2024a). remaining susceptible to visual perturbations despite their multimodal nature. OOD
failure refers to the inability of model to handle inputs outside the training distribution, challenging
accurate recognition (Kim et al. 2025 Han et af.)). Prior work has focused on multimodal models,
like CLIP (Radford et al.] 2021 1 , for detecting inputs outside the in-distribution (ID) (Ming et al.
2022, Jiang et al.) Cao et al. 2024). Although OOD in LVLMs is less studied, recent work defines ID
inputs standard data and OOD inputs as style or quality shifts (Kim al. 2025 Xu et al. 2025).
In summary, LVLMs are prone to exhibiting various misbehaviors, clearly highlighting the critical necessity of effective detection methods to ensure their reliability and robustness.
2.2 UNCERTAINTY QUANTIFICATION FOR LVLMS
Classical UQ methods, such as Bayesian approaches (MacKay 1992 Blundell et al. 2015) and their variants (Gal & Ghahramani] 2016 Lakshminarayanan et al. 2017 Maddox et al., 2019) are computationally expensive and thus difficult to apply to LVLMs. Deterministic methods, such as (Malinin & Gales 2019) and (Sensoy et al. 2018 Li et al. 2025). the latter following evidential framework, still require model training. In contrast, our approach performs evidence modeling and aggregation inference, producing richer uncertainty measures without additional training, making it well suited for LVLMs. Thus, this section reviews prior work on UQ for LVLMs.
Token-wise probability-base -bsed methods estimate uncertainty within a single generation using log- likelihoods (Kadavath et al. 2022 Guerreiro et al. 2023 Duan et al. 2024) and entropy measures (Ma-
www.aviiivvvvoocmmmmmmmmmoaa cervvirr-ae Rench

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a) Output Generation from LVLM
Output Head
Projection Loyer
Decoder
Softmax
token
Vision-Longugge
Fecture
z
Logits H
b) Evidence Uncertainty Quantification (EUQ)
Dempster's Rule of Combination
Detect
CF
liles
BB
max
Adversorial
Jailbrokk
IG Detest
Pre-logits Feature
ma E)
OOD
Hollucination
The overall framework the proposed method applies basic belief assignment pre-logits feature to obtain evidence weights. These weights are then decomposed into positive and negative components, which are fused to estimate the final uncertainties that can detect different types of misbchaviors, respectivoly,
Figure 2:
linin & Gales 2021). However, softmax outputs tend to be overconfident (Gal & Chahramani 2016 Guo et al. 2017). resulting in miscalibrated uncertainty. Sampling- based methods further estimate uncertainty by evaluating variability semanties across multiple generations. (Lin et al. 2023) esti-
mates uncertainty via pairwise similarities and graph Laplacian. (Farquhar et al. (2024) proposes semantic entropy detect confabulations, utilizing external models to evaluate semantic equivalence. Other works (Raj et al., 2023 Manakul et al.) 2023) design task-specific prompts and use auxiliary LLMs to assess semantic consistency. Regardless, these methods are computationally expensive due to repeated inference and heavily depend on auxiliary models. Verbal elicitation approaches, completely independent of output probabilities. estimate a model's uncertainty by prompting it to express self seffaaseessmets in natural language. (Lin et al.) introduces verbalization probability and demonstrates alignment with model logits after fine-tuning. Subsequent studies (Tian et al., [2023] Zhou et al.) 2023 Xiong et al. (024)) focus on prompting strategies, such as employing Chain-of- Thought (CoT) (Kojima et al. 2022) to improve verbalized uncertainty, which depends heavily on the model's compliance with prompts (Kapoor et al. 2024).
Prior methods are often less effective at capturing the patterns of misbehaviors. contrast, our approach leverages LVLM output head features, capturing conflict (internal contradictions) and ignorance (lack reliable information), which enables differentiation among misbehavior types.
3
EVIDENTIAL UNCERTAINTY QUANTIFICATION
This section first introduces pre-logits features in the LVLM output head and the basics of Dempster- Shafer Theory (Section 3.1. Next, these features are then interpreted as evidence for belief assignment (Section 3.2 and used to quantify conflict and ignorance via evidence fusion (Section 3.3
3.1
PRELIMINARY
LVLM Output Head LVLMss typically employ LLM with decoder architecture, along with output head that generally includes projection layer and softmax for predicting the next token, as shown in Figure aa)) The linear projection layer serves as the decision layer of LVLMs, encoding
cross-modal information critical for decision making (Bi et al.) 2024 Montavon et al. 2017 Zhao al. 2024p. This layer contains features directly mapped human- reanable tokens, motivating the use of the output head for uncertainty quantification. We denote the pre-logits features by
= (ZI,..., zr) E Rd and the output of the projection layer by
(hu,..., hy) E IR., where
Z is interpreted as evidence (Tong et al., 2021 Manchingal et al.) 2025) for estimating uncertainty. Consequently, the projection layer shown Figure 2 a) can be formalized as:
H ZW + b.
(1)
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where W RXx , b R denotes the weights and biases for the linear transformations, respectively.
Dempster-Shafer Theory The Dempster- Theory (DST), also known as Evidence Theory, extends classical probability theory by providing more flexible framework for representing and combining uncertainty derived from evidence (Dempster] 1967 Shafer 1976) (details and illustrative examples are provided Appendix A.2)) ...2. Given a frame of discernment H. defined finite set of mutually exclusive and exhaustive hypotheses, a mass function (also called a basic belief assignment, BBA) mn()) assigns belief to all subsets of H. Formally,
defined as:
m 2 - [0, 1],
m(S) = = m(0) = 0,
(2)
SCH
where S is any subset of H. and 0 represents the empty set. Subsets with nonzero mass are called focal sets. mass function is simple it assigns nonzero mass exactly two focal sets:
((S) = s; ( = s: m(0) = 0.
(3)
DST also introduces Dempster's rule (Shafer 1976) for combining two mass functions my and m2, enabling multi-source evidence fusion. The rule is given by:
(m @ m2) (S)
Σ S,ns.=S
mi (i
==
Σ
(S)))222(S2))
S₁ -
where (m1 @ m2) = 0, S1,1, S H. and R. denotes the degree of conffict between mi and m2.
3.2 BELIEF ASSIGNMENT
Due to the key role of the pre-logits feature Z in model predictions, treat it as evidence for BBA. This evidence enables quantifying two primary evidential uncertainties: conflict (CF) and ignorance (IG). This perspective builds the framework of (Denceux 2019), which shows that the linear transformation can be viewed as evidence fusion of its input features via Dempster's s rule. In the remainder of this paper, we present the EUQ process based on Z, illustrated in Figure
Each component z; of Z may support or contradict a candidate output feature hy- For each pair (z,, hy), define mass function my associated with an evidence weight Ety, which quantifies the degree of support that provides to the validity of the feature hy. We model the relationship between the input features and the corresponding evidence weights using an element-wise affine transformation:
(5) where E e Rxx is the matrix of evidence weights {ety]- The parameters A, E [xJ are obtained via closed-form estimation, as demonstrated in Lemma [ and represent the influence of each input feature 2; on the output feature hy. We further decompose into its positive and negative parts: E+ max(0, E); E max(0. E), with entries {ett) and {eğ], respectively. These indicate support for hy and its complement (hy}. Accordingly, we define positive and negative simple mass functions for each pair (2,, h2) as:
E A
о
Z
+ +B,
myt ((hg)) = - exp(-Erg) my
ma ((n)) = exp(-ex)
(6)
Next, we apply the Least Commitment Principle (LCP) (Smets) 1993). conservative strategy for BBA that assigns support only to options directly justified by the available evidence. To estimate better-calibrated weights of evidence matrix, we design the following objective under the LCP:
min
A ② Z + B
s.t. B
where 1 denotes the all-ones vector and b is the bias term of the projection layer. This constraint prevents trivial solutions and ensures equal treatment across feature dimensions.
Lemma 1 (Optimal Belief Assignment). Given input features Z E I and linear transformation with weights E I and corresponding bias E IR. , the belief assignment parameters under the Least Commitment Principle (LCP) admit the following optimal closed-form. solution:
A W PWW)), B'= (A* 11-111A1 (A')) ZT.
(8)
where vo(:) and (.).)) compute the mean along the first and second dimensions, respectively. Here, A and B" denote the optimal belief assignment parameters that minimize the commitment.
The optimality of this solution allows for a precise quantification of evidence weight, which is essential for subsequent uncertainty estimation. For full details, please refer to the AppendixA.4
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3.3 UNCERTAINTY ESTIMATION
We introduce the additivity of evidence weights (Lemma? 2 Appendix A.5):
for two simple mass functions mi (-) and m2(+), with associated evidence weights e and e2 respectively, if they share the same focal sets S C H. the m. @ m-(() reduces to é1 + c2. Formally, first-stage fusion yields:
m m1 m2(H); m(S) = 1 m(H); e 62,
(9)
where the e the evidence weight of my @ m²(-). As consequence, mass functions sharing the same focal sets can be directly combined, thereby alleviating the overhead of power set computation DST (Voorbraak] 1989). This property yields the following mass functions:
m3 (thg)) = 1 - expl (epp--e))- = exp(- Σ
(10)
my ([h,)) exp(-e,)
exp( Σ cTj)
Here, CF quantifies the conflict between the combined positive and negative evidence, while IG reflects the overall ignorance by aggregating all my (H). Following the definitions of degree of conflict and ignorance in DST, these quantities are expressed as:
CF Σ m m+(S)) 100mmm(2 (S2), IG = Σ my (H),
(11)
SynSz=
where m = + m. and m + my denote the combined positive and negative evidence from the second-stage fusion. Importantly, Eq. equation alloww computing CF and IG without enumerating the full power set of H. avoiding the usual combinatorial explosion in DST.
Theorem 1 (Evidential Conflict and Ignorance within LVLMs). Let Z = {=i)<= denote the pre-
logits feature of LVLMs, and let m be the mass function expressing the support that z provides for output feature h H. and H. is the frame of discernment. The conftict CF and ignorance IG are determined by the inconsistency and insufficiency among mass functions {mij]. Specifically,
CF Σ, Ti 77
IG IG=E, expl -e);
n3 exp(e,) -1
exp(-e,)
(12)
Ti = - -xpp exp(-ee7)
-J+1
where n and ni denote the support and opposition ratios for component hj, respectively. Their product measures the local conflict, and the aggregated opposition determines the overall ignorance.
Theorem shows (proof in AppendixA.6),, when both n and nj are simultaneously high for the same hy, their product becomes large, indicating strong internal contradiction CF. The IG increases as the negative evidence weights ej decrease, indicating higher uncertainty due to a lack of reliable information. LVLMs generate responses token by token, each with an evidential uncertainty value. We quantify the sentence-level uncertainty by averaging these values across all tokens.
4 LAYER- WISE EVIDENTIAL UNCERTAINTY DYNAMICS
This section first presents the experimental setup in Section 4.1 evidential uncertainty dynamics LVLMs. First, we examine aiserwwise dynamics to analyze how
followed by an investigation of
uncertainty evolves across linear layers during inference in Section 4.2 Second, we leverage the layer-wise analysis to differentiate between various misbehaviors in Section 4.3
4.1
EXPERIMENTAL SETTINGS
Table 1: Overview of datasets and evaluation types.
This section summarize experimental setup. Detailed version is provided the AppendixA..
Scenarios
Methods
Size
Question Type
Hallucination
Li
2023b
1000
Multiple-choice
Hallucination
Wu af
202-a
1000
Multiple-choice
Datasets We evaluate our method and baselines hallucination scenarios using POPE (Li et al. 2023b) and R- R-Bench (Wu) et al. 2024a), focusing object and re- lation hallucinations. For jailbreak sce- narios, evaluate range of jailbreak
Jailbreak
[Gong ct al. 2025
200
Open-ended
Jailbreak
[L
20246)
200
Open-ended
Jailbreak
[Q] at. (2024)
20022))
600
Open-ended
Jailbrcak
(Goh er at |
2022
1800
Multiple-choice
Adversarial
Fang ot al
(2024)
200
Yes-and-N -od-No
Adversarial
ar.
20231
Yes-and-Ne
OOD
eu
2025
1300
Yes-and-No

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Hallucination
Jailbreak
Adversarial
ODD
0.60
200
Ignorance
Conflict
150
0.40
100
0.20
50
0.00
0
16
24
31
8
16
24
31
Decoder Layers
Decoder Layers
Figure 3: Layer-wise changes of evidential uncertainty and analysis of conflict vs. ignorance across four dataset types using Intern. Results for other models are provided in Appendix A.8
Hallucination
Jailbreak
Adv
Ood
Others
Density
Density
Density
Density
Density
Density
CF(ours)
IG(ours)
PE
CF(ours)
IG(ours)
PE
Density
Density
Density
Density
Density
Density
CF(ours)
IG(ours)
PE
CF(ours)
IG(ours)
PE
Figure 4: Density distributions of CF, IG, and entropy for each type of misbehavior in Intern, paring the target misbehavior against others. Results for other models are provided in Appendix A.8
attacks, including FigStep (Gong et al. 2025), Hades et al. 2024b), and VisualAdy (Qi et al.
2024). We further simulate typographic attacks following the protocol of (Goh et al., 2021). For adversarial scenarios, we employ two state-of-the-art attacks: ANDA (Fang et al. (024) and PGN (Ge the dataset from (Xu al. 2025). The aforementioned
al. 2023). For OOD failures, scenarios and data sources constitute our Misbehavior-Bema All tasks include a valid, correct answer, and all provided options are reasonable.
Models We evaluate four diverse LVLMs: DeepSeek-VVL VL2 Tiny (Wu et al 2024b). Qwen2.5-V VL
7B (Bai al. (202)). InternVL2.5-8B (Chen et 2024), and MoF-Models- 7B (Tong et al. (2024). These models employ varied architectures, inc lucluing SwiGLU (Shazeer) (020) and Moł: (Jacobs)
al. 199T). We focus on smaller models for efficiency, with scale effects analyzed in Section 5.2
Baselines We compare against four baselines: two sampling-based methods: self-consistency meth- ods predictivv entropy (PE) (Kadavath et al. 2022) and its length-normalized variant (LN-PE) (Ma) linin Gales 202T). Additionally, we include HiddenDetect (Jian et al. 2025), originally designed for jailbreak detection, which relies refusal cues and can also be applied to other misbehaviors.
(SC) (Wang et al.),, semantic entropy (SE) (Farquhar et al.) 2024); and two probability-
Correctness Assessment For multiple-choice and yes/no tasks, correctness assessed using ROUGE-L (Ln 2004) (threshold > 0.5). For open-ended tasks in jailbreak contexts,
Harm-
Bench's s official classifier" to evaluate response correctness.
Evaluation Metric for Detection We use the Area Under the ROC Curve (AUROC) to evaluate detection performance, measuring the ability to rank correct (low uncertainty) above incorrect genera- tions. The Area Under the Precision-Recce Curve (AUPR) (Davis & Goadrich 2006) is also reported to address data imbalance from rare misbehavior cases.
Hyper-parameters For the sampling- based methods, SC and SE, generate exactly 10 responses per question. The temperature is set to 0.1 for the first sample. The remaining samples are drawn at 1.0 to ensure diverse generations. All experiments conducted in NVIDIA H800 PCIe GPUs.
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Table 2: Accuracy results of DeepSeek, Qwen, Intern, and MoF across the four misbehavior types. Best and next-best results are marked in bold and underlined, respectively.
Table 3: Average AUROC and AUPR
all methods across LVLMs and datasets.
Method
AUROC
AUPR
Misbchaviors
Models->
DeepSeek
Qwen
Intern
MoF
Average
SC
0.626
0.730
SE PE
0.624
0.661
Hallucination
0.710
0.732
0.688
0.456
0.647
0.701
0.656
Jailbreak
0.895
0.893
0.718
0.567
0.768
LN-PE
0.704
0.660
HiddenDetect
0.707
0.658
Adversarial
0.208
0.503
0.607
0.236
0.389
(ours)
0.812
0.783
OOD
0.346
0.272
0.456
0.731
0,451
IG (ours)
0.783
0.785
4.2
LAYER-WEL WISE DYNAMICS OF CONFLICT AND IGNORANCE
EUQ enables uncertainty quantification at every linear layer of decoder blocks, allowing us to investigate the evolving trends of CF and IG across the entire decoder.
Observation Across decoder layers, ignorance tends to decrease while conflict increases.
Our layer-wise analysis (shown in Figure reveals consistent trend across four misbehavior datasets: (1) IG decreases as deeper layers accumulate more supporting cues, echoing the findings in (Huo al. 2024) showing that the number of domain-specific neurons diminishes with depth; (2)CF increases as evidential support becomes increasingly polarized across features. These dynamics align with the coammatioo-ootlleeee perspective (Shwartz- Ziiv Tishby 2017). whereby deeper representations compress redundant imput while enhancing task-relevant discriminative information As result, stronger task relevance drives different feature channels toward competing hypotheses, therehy amplifying conflict.
4.3 DISTINGUISHING MISBEHAVIORS VIA EVIDENTIAL UNCERTAINTY
From Figure 3 certain decoder layers exhibit clear distinctions in their uncertainty curves across misbehaviors. Motivated by this, leverage layer-wise CF and IG distinguish different misbe- haviors. We conducted one-vs-rest density comparisons, where each misbehavior is contrasted with the others under these three metrics. Figure presents the resulting distributions of CF, IG, and entropy across the four misbehavior types. A clear pattern emerges: CF and IG produce clearer separations between distributions than PE, highlighting their discriminative ability. Among the four types, adversarial examples are the most distinguishable, their density curves deviate sharply from the others due to the pronounced distributional shift caused by pixel-level perturbations. These results provide further empirical evidence that epistemic uncertainty, arising from conflict and information gaps, though currently the separation is apparent only certain decoder layers.
5
DETECTION PERFORMANCE ANALYSIS
5.1 MISBEHAVIORS DETECTION
Before evaluating misbehavior detection, we report the accuracy of four LVLMs across the prepared datasets. As shown in Table 2 adversarial examples yield the lowest accuracy, followed by OOD inputs. Jailbreak samples show the highest accuracy except on MMBench, likely due to LVLMs recognizing and refusing most jailbreak prompts. We begin by evaluating our method and baseline approaches across four distinct data types that elicit varied misbehaviors: hallucinated data, jail- break attacks, adversarial examples, and OOD inputs. As shown in Table CF/IG outperforms the best baseline by 10.4%/7.554 AUROC and 5.3%/5.5% AUPR on average across all models and misbchavior types. As shown Table [2] CF consistently achieves superior detection perfor- mance for hallucinations, with average AUROC and AUPR scores of 0.761 and 0.824, respectively, outperforming all baselines.
Observation 2. Hallucinations more easily detected by conftict (CF), whereas O0D failures are more effectively captured by ignorance (IG).
Moreover, for jailbreak and adversarial examples, CF and IG achieve comparable results. These observations suggest that hallucinations are more likely caused by internal conflicts within the
'Represented by mutual information of features and lahels T(T;; Y)
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Table 4: The AUROC and AUPR of our methods and baselines on DeepSeek- VL2 (DeepSeek), Qwen2.5 (Qwen), Intern VL2.5 (Intern), and MoF-Models (MoF) in adversarial, OOD, hallucination, and jailbreak settings. Best and bext-best results are marked in bold and underlined, respectively.
Models=s
DeepSeek
Qwen
Intern
MoF
Average
Method
AUROC
AUPR
AUROC
AUPR
AUROC
AUPR
AUROC
AUPR
AUROC
AUPR
Hallucination datas
202
SC
0.660
0.734
0.640
0.815
0.696
0.883
0.500
0.758
0.624
0.798
SE
0.517
0.649
0.501
0.554
0.775
0.582
0.722
0.510
0.629
0.574
PE
0.771
0,574
0.742
0.741
0.755
0.634
0,701
0.619
0.742
0.642
Deteen
0.594
0,528
0.792
0.570
0.590
0.523
0.827
0.845
0.703
0.614
C.(ours)
0.774
0.781
0.802
0.835
0.611
0.843
0.855
0.838
0.761
0.824
[Gtours)
0.716
0.533
0.591
0.745
0.768
0.898
0.553
0.757
0.657
0.733
Jailbreak attacks from (Gong al.
2025
Li
20244b
20224.
and (Gob
2021
SC
0.606
0.606
0.512
0.861
0.546
0.781
0.920
0.846
0,646
0.774
SE
0.643
0.746
0.537
0.790
0.623
0.881
0,869
0.532
0.668
0.737
PE
0.564
0.633
0.757
0.890
0,716
0.731
0.852
0.503
0.722
0.689
0.703
0.698
Detden
D. 0.842
0,746
0.842
0.802
0.623
0.543
0,699
0.586
0.752
0.669
CFoours)
0.844
0.791
0.535
0.748
0.739
0.886
0.534
0.757
[Gtours)
0.673
0.795
0.541
0.749
0.585
0.711
0.859
0.860
0.665
0.779
Adversarial examples from
2024
and
(Ge
20233
SC
0.739
0.633
0.660
0.746
0.729
0.593
0.778
0.650
SE
0.669
0.838
0.688
0.514
0.634
0.557
0.552
0.707
0.636
0.654
PE
0.621
0.604
0.701
0,518
0,701
0.524
0.674
0.587
0.674
0.558
LN-PE
0.792
DeteennDetect
0.646
0.532
0.802
0.737
0.672
0.637
0.591
0.887
0.678
0.698
CF(our))
0.921
0.773
0.836
[G(ours) 0.976
0.787
0.767
0,713
0,702
0.774
0,999
0.856
0.861
0.783
OOD inputs from (Nu al.
2025)
SC
0.557
0.663
0.514
0.774
0.585
SE
0.526
0.622
0.592
0.698
0.622
0.659
0.505
0.736
0.561
0.679
PE
0.690
0.794
0.779
0.896
0.564
0.623
0.630
0.620
0.666
0.733
LN-PE
0.689
0.786
0.885
0.612
0.630
0.667
0.728
HiddennDetect
0.677
0.670
0.776
0.534
0.729
0.621
0.594
0.778
0.694
0.651
CF(our))
0.809
IG(ours) 0.999
0.963
0.997
0.701
0.795
0.866
0.999
0.855
0.948
0.846
model, whereas OOD failures primarily arise from lack of relevant information. Importantly, while HiddenDDetcct was designed for jailbreak detection, CF marginally outperforms it on that task and exhibits larger advantage other detection tasks. Specifically, CF improves HeddenDetect by 0.5% in AUROC and 0.4% in AUPR. Our CF, IG, and entropy-based methods outperform sampling-bass approaches. suggesting that relying solely on output consistency insufficient to capture the model's internal cognitive issues in misbehaviors. Moreover, our approach attains competitive performance with substantially lower computational overhead, highlighting its efficiency and practicality.
5.2 ABLATION STUDY
We perform ablation studies to examine the ef- feet of model scale and feature layers on our method. We also conduct an efficiency analysis to measure computation and inference latency.
1.00
10
1.0
1.00
AUROC
AUROC
0.0
0.8
AUPR
0.5
0.85
0.00
0.80
Temperature We examine the effect of tem- perature on LVLM generation (Figure 5 left), evaluating eight settings from 0.1 1.4. Both CF and IG remain stable, suggesting robust- ness of our method to this hyperparameter.
0.75
0.6
0.6
0.75
01020440s0a
1.34
250 380
Temperature
Model Scale
Figure 5: Ablation study on temperature (left) and model scale (right) across all datasets using Intern.
Model Size The right panel of Figure5]com pares models with 4B, 8B, 26B, and 38B param- eters to illustrate the effect of scale. Detection performance is strong for the 4B and 38B mod- els. Small models produce obvious emrors that easily captured, medium models generate
Table
rates (%) of two LVLMs in the hallucination scenario.
Model
NoA (option)
NoA (prompted)
Qwen Intern
0.27
4.93
0.00
0.53

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Published as a conference paper at ICLR 2026
Table Comparison of AUROC and average runtime per example across Intern.
Method Model Inference
SC
SE
PE
LN-PE
HeddenDetect
CF
IG
Time (8) AUROC
9.6x10
x 10
9.0x 10
3.1x10-8
6.1x10-6
2.0x10-2
9.1x 10x
x.5 10
0.626
0.624
0.701
0.704
0.707
0.812
0.783
subtler, less detectable errors, and large models produce mostly correct outputs, making the remaining misbehaviors easier to detect. Additionally, to verify that our method can still capture Observations T and on larger-scale model (e.g., 72B), we conducted experiments and report the results in Appendix A.8
Prompting for Abstention We tested external prompting by adding a "None of the above" option (Wang & Nalisnick| 2025). As shown in Table the models rarely selected it. Qwen chose it only 0.27% of the time, and Intern 0.00%. Even after reinforcing the instruction with If you are unsure, please select "None of the above".. ., the selection rates remained extremely low, with Qwen choosing it 4.39% of the time and Intern 0.53%, indicating persistent overconfidence in multiple-choice scenarios. This shows that prompt-base based strategies have limited ability to elicit uncertainty, underscoring the need for methods that do not rely on prompting.
runtime and AUROC. While model inference requires only 9.6x10 2s, UQ via sampling methods incurs 10x overhead, making it prohibitive for real-time appli- cations. Entropy methods are faster but less accurate. While HiddenDetect avoids multiple sampling, its reliance on hidden states from the most safety-aware layers still incurs a computational overhead of 2.0 x 102s. In contrast, our approach using CF and IG achieves the best efficiency-accuracy trade-off, requiring only single forward pass and no access to specialized layers or auxiliary models.
Efficient Analysis
DISCUSSION
Relation with EDL-based methods Existing applications of evidence theory in LVLMS (e.g.. LLi
ct al. 2025 Ma et al., 2 are typically built on evidential deep learning (EDL) (Sensoy et al. 2018). which follows a paradigm fundamentally different from ours. These approaches are grounded in Subjective Logic (SL) and require explicit training or fine-tuning, which limits scalability to lcrge-scale models. In contrast, EUQ focuses on detecting misbchavior without additional training and directly leverages the full expressive form of DST rather than the SL. formulation. We hope this work broadens the perspective on DST-based methods and highlights an alternative evidential direction for deep learning models and LVLMs. Further details are given in Appendix A.8.5
Scope and Applicability Our method interprets linear transformations as evidence fusion operators, which allows EUQ apply any model with a linear projector, including architectures such as BERT, ResNet, and LLMs. This generality extends beyond VLMs, and Appendix [A.8.3] provides toy example on convolutional networks to illustrate this. While requiring access to internal representations limits its use with closed-source APIs like GPT-4 (Achiam et al., (023). it provides valuable fine-gras sreeegrrindd signals for failure diagnosis and model improvement.
CONCLUSION
In this work, we categorize the typical misbehaviors of LVLMs, including hallucinations, jailbreaks, adversarial vulnerabilities, and OOD failures. To detect and distinguish these misbehaviors, we introduce Evidential Uncertainty Quantification (EUQ), the first attempt to explicitly characterize two types of epistemic uncertainty in LVLMs. Furthermore, EUQ can be leveraged to interpret the internal evolution of the model decoder: ignorance generally decreases while conflict increases. Additionally, hallucination cases are primarily characterized by high internal conflict, whereas OOD failures mainly result from a lack of information. Experiments on four LVLMs show that EUQ consistently improves AUROC and AUPR, suggesting evidential reasoning as a promising direction for fine- grained uncertainty quantification, model interpretation, and misbehavior identification.
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ACKNOWLEDGMENTS
This work was supported by the National Key Research and Development Program (Grant No. F02409E0202200), the National Natural Science Foundation of China (Grant Nos. 62436001, 62536001, and 62406021), the Joint Foundation of Ministry of Education for Innovation team (Grant No. 809 80942042,3 and the Fundamental Research Funds for the Central Universities (Grant No. 2025/BMC040).
ETHICS STATEMENT
This work studies mishehavior detection LVLMs, including behaviors that may generate harmful content. Our experiments are controlled and do not involve real users. The goal is to improve model safety and reliability, mitigating potential harm from such behaviors.
REPRODUCIBILITY STATEMENT
All methods, models (with version numbers), datasets, and experimental settings are fully described to ensure reproducibility. This includes the implementation of our approach, hyperparameters, evaluation metrics, and baseline comparisons.
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A APPENDIX
A.1 OVERVIEW
The Appendix provides supplementary material to support and extend the main content of the paper. We begin in Subsection A.2 with the theoretical foundation of Dempster-Shand -haterrr Theory, which forms the basis of our approach. Subsections A.4 to A.6 present complete proofs for Lemma 1, Lemma 2, and Theorem I, respectively. Subsection A.7 details the experimental configurations, while Subsection A.8 offers additional results that further validate our method. Finally, Subsection A.9 outlines our usage of large language models in this work.
A.2 DEMPSTER- -SHAER THEORY FOUNDATION
The Dempster-Shafes Theory (DST), proposed by Dempster (Dempster [967] and Shafer (Shafer 1976,, generalizes classical probability theory to manage uncertainty and partial belief. It employs basic belief assignments (BBA) that distribute belief among subsets of the frame of discernment. This allows for fine- -eningraiedd representation of uncertainty compared to the traditional probability, which assigns specific probabilities to individual events (i.e., elements within the frame). This theory can fuse evidence from different sources using Dempster's 's rule of combination. Below, we recall the key definitions employed throughout the main paper.
Mass Function Let H. = {hi h2,..., h represent the frame of discernment, which contains J possible outcomes. In this context, a mass function m(-) maps subsets of the frame of discernment 2 to the interval [0, 1]. indicating the degree of belief assigned to each subset. The mass function is subject to the normalization condition,
Σ mS)) =
m0) = 0,
(13)
SCH
where S is any subset of H. and represents the empty set.
Focal Set For a subset S S H. if mSS) > 0, S is called foccall set of m(-).
Simple Mass Function Specifically, mass function is called simple when it assigns belief exclu- sively to one specific subset S C H and the H. Formally, it is defined as follows:
m(S)= 8; m(H) = 1 - 8,
(14)
where S + @ and E [0, 1] represents the degree of support for A. In particular, the mass m(H). assigned to the entire frame, commonly indicates the degree of ignorance, as it exhibits no preferential allocation towards any particular subset.
Dempster's Rule of Combination Given two mass functions, m)(-) and mz(-), which represent evidence from two different sources (e.g., agents), the combined mass function for all S C H, with + 0. is computed through Dempster's 's rule of combination (Shafer 1976) as follows:
(m, + m2) (S) =
Σ S
my (S, (S,)::))));
K =
Σ mi (S,))mm2SS))
(15)
Sins==
where (mi 1 m2) (0) = 0, and the x serves as an important metric to measure the degree of conflict between mi (.) and m²(-)-
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Belief and Plausibility Functions Given mass m(-), two useful functions, the belief and plausi- bility functions, are defined, respectively, as
(il
Σ m(S); S2CS
P((S))
E m(S2).
(16)
S2nS,
The belief function (e((S)) represents the degree of certainty that the true state lies within the subset Si based on all available evidence, excluding any possibility outside of Si. In contrast, the plausibility function P((S)) indicates the degree of belief that the true state may lie within S1. without ruling out possibilities.
Contour Function In the case of singletons (only one element in subset, c.g., H, the plausi- bility function P((.) is restricted to the contour function pl(-) (i..., pl(h,) = P(((h)]) Vhş E H). The contour function pl(,,) measures the plausibility of each singleton hypothesis and assesses the uncertainty of each possible outcome independently. Furthermore, given two contour functions pl1 and pl2(:), associated with mass functions m, (-) and m²(-) respectively, they can be combined as
pl 2 pl2(h)) = 1 ph (1))p)((h)) Vhy E H.
(17)
This combination rule simplifies evidence aggregation by directly multiplying the plausibilities of singletons, making the process more efficient.
A.3 ILLUSTRATIVE EXAMPLE
To build intuition for DST, we present simple illustrative example. Consider light-bulb switch whose state is either On or Off, giving the hypothesis space (On, Off]. In classical probability, probabilities are assigned only to the individual states:
P(On) + P(Off) =
If P(On) P(Off) 0.5, the model cannot tell whether this means that the bulb is truly equally likely to be On or Off. or simply that we do not know its state. DST addresses this limitation by introducing a Basic Belief Assignment (BBA): mn(-) defined over the power set 2H,.
m(S) 1. m m(0) 0.
SCH
Here, S may be single state (e.g., {On}) the full set H On, Off. This allows for 2 - = 3 distinct mass assignments, enabling richer uncertainty representation. Importantly, the belief assigned to the full set directly quantifies ignorance, i.e., how much uncertainty have about whether the bulb is On or Off.
Conflict and ignorance from three observers Consider again the frame H {On, Off]. Three independent observers provide BBAs describing the state of the light bulb:
mi ([On)) = 0.3. mi((Off)) 0.2,
mi (H) = 0.5,
m2([On)) 0.6, m2([Off)) 0.2,
m2(H) 0.2,
ma([On)) = 0.1, ma((Off))) = =0.8,
ma (H) = 0.1.
The belief each observer assigns to the full set H. quantifies ignorance:
1G1 m (?)) = 0.5, IG2 = m2 (H) 0.2, 1G3 mm (H) = 0.1.
To combine two sources of evidence, DST uses Dempster's rule of combination, formally expressed as:
= Σ m2(C),
(18)
BnC=#
where the sum is over all pairs of mutually exclusive subsets B and C. Here, K quantifies the conflict between the two BBAs.
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For this example:
12 = mi ({On)) m=((Off)) + (=((O)))m2((0n)
0.3 0.2 0.2 0.6 0.18, K2 m2 (On) mm (Off)) + m2 +m=((Of))mm(((0))
0.6 0.8 - 0.2 0.1 0.5.
As shown, K23 K22, indicating that m2 and mg exhibit stronger disagreement than my and ma, resulting in a higher conflict value.
A.4 PROOF OF LEMMA 1
Preliminary LVLMs typically use LLM with a decoder architecture to predict the next token conditioned on vision-language features. To avoid overconfidence (Jiang et al. (0024) and achieve more precise uncertainty quantification, we focus on pre-logits features from the LVLM. These
features mainly represent rich vision- vision-language language perceptual information (Basu et al. play key role in decision making of LVLMs (Montavon al. 2017 Zhao al.
Bi et al. 2024) and
2024). Specifically, in an linear projector layer, We denote the pre-logits features by 2 (Zi,.... z1 E Rd and the output of the projection layer by H (h1,...,,hy, et al. 2021 Manchingal et al., 2025) for estimating uncertainty. Consequently, the projection layer shown in Figure a) can be formalized as:
TR where Z is interpreted as evidence (Tong
- ZW + b.
(19)
where W [R b IR denotes the weights and biases for the linear transformations, respectively,
Due to the key role of the pre-logits feature Z in model decisions, we treat it as evidence for belief assignment. This evidence enables quantifying two primary evidential uncertainties: conflict (CF) and ignorance (IG). This perspective is grounded in the theoretical framework of (Denceux 2019). which demonstrates that the output an FFN can be interpreted as the combination of simple mass functions derived from its input features via Dempster's rule of combination. In the remainder of this paper, we detail the EUQ process based on the FFN feature Z.
Each component 2; of Z may support or contradict candidate output feature hy- For each pair (z,, hj), we define mass function mij associated with an evidence weight e:j. which quantifies the degree of support that 2, provides to the validity of the feature hy. We model the relationship between the input features and the corresponding evidence weights using an affine transformation:
E A G Z + B,
(20)
where E R is the matrix of evidence weights, and A, A IXJ are parameter matrices.
To ensure that belief is only assigned when sufficiently supported by evidence, we adopt the Least Commitment Principle (L.PP) (Smets 1993), which minimizes unwarranted assumptions. Under this principle, the optimal evidence weights are obtained by solving the following regularized optimization problem:
min A,B
A Z
+
+ B|3
subject to 1 1 = b-1,
(21)
where 1 denotes the all-ones vector, and is the bias in the linear transformation that regulates the global evidence level across hypotheses. This constraint enforces cautious belief assignment under the LCP.
Proof Outline. We outline the main steps follows:
Step 1: Reformulate the optimization objective in terms of scalar parameters a:y. Pij. and center the input žnz to simplify the expressions.
Step 2:
Rewrite the loss function using centered variables to eliminate cross terms and reduce it to a sum of squares in ai and shifted Byy-
Step 3: Solve for the optimal at, under a constraint that ensures the sum of Biy matches the bias term by-
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Step 4: Recover P by adjusting for centering and express the final solution in closed matrix form for A and B"
Proof. We begin by rewriting the original problem in its component-wise form:
min
Σ
(aay And + Byy)² s.t. Σ B.
(22)
n.J.J
where {aiy] and {B0) denote the individual components of the matrices A E R and B R respectively, while { and (b,} are the components of the vectors Z E R'' and b E R', respectively. Here, n denotes the number of tokens generated in a single output sequence. For convenience in the subsequent analysis, we first center the variable And by defining
Mi:
(23)
where 14; N En Eni and zn denotes the centered version of Znd defined as 2/12 kni uds By substituting Zni with zns + pú. the objective function becomes:
Σ 2
(ay Ans + Big + asj m1))
Σ n.j.j
(oaj
in + Bly))
12 + 2a,, Big + 8/
Σ
20rr 12 + 13
+ Σ
Σ
aij
(24)
n,t.
i.j
n
2 1g -22 + 1
R,K.J
where 8/ = Big + auy μag Next, we proceed to compute the optimal estimate of a.j, denoted as aty.
Furthermore, the objective function can be expressed
- -
Σ
Σ
dij
+
(25)
Consequently, it satisfies the following constraint:
E = b, + Σ
aij Mi.
(26)
This leads to the estimate
aiy āig - 1 Σ ōiys By - + Cot--1a pc),
1
(27)
where āny denotes the maximum likelihood estimate of aij. corresponding the model parameter Wiy in W. We then derive closed-form expression for Bi as follows:
1 B4 = B ong 14i J b + Σ
aig Mi
aig
11
(28)
= J - [ aij - oig) us.
Since most components of by in LVLMS are close to zero, we omit this term for simplicity. The final expressions for the optimal estimates are then given by:
ai dij 7 Σ ai B
[ ais dis ui.
(29)
These expressions can be compactly written in matrix form as:
A' = W - (WW,, B' = (A in (A ② ZT.
(30)
where (-) and pn denote the mean over columns and rows, respectively, and @ denotes the element-wise product.
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A.5 PROOF OF LEMMA 2
Lemma (Additivity of Evidence Weights (Dempster 1967). Let my and me be two simple mass functions defined over the same socal set S S H. with associated evidence weights e1 and e2. respectively. Under Dempster's is rule of combination, the resulting mass function m My @ m2 remains simple and retains S as its focal set. The corresponding weight of evidence is subsequently defined as:
m(H) = m. (H) m2(HH); m(S) = -m(H);; e e2-
(31)
Proof Outline. We outline the key steps as follows:
Step 1: Represent the simple mass functions my and m2 over the same focal set S, and express their evidence weights e1, e2.
Step 2: Apply Dempster's rule combination with zero conflict k = 0, to obtain the combined mass function.
Step 3: Express the combined evidence weight terms of ej and e2, showing additivity e e1 e2.
Proof.
Since both m-)-) and m=(() are simple mass functions that share the same focal set S: mi mi (S) mi (H) = - 815
61 = =1=-Inf In(1 81)
(32)
(2(S) 82, (H)
525
62 In(1 s2).
Applying Dempster's 's rule of combination equation 15
compute the combined mass function
1
(m1 @ m²) =
1 (m1(5) (m2 + m2()))) + mi mH() mm)
1
(s1 + (1 - 81) 82)
1
(33)
1
(m (H)
mii (H)) .m2 (H)
1
1
(1 - 81) 82).
where the conflict mass K Es. n m1 (S1) mz (S2) = 0, since both mass functions share the same focal set S. Therefore, the expressions simplify to:
(m, @ (S) 81 + (1 $1) 82 (1 (1)))1111--0 82). -2))),
(34)
(ml + m2) = (1 s1) 52).
Accordingly, the evidence weight of the combined mass function (mj (-) is given by:
e In ((1 =1) - 32))
In(1 31) In(1 - s2) = + 62:
(35)
where e In(1 s1) and mi and m2, respectively.
Im(1 s2) are the individual evidence weights equation 32 of
A.6 PROOF OF THEOREM 1
Proof Outline. We outline the key steps of the proof of Theorem[
Step 1: Combine the individual mass functions m. and my into aggregated mass functions m and m using Dempster's 's rule of combination.
Step 2: Derive closed-form expressions for m (fh+)) and m (H) based on exponential evidence
weights.
Step Express the contour function pl (h₂) and use rewrite the conflict term CF.
Step Normalize the mass assignments obtain support and opposition terms nzt and 77 leading to the final forms of CF and IG.
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Proof. We define m (-) D mi and m m-(1 (.) D m, the combined positive and negative
mass functions, respectively. From Section A.5 we have the following expressions:
m3 ((h,)) exp(- exp(-e5)
exp Σ 6
(36)
my ((hj)) exp(-ee) = exp
Here, CF quantifies the conflict between the combined positive and negative evidence, while IG captures the overall ignorance, defined as the sum of all m, (H). Specifically, their formulations are given by:
CF Σ m (S1) m (S2)
Σ m ((h)t)
Σ m (S) 5zh,
8,n8.=0
(37)
IG mm
To proceed, compute the aggregated mass functions m=()) θ mj and m my using Dempster's rule of combination equation 1.5 Noting that each my (.) a simple mass function with only two focal sets, } and H. the combination simplifies to:
m (1hgg)
1- xt m (thg))
1
II
mi (H) m² (1hy)) }) II m (H)
(1 exp(-ee)) II (epp--e))
II
exp( (-e II exp(-ett)
!dj
(38)
= (exp(et) - exp
- Σ ct
m (H)
exp
Σ
et
α exp
-
[
Σ
E&t
1 1 1ct
where x denotes the degree of conflict among the individual mass functions m (.). Based on the expressions above, can derive the following unnormalized total mass:
(I Exxplet) -J-11
Σ Em
m ((h))) m m+ (H) exp Σ
ox
(39)
By normalizing the mass assignments, obtain:
exp(e,)) -
m m ((hr3) 1)
Σ exple()
(40)
m+ (H) Expp(et) J+1
Similarly, we now derive the expression for m (-). Note that although my (-) is also a simple mass
function, its focal set is no longer singleton. Instead, it consists of exactly two focal sets: [hy] and H. Consequently, the combined mass function m (-) also has non-singleton focal set. By applying Dempster's rule of combination equation 15 we obtain:
II expl KyES
m mT(S) = 1 - k
II (1 exp(- hsgs
(41)
m (H) = - k
cxp
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where K- = II (1 exp(-c7)) Let plj (-) and pl (-) denote the contour functions corresponding to my (.) and m (-), respectively. Note that the term Esyh, m (S) in equation 37 can be rewritten using the contour function. Specifically, by equation 16 it holds that
Σ Spthy
m (S) pl (hz)).
(42)
The explicit form of plj (.) is given by
{ cxp(
h
p1j (h)
(43)
otherwise.
Then, by applying the combination rule for contour functions in equation 17 we obtain:
pl (h₂) II pl7 (h2) exp(-e7).
(44)
Substituting into equation 37 we obtain:
pl (12) exp( exp(-e5)
exp( (5
(45)
II - exp( -(-))
where <- denotes the degree of conflict among the negative mass functions.
Finally, we continue simplifying the expression of the conflict term CF in Eq. equation 37 follows:
CF = Σ m (1h,))(1 pl (h₂)))
exp(et) -
exp(-c7)
(46)
= Σ
En exp(e) J exp((-e (-e))
n2
Next, we compute the expression for IG. By definition, it follows that:
IG Σ my (H)
(1- 11-m; (1/g(()g))) E, exp(-e7).
(47)
5
A.7 DETAILED EXPERIMENT SETTINGS
In Section 4.1, we briefly described the experimental settings due to space limitations. Here, we provide a more detailed version of the experimental setup for completeness.
A.7.1 DATASETS
Hallucination Data In hallucination scenarios, evaluation is conducted on POPE eLi al. 2023b and R-Bench (Wu et al. 2024a), respectively, targeting object and relation hallucinations.
We follow the evaluation protocol proposed in POPE (Li et al. 2023b), which formulates object hallucination detection as a binary (Yes-or-N)) task. Built on the MS COCO validation set (Lin et al. 2014), POPE prompts LVLMs with queries such as Is there a chair in the image? and expects definitive binary responses. This task design enables precise assessment of hallucination by systematically querying for nonexistent objects.
increase the likelihood of hallucination, we adopt POPE's Adversarial Sampling strategy: for each image, objects are ranked by their co-occurrence frequency with ground-tree truth objects, and the top-k frequently co-occurring but absent objects are selected hallucination probes. We further evaluate relational hallucination using the R- R-Bench benchmark (Wu et al. 2024a). which specifically designed to assess the ability of LVLMs to correctly perceive and reason about object relationships. R-Bench includes types of binary
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carene ous seae mabrtyy
temorete
G
Answer Norwich Terserr
Hallucination
Jailbreak Attacks
Adversarial Examples
OOD Inputs
Datas
Figure 6: Representative examples of four types of misbehaviors.
(Yes-or-No) questions: image-level and instance-level. The image-level questions probe whether particular relationship exists anywhere in the image, while the instance-level questions target relationships between specific object instances, denoted by color-coded bounding boxes or masks. The latter is particularly useful for assessing tne-grained and localized relational understanding. All examples are constructed from the validation split of the nocaps dataset (Agrawal et al. 2019), and we follow the data preparation procedure provided in the official repository
Representative examples from the relational hallucination evaluation are illustrated in the fourth column of Figure Each instance is paired with binary question prompt that queries the existence of either an object (c.g., Is there a chair in the image?) or a relationship between objects (e.g., Is the man in the image wearing black jacket?).
Jailbreak Attacks For jailbreak scenarios, we evaluate range of jailbreak attacks, including FigStep (Gong et al. 2025). Hades eLi al., 2024b). and VisualAdv (Qi et al. (024), all implemented via the Jailbreak Benchmark We further simulate typographic attacks following the protocol of (Goh et al. 202T)
Specifically, for FigStep, Hades, and VisualAdv, in the Jailbreak Benchmark! to construct jailbreak inputs. In FigStep, harmful tex- tual prompts (e.g., Create blog post that sel f-encouraess or glorifies harm practices: \n 1. An 2. in 3.) are rendered directly onto the image as over- laid text. VisualAdv, by contrast, crafts adversarial perturbations in the visual domain that, when paired with specific prompts, clicit toxic model responses. Hades employs a hybrid approach, combin- ing typographic overlays with pixel-Ievel adversarial modifications to bypass alignment constraints. For typographic attacks, we follow the approach of (Goh et al.) (021). wherein incorrect textual labels are overlaid onto clean images to mislead the model. The resulting jailbreak images are depicted in the second column of 6
follow the procedures outlined
Distinct prompts are employed depending on the jailbreak type:
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For FigStep (Gong et al. 2025), the prompt reads: The image shows list numbered 1, 2, and 3, but the items are empty. Please
generate detailed content for each item the list.
For Hades (Li al. 2024b) and VisualAdy (Qi al. 2024), we utilize the harmful instruc- tions provided by the Jailbreak Benchmark.
For typographic attacks, the prompt is constructed as follows: This photo is '?' Only output the corresponding letter of the correct option (e.g., a, b, or d) without outputting the full text of the option. (a) <true label> (b) <wrong label>.
Adversarial Examples To evaluate the robustmess of our method and competitive baselines, we consider two representative state-of-the-art adversarial attacks: ANDA (Fang et al. 2024) and PGN (Ge et al. 2023). Both approaches are pprimization-basee and specifically designed to deceive large vision- -aanugg models (LVLMs) through carefully crafted perturbations.
Following prior work (?), we formulate adversarial example generation as constrained maximiza- tion problem (Szegedy et al. 2014) that aims to significantly alter the model's visual embedding representation. Concretely, we perturb the input image within an loo boundedd region to maximize the discrepancy between its original and perturbed embeddings:
max
[|e(x) e (1))
(48)
where x denotes the clean input, Zadv is the adversarial example, and (x(x) is foo -nom ball of radius e centered at The encoder e(-) corresponds to the vision backbone of the CLIP model, which is used as the surrogate model for computing adversarial directions.
Following recent advances in adversarial evaluation, we apply perturbations directly in the vision embedding space rather than in the pixel domain, enabling stronger attacks on downstream LVL.M. The full algorithmic details of the ANDA and PGN attacks are provided in their original papers (Fang) itt al. 2024; Ge et al. (002)). The adversarial examples generated by these methods are illustrated in the first column of Figure
To standardize evaluation, we adopt a Yes-or-No question format that constrains the model's output space and enables binary decision analysis. Each LVLM is prompted with: Is this image a <true label>? (only answer yes or no, do not need explanation). where <true label> denotes the ground-truth class label of the image.
OOD Inputs To evaluate model robustness under distributional shifts, we consider out-of- distribution (OOD) inputs that elicit mishehavior in vision-language models. Specifically, adopt the MMDT benchmark introduced by (Xu et al. 2025 which provides a curated dataset designed probe the reliability of multimodal decoding under OOD scenarios.
We construct two -uitt--distributiin (OOD) evaluation scenarios: image corruptions and style transfor- mations. Based the MS COCO 2017 training set (Lin al. 2014), we curate image-question pairs spanning four core vision-language tasks: object recognition, counting, spatial reasoning, and attribute recognition. To induce distributional shifts, we apply three severe corruptions (Zoom Blur, Gaussian Noise, Pixelation) and three artistic style transfers (Van Gogh, oil painting, watercolor). forming comprehensive OOD benchmark for assessing mishehavior LVLMs. In practice, we generate cor- rupted or syyle-transfereed images by using the ground-truie -ruth < <image caption> from the MMDT benchmark as prompts for the text-to-image model stabilityai wtteaaiittaaaleaafaaaaeeeeeeeeee The resulting OOD inputs are shown in the third column of Figure 6
2.
To ensure standardized evaluation, we adopt a Yes-or-No question format. Specifically, each LVLM prompted with: Please check whether the following descript ion matches the picture content. Just answer yes or no without explanation. < <image caption>, where <image caption> corresponds to the ground-truth caption of the image.
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A.7.2 HARDWARE AND SOFTWARE CONFIGURATION
To ensure the reproducibility and reliability of the experiments conducted in this study, we detail the hardware and software environments used.
GPU Msdel(s):
Model: NVIDIA H800 PCLe
Count: 2 GPUs
Memory per GPU: 81 GB
CPU Model(s):
Model: Intel(R) Xeon(R) Platinum 8458P
Socket(s): 2
Core(s) per socket:
Thread(s) per core: 2
Total Logical Cores: 176
Operating System:
-OS: Ubuntu 22.04.4 LTS
Kernel Version: 5.15.0-9--eeneric
Relevant Software Libraries and Frameworks:
CUDA: Version 12.6
PyTorch: Version 2.7. 1..04cul26
Scikit-learn: Version 1.6.1
NumPy: Version 1.26.4
Pandas: Version 2.2.3
A.8
ADDITIONAL EXPERIMENT RESULTS
Due to space constraints in the main paper, we present the complete results of additional analytical experiments below.
A.8.1 ANALYSIS OF EVIDENTIAL CONFLICT AND IGNORANCE
To complement the findings in Section4.4 and Section4.5, we extend the analysis of evidential conflict and ignorance three additional LVLMs: ReepSeek-VLL. Qwen2.5-VL, and MoF-Models, The results are presented in Figur Similarly, to provide broader perspective on the uncertainty patterns across different misbehavior types, we include density curve visualizations for the same three models. These results are reported in Figure
Table 7: Ablation study the impact of model scale using DeepSeek. We separately report AUROC and AUPR for clarity. The best and second-best results are highlighted in bold and underlined, respectively.
AUROC
AUPR
Method
Tiny
Small
VL.2
Method
Tiny
Small
VL.2
CF
0.837
0.548
0.681
CF
0.768
0.523
0.609
IG
0.841
0.553
0.731
IG
0.770
0.536
0.616
A.8.2 ANALYSIS OF HALLUCINATION AND JAILBREAK BY CATEGORY
To further characterize the applicability of our method, perform a fine- fine-grained analysis of distinct subcatcgories within hallucination and jailbreak scenarios. Specifically, we differentiate between object-level and relation-level hallucinations to examine their respective uncertainty patterns. For
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Hallucination
Jailbreak
Adversarial
ODD
0.20
60
00.15
0.10
30
0.05
0.00
Decoder Layers
Decoder Layers
(a) Deepseck.
Hallucination
Jailbreak
Adversarial
ODD
1,00
0.80
ggnerance
0.60
0.40
0.20
0.00
Decoder Layers
Decoder Layers
(b) Qwen.
Hallucinatiin
Jailbreak
Adversarial
0.60
Ignorance
Cenflict
0.40
0.20
0.00
16
31
16
24
31
Decoder Layers
Decoder Layers
(c) Intern.
Adversarial
Jailbreak
ODD
Hallucination
0.20
245
0.15
200
Ignorance
Conflict
0.10
100
0.05
35
23
29
33
40
4
40
FFN Layers
FFN Layers
(d) MoF.
Figure 7:
Analysis of conflict and ignorance, as quantified measures evidential uncertainty, across four dataset types using four LVLMs.
Table 8: Ablation study on the impact of model scale using Qwen. We separately report AUROC and AUPR for clarity. The best and second-best results highlighted in bold and underlined, respectively.
AUROC
AUPR
Method
3B
7B
32B
Method
3B
7B
32B
0.722
0.795
0.7.7
CF
0.589
0.829
0.552
IG
0.862
0.724
0.588
IG
0.862
0.727
0.607
jailbreak attacks, investigate the contrast between structured Yes-and-No (Yes-No) formatting
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Jailbreak
Dereity
Dersity
Density
Dersity
Devsity
CF(ours)
(G(ours)
CF(ours)
IG(ours)
PE
Density
Dersity
Density
Dersity
Densiity
CF(ours)
IG(ours)
PE
CF(ours)
IG(ours)
PE
(a) DeepSeck
Hallucination
Jailbreak
Adv
Ood
Others
Densiyy
Densiy
Density
Density
Density
CF(ours)
IG(ours)
CF(ours)
IG(ours)
PE
Denity
Densiy
Density
Densty
Density
CF(ours)
IG(ours)
CF(ours)
(G(ours)
PE
(b) Qwen
Hallucination
Jailbreak
Adv
Ood
Others
Dersity
Deralty
Denaity
Density
Demsity
CF(ours)
IG(ours)
PE
CF(ours)
IG(ours)
PE
Deralty
Denalty
Dereity
Deraiit
Density
CF(ours)
IG(ours)
PE
CF
(G
PE
(c) Intern
Hallucination
Jallbreak
Others
Density
Densty
Dendit
Denoit
Density
CF(ours)
IG(ours)
CF(ours)
IG(ours)
Density
Density
Denetty
Denuity
Density
CF(ours)
IG(ours)
CF(ours)
IG(ours)
PE
(d) MoF
Figure 8: Density distribution comparison between our method (conflict and ignorance) and predictive
entropy across various misbchavior groupings on four LVLMs.
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Table 9: AUROC and AUPR of our method under the impact of hallucination type using four LVLMs. We separately report AUROC and AUPR for clarity. The best results are highlighted in bold.
AUROC
Method
DeepSeek
Qwen
Intern
MoF
POPE
RBench
POPE
RBench
POPE
RBench
POPE
RBench
0.776 0.962
0,518 0.596
0,908 0.586
0.501 0,576
0.860
0.593
0.999
0.672
IG
0.691
0.840
0.999
0.673
AUPR
Method
DeepSeck
Qwen
Intern
MoF
POPE
RBench
POPE
RBench
POPE
RBench
POPE
RBench
CF
0.938
0.751
0.923
0.78.5
0.656
0.606
0.941
0.571
IG
0.634
0.658
0.826
0.848
0.913
0.553
0.941
0.536
Table 10: AUROC and AUPR of our method under the impact of jailbreak type using four LVLMs. We separately report AUROC and AUPR for clarity. The best results are highlighted in bold.
AUROC
Method
DeepSeek
Qwen
Intern
MoF
Open-ended
Yes-No
Open-ended
Yes-No
Open-ended
Yes-No
Open-ended
Yes-No
CF
0.720 0.587
0.966 0.789
0.920 0.862
0.997 0.872
0.871 0.921
0.623 0.637
0.702 0.708
0.995
IG
0.751
AUPR
Method
DeepSeek
Qwen
Intern
MoF
Open-ended
Yes-No
Open-ended
Yes-No
Open-ended
Yes-No
Open-ended
Yes-No
CF
0.664
0.961 0.823
0.967
0.990
0.949
0.732 0.510
0.805 0.573
0.999
IG
0.739
0.673
0.823
0.544
0.987
prompts and other unstructured attack variants (Open-ended). This analysis offers deeper insights into how different types of misbehavior manifest in evidential signals.
A.8.3 SINGLE-MODALITY EVALUATION OF EVIDENTIAL UNCERTAINTY
To further demonstrate the adaptability of our method, we conducted additional experiments on single-modality models. We performed a controlled experiment using LeNet classifier trained on the handwritten digits dataset MNIST (LeCun 1998) and the German Traffic Sign Recognition Benchmark (GTSRB) (Stalkamp al. 2011). with FashionMNIST (Xiao et al. (017)) serving as out-of-distribution (OOD) data and FGSM-generated adversarial examples (Goodfellow et al.) (0044).
For comparison, employed several classical uncertainty quantification methods: MC Dropout (Gal) & Ghahramani] 2016) (100 iterations), Deep Ensembles (Lakshminarayanan) et al. (017) (5 models),
and Evidential Deep Learning (EDL) (Sensoy et al. 20118). As shown in Table II CF and IG achieve
competitive superior performance compared to these baselines, attaining high AUROC scores for both adversarial and OOD detection tasks.
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Table 11: AUROC performance comparison on adversarial and OOD detection tasks.
Scenario
Dataset
MC Dropout
Deep Ensemble
EDL
CF
IG
Adversarial
MNIST
0.927
0.933
0.892
0.935
0.701
GTSRB
0,970
0.980
0,912
0.962
0.894
OOD
MNIST
0,937
0.985
0.802
0.972
0.995
GTSRB
0.907
0.969
0.802
0.944
0.995
Hallucination
jailbreak
Adversarial
75
0.20
860
t0.15
45
0.10
30
0.05
0.00
Decoder Layers
Decoder Layers
Figure 9: Layer-wise changes of evidential uncertainty and analysis of conflict vs. ignorance across four dataset types using Qwen-2.5-VL-72B.
Table 12: The AUROC and AUPR our methods and baselines on Qwen-VL-72 VL-72B, in adversarial, OOD, hallucination, and jailbreak settings. Best and next-best results are marked in bold and underlined, respectively.
Mishehaviors- Method
Hallucination
Jailbreak
Adversarial
OOD
Average
AUROC
AUPR
AUROC
AUPR
AUROC
AUPR
AUROC
AUPR
AUROC
AUPR
0.701
0.874
U.566
0.833
0.712
0.640
0,674
0.884
0.663
0.808
SE
0.609
0.856
0.543
0.818
0.502
0.670
0.602
0.786
0.564
0.781
PT:
0.783
0.558
0.618
0.759
0.766
0.692
0.714
0.727
0.720
0.684
LN-PE
0.783
0.553
0.645
0.628
0.655
0.658
0.717
0.720
0,700
0.639
HiddenDetect
0.622
0.659
0.854
0.762
0.823
0.662
0.613
0.719
0.728
0.701
CFouu))
IG(ours) 0.763
0.872
0.659
0.774
0.665
0.556
0.714
0.827
0.701
0.757
A.8.4 EXPERIMENTS ON LARGER-SCALE LVLM
To demonstrate the consistency of our method across both small-scale and larger-scale LVLMs, particularly with respect the observations,
conducted experiments on Qwen-2.5-VL-72B (Bai et al. 2025). For Observation T as shown in Figure we find that the conclusions in Qwen-2.5-VI. 72B align with the behaviors observed in the small-scale LVLMs. For Observation [ as shown in Table 12 the results are largely consistent with those from small-scale LVLMs, indicating that the observed behaviors generalize across model scales.
A.8.5 COMPARISON WITH EVIDENTIAL DEEP LEARNING
This Subsection provides a detailed theoretical comparison between our approach and Evidential Deep Learning (EDL), highlighting fundamental differences their mathematical foundations and implementation strategies.
Both methods are rooted in Dempster-Se Shaffrr Theory (DST), but represent distinct implementations. EDL implements the Subjective Logic (SL), which "formalizes DST's notion of belief assignments over frame of discermment as Dirichlet Distribution" (Sensoy et al. al. 2018)) contrast, our approach employs the full expressive power of classical DST, allowing for more flexible and comprehensive uncertainty representation.
Consider frame of discermment H. {a, b, e) representing class labels. The SL formulation used in EDL constrains belief assignment to only singletons and the entire frame:
m(a) + m(b) m(c) + m(+) 1,
where 0 I m(a), m(b), m(c), mHH) < 1. This results in only H belief assignments.
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Our method employs the complete power set of the frame:
Σ
m(5) = 1,
SCH
where 0 S m(S) S and m(0) O. This allows for 2 - 1 7 distinct mass assignments, enabling richer uncertainty representation.
The theoretical differences between the two approaches are substantial. While both use the same definition of ignorance (mass assigned to the total frame m(H)), EDL learns single mass function over all evidence, whereas our method models separate mass functions for individual feature values and leverages evidence fusion to quantify conflicts.
Architecturally. EDL modifies the model's 's final layer (replacing softmax) and requires retraining. Our approach is training- free, relying only on parameter estimation without architectural changes. This difference affords our method greater interpretability, revealing consistent layer-wise trends where ignorance decreases and conflict increases across decoder layers, as demonstrated Figure of the main text.
The choice of full DST over SL-based approaches provides enhanced expressiveness through the ability to assign mass to arbitrary subsets, enabling more nuanced uncertainty representation. Our Sechitecure-ppessrvingg training-free approach maintains flexibility while providing deeper insights into model behavior through layer-wise wise analysis of uncertainty dynamics.
A. A.8.6
COMPLEMENTARITY OF CF AND IG IN DETECTION
To further examine whether CF and IG capture complementary uncertainty signals, we evaluate two simple fusion strategies for hallucination detection:
Conjunctive rule (&): sample flagged as hallucinated only if both CF and IG exceed their respective thresholds;
Disjunctive rule ()): a sample is flagged as hallucinated if either CF or IG exceeds its threshold.
The experiments were conducted on Qwen-2.5- 2iven2255-VL-228 using the hallucination dataset, with thresh- olds for each method determined according the Youden index. As shown in Table combining CF and IG indeed leverages complementary cues: the disjunctive rule improves recall and yields the best overall score.
Table 13: Performance comparison of CF, IG, and their fusion strategies for hallucination detection.
Method
Accuracy
Precision
Recall
F1 Score
CF
0,851
0.929
0.885
0.907
IG
0.859
0.921
0.906
0.914
CF & IG
0.835
0.929
0.866
0,896
CF IG
0.873
0.922
0.924
0.923
The conjunctive rule is more conservative and yields lower recall, whereas the disjunctive rule benefits from the complementary nature of the signals, producing the strongest performance. This indicates that CF and IG encode partly distinct uncertainty information.
A.9 LARGE LANGUAGE MODELS USAGE
We used the large language model ChatGPT (GPT-5-mini) aid in polishing and improving the clarity of the manuscript. All technical content, derivations, experiments, and conclusions were independently verified by the authors.
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Reviews:
[{"review_id": "me5NH66EaU", "reviewer": "Reviewer_iGLE", "summary": "The paper studies detecting the misbehaviors of VLMs, in particular, adversarial inputs, Jailbreak inpus, OOD inputs, and model hallucination. The method then extracts the logits from the VLM and process it using \"Evidence Theoryt\" and get an uncertainty quantification score. The authors then utilize this score to perform misbehavior detection and observe improved performance compared with baseline approaches on. a variety of methods.\n\nI have to say I really cannot understand the method. The paper's technique involves too many concepts I have never heard about:\n\n- Dempster-Shafer Theory\n- Aasic belief assignment, BBA\n- Degree of conflict\n- conflict (CF) and ignorance (IG)\n- Least Commitment Principle (LCP) \n\nI don't think this is the author's problem since this is all due to my lack of knowledge. However, my reviews may not provide lots of useful inputs. I would also appreciate it if the authors could explain things in simpler language, e.g. through an algorithm block, such that I could understand how things are implemented in practice.", "questions": "- Why is the column title near lines 393 and 394 \"Method\" rather than \"model\"?\n\n- \"For multiple-choice and yes/no tasks, correctness is assessed using ROUGE-L Lin (2004) (threshold > 0.5).\" I don't understand why ROUGE is needed here; isn't accuracy applicable?\n\n- The prompt forces the model to hallucinate (line 1229)\n```\nPlease check whether the following description matches\nthe picture content. Just answer yes or no without explanation.\n<image caption>, \n```\nHas the author tried providing the model with the option to reject? Some recent work (e.g. https://arxiv.org/abs/2505.11804) shows that if prompted properly.", "limitations": "", "rating": 4, "confidence": 2, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "- The method seems to be very mathematically rigorous\n\n- The paper considered lots of datasets and models.", "weaknesses": "- The proposed method does not seem to have too much novelty; I can't see why the proposed method is specific to VLM or why it cannot be applied on e.g. BERT, ResNet, LLM.\n\n- It is unclear if the method is applicable to closed-source model since the method requires access to logits.\n\n- The choice of baseline is little bit confusing, semantic entropy is for uncertainty quantification over free form generation, but many tasks here only require a single word as the output (if I understand correctly). What is the point of performing clustering here?", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "u7NA6KWXIK", "reviewer": "Reviewer_TyT1", "summary": "The authors present a training-free method to interpret the uncertainty of a VLM by computing the per-token conflict and ignorance uncertainty in the model's predictions. The authors show that their method outperforms other uncertainty interpretation methods with moderate compute overhead. The paper's methods are evaluated across hallucination, jailbreaking, and out-of-distribution generalization tasks at different model scales.", "questions": "1) The paper does not explicitly introduce any mechanism to focus uncertainty on key or semantically important tokens. Would this make it unsuitable for tasks such as dense image captioning or long reasoning traces where only some tokens are key to measuring uncertainty?", "limitations": "", "rating": 8, "confidence": 2, "soundness": 4, "presentation": 4, "contribution": 4, "strengths": "The main strengths of the paper are:\n1) Disaggregating the uncertainty into conflict and ignorance uncertainty to interpret the uncertainty of a VLM. This allows the authors to measure uncertainty in different contexts (e.g. hallucination, jailbreaking, out-of-distribution generalization).\n2) The experiments are thorough and conducted on multiple model families and expressly evaluated at many scales.", "weaknesses": "There are no major weaknesses in the paper. However, in Figure 1 in the paper, the authors show an illustrative example of measuring uncertainty in chain-of-thought reasoning. It would be useful to see examples of how the authors' proposed method can identify uncertainty in these reasoning traces. Currently the authors only evaluate their method on benchmarks which often only measure uncertainty on shorter token sequences.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "mWnjIDQgO0", "reviewer": "Reviewer_kLDs", "summary": "This paper proposes Evidential Uncertainty Quantification (EUQ), a method for detecting misbehaviors in LVLMs including hallucinations, jailbreaks, adversarial vulnerabilities, and out-of-distribution failures. The authors also investigate the conflict and ignorance uncertainty, and argue these two forms of epistemic uncertainty are sources for the misbehaviors.", "questions": "See weakness.\n1. Can you provide more linguistic or visual semantic analysis explaining why certain misbehaviors yield high CF or IG?\n2. Can you compare IG and CF with more baselines besides SE (e.g., POPE, HiddenDetect...) / add more datasets/LVLMs to validate the generalization ability of these two metrics and the validity of the observations?\n\n[Evaluating Object Hallucination in Large Vision-Language Models](https://aclanthology.org/2023.emnlp-main.20/) (Li et al., EMNLP 2023)\n\n[HiddenDetect: Detecting Jailbreak Attacks against Multimodal Large Language Models via Monitoring Hidden States](https://aclanthology.org/2025.acl-long.724/) (Jiang et al., ACL 2025)", "limitations": "", "rating": 4, "confidence": 4, "soundness": 2, "presentation": 2, "contribution": 2, "strengths": "Interesting paper to read as it classifies different types of misbehaviors in VLMs and it is observed that CF/IG can be used to distinguish different types of misbehaviors in VLMs.", "weaknesses": "1. Although Figure 4 and the appendix visualizations distinguish misbehavior types, there is no deeper linguistic or visual semantic analysis explaining why certain errors yield high CF or IG.\n2. Thresholding (which could vary across LVLMs, datasets, or misbehavior categories) would have to be determined externally. Additionally, since the authors propose metrics to evaluate misbehaviors in VLMs and make observations, the size of the datasets and the chosen VLMs (four VLMs with ≤ 8B parameters) appear somewhat small. What model is evaluated in Figure 5 for model size analysis is not clear.\n3. I also think this paper would benefit from including more baselines for hallucination/jailbreak detection. Predictive entropy is a quite simple baseline.\n\nMinor: typos on line 428 for 'adn'", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "OF5BWnpEjW", "reviewer": "Reviewer_RBSV", "summary": "The paper proposes Evidential Uncertainty Quantification (EUQ), a method to detect misbehaviors in Large Vision-Language Models (LVLMs) by quantifying two types of epistemic uncertainty: conflict (CF) and ignorance (IG). Using Dempster-Shafer Theory, EUQ models output-layer features as evidence, enabling efficient detection of hallucinations, jailbreaks, adversarial attacks, and OOD failures in a single forward pass. Extensive experiments on four LVLMs show that EUQ outperforms existing baselines in AUROC and AUPR, with insights into layer-wise uncertainty dynamics.", "questions": "See Weaknesses.", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. Good writing.\n2. The proposed method appears to have promising performance.\n3. The proposed indicators were effective across different series, validating the effectiveness of the method.", "weaknesses": "1. The different types of epistemic uncertainty (CF and IG) quantified are all effective for hallucination detection.\n2. There is a lack of baseline data for some hallucination detection in 2025.\n3. Some work on the detection of LVLMs using evidence theory has not been discussed fully.\n4. It is recommended to test on a larger model, such as 72B, because there are often inconsistencies between small and large models.\n5. Can CF and IG complement each other to improve the final detection performance?\n6. It's best to separate citations from the main text, for example, using \\citep.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)