EVADE-Bench: Multimodal Benchmark for Evasive Content Detection in E-Commerce Applications

## Abstract

Abstract E-commerce platforms increasingly rely on Large Language Models (LLMs) and Vision–Language Models (VLMs) to detect illicit or misleading product content. However, these models remain vulnerable to evasive content : inputs (text or images) that superficially comply with platform policies while covertly conveying prohibited claims. Unlike traditional adversarial attacks that induce overt failures, evasive content exploits ambiguity and context, making it far harder to detect. Existing robustness benchmarks provide little guidance for this demanding, real-world challenge. We introduce EVADE , the first expert-curated, Chinese, multimodal benchmark specifically designed to evaluate foundation models on evasive content detection in e-commerce. The dataset contains 2,833 annotated text samples and 13,961 images spanning six demanding product categories, including body shaping, height growth, and health supplements. Two complementary tasks assess distinct capabilities: Single-Violation , which probes fine-grained reasoning under short prompts, and All-in-One , which tests long-context reasoning by merging overlapping policy rules into unified instructions. Notably, the All-in-One setting significantly narrows the performance gap between partial and full-match accuracy, suggesting that clearer rule definitions improve alignment between human and model judgment. We benchmark 26 mainstream LLMs and VLMs and observe substantial performance gaps: even state-of-the-art models frequently misclassify evasive samples.
By releasing EVADE and strong baselines, we provide the first rigorous standard for evaluating evasive-content detection, expose fundamental limitations in current multimodal reasoning, and lay the groundwork for safer and more transparent content moderation systems in e-commerce.
The dataset is publicly available at: https://huggingface.co/datasets/koenshen/EVADE-Bench .

## 1 Introduction

Figure: Figure 1: Illustration of an EVADE sample.
Refer to caption: x1.png

In recent years, Large Language Models [44, 42] and Vision Language Models (VLMs) [19, 20] have made significant progress across various fields.
These models have gained widespread attention for their applications in natural language processing, image recognition, and multimodal tasks, and continue to drive technological advancements across industries[25, 35, 32]. Particularly in the e-commerce domain, these models have been extensively applied to tasks such as product search, recommendation, and content moderation [38, 17]. However, when confronted with the task of Evasive Content Detection (ECD)—identifying text or image content that has been deliberately altered to circumvent platform rules while still conveying misleading information—they exhibit significant limitations in performance.

The task of ECD represents an adversarial dynamic between sellers and platform policies, which differs from traditional adversarial attacks [46, 15]. Conventional adversarial attacks typically manipulate inputs (e.g. tiny pixel noise or prompt injections) to induce harmful or incorrect model outputs [8, 21]. In contrast, the adversarial nature of ECD lies at the data level. For instance, sellers may obscure prohibited health claims using euphemistic language or crop images to conceal policy-violating details. When such content escapes detection, platforms face regulatory fines, lawsuits, fraudulent transactions and reputational damage—economic losses that rival technical security breaches[33]

Merchants often try to enhance product sales by exaggerating claims, using ambiguous language, or distorting images to attract consumers and bypass platform content review. In contrast, platforms must ensure fair transactions and must identify and regulate misleading product promotions as effectively as possible. In this context, merchants continuously attempt to exploit loopholes in advertising laws by using misspellings, slang, emojis, and mosaics to modify text and images, avoiding the platform’s automatic scrutiny. Thus, adversarial issues in the e-commerce sector are even more pronounced.

Despite these violations, evasive content detection remains underexplored. Current models are particularly limited for two key reasons. First, LLMs and VLMs continue to hallucinate [16, 6], mis-follow instructions [24, 27], and struggle with long or multimodal contexts [40, 7], making it difficult to recognize nuanced deception. Second, real-world e-commerce regulations evolve rapidly, and human annotations are often inconsistent, leading to noisy and ambiguous training data. Together, these factors hinder the development and evaluation of models capable of trustworthy moderation.

To address this gap, we introduce the Evasive Content Detection in E-Commerce Benchmark (EVADE), a Chinese multimodal benchmark specifically designed for evaluating how well LLMs and VLMs detect evasive content in real-world e-commerce scenarios. Every example is iteratively annotated by domain experts to ensure accurate and consistent ground truth. EVADE contains 2,833 text samples and 13,961 product images, collected from six demanding product categories where deceptive content is most prevalent: weight loss, diseases, height growth, body shaping, female care, and male care.
Each sample is iteratively annotated by domain experts to ensure its accuracy and reliability. Furthermore, we design two tasks for EVADE: Single-Violation and All-in-One. The former evaluates a model’s ability to perform rapid and fine-grained judgments within in a narrowly scoped context, while the latter explores its capacity for reasoning under complex and rule-intensive conditions.

EVADE offers two evaluation tracks to assess both a model’s ability to quickly identify specific violations and its robustness when faced with comprehensive and strategically worded prompts. We conduct a comprehensive evaluation of 26 mainstream LLMs and VLMs on EVADE, analyzing their performance on this challenging and underexplored task.

Our key contributions are as follows:

- 1.
We release EVADE, the first expert-curated, Chinese multimodal dataset tailored for evasive content detection in e-commerce. The benchmark features two evaluation tracks—Single-Violation and All-in-One—that probe distinct reasoning capabilities under varying policy contexts.
- 2.
We show that clearer rule categorization significantly improves model consistency and reduces false predictions, highlighting the importance of benchmark design for reliable evaluation.
- 3.
We benchmark 26 open- and closed-source LLMs and VLMs, offering the first systematic baseline on this underexplored but high-impact problem.

Through these contributions, we aim to catalyze research in adversarial and evasive content detection, support the development of safer and more trustworthy moderation systems, and advance the field of robust multimodal reasoning in demanding commercial settings.

## 2 Related Work

### 2.1 Evasive and Obfuscated Content Detection on Online Platforms

Recent work on online safety highlights adversarial and obfuscated content detection in hate speech and cyberbullying. SWE2 [26] enhances robustness to lexical attacks by combining word- and subword-level features, while an LSTM-based model with correction mechanisms improves resilience to deceptive cyberbullying patterns [4]. Autoregressive models have also been used to craft graded adversarial examples for implicit hate detection [28], introducing a "build-it, break-it, fix-it" retraining loop that boosts model robustness to nuanced, context-sensitive abuse.

In e-commerce, deception extends beyond text to UI manipulation and covert messaging. For instance, fine-tuned BERT models with generative reasoning have been used to detect misleading interface language from online shopping platforms [36]. Separately, Liu and Poesio [22] use LLM-generated multilingual fake reviews to improve cross-lingual review detection. While impactful, these works target unimodal or general deception, overlooking the multimodal, regulation-specific challenges of evasive advertising.

### 2.2 Multimodal Large Language Model Benchmarks

The safety and robustness of VLMs have gained attention with specialized benchmarks. The Hateful Memes challenge [18] pioneered rigorous multimodal evaluation by using subtle hateful content contrasted with benign distractors, discouraging unimodal shortcuts.

Later benchmarks like MM-SafetyBench [23] used 5,040 adversarial image-text pairs to show that even aligned models are vulnerable to malicious prompts. MMSafeAware [39] found GPT-4V misclassified over one-third of unsafe and over half of safe inputs, exposing poor safety awareness across 29 threats. VLDBench [37] evaluated 31,000 news-image pairs and showed that adding visual context improves disinformation detection by 5–35% and enhances compliance monitoring.

### 2.3 Gap and Our Contribution

Existing benchmarks largely address general safety issues like hate speech or misinformation, often overlooking the complex, policy-evasive strategies prevalent in real-world e-commerce. They also tend to lack domain specificity, multimodal context, and real enforcement alignment. To bridge this gap, we present EVADE—the first expert-annotated, Chinese-language, multimodal benchmark tailored to e-commerce policy violations. EVADE captures both fine-grained and long-context tasks across text and image, offering a rigorous foundation for evaluating multimodal reasoning and regulatory compliance in LLM/VLM systems.

## 3 The EVADE

### 3.1 Overview of the EVADE

We introduce EVADE, a multimodal benchmark grounded in Chinese advertising law regulations, designed to evaluate whether current LLMs and VLMs can effectively identify evasive content in real-world e-commerce settings. EVADE integrates both textual and visual inputs, requiring models to jointly comprehend and reason over multimodal data in accordance with explicit policy guidelines.

This benchmark comprises 2,833 text samples and 13,961 images, all collected from real-world e-commerce platforms. Each instance has been manually annotated by domain experts with deep familiarity in advertising law, ensuring high-quality and regulation-compliant labels.

**Table 1: The data distribution of EVADE and the prompt length corresponding to each violation category.**
| Data Type | Text Count | Image Count | Prompt Tokens |
| --- | --- | --- | --- |
| Body Shaping Products | 202 | 2,134 | 614 |
| Women’s Health Products | 211 | 1,295 | 652 |
| Height Growth Products | 553 | 3,424 | 953 |
| Men’s Health Products | 652 | 1,738 | 1,123 |
| Weight Loss Products | 442 | 1,203 | 1,364 |
| Health Supplement Products | 773 | 4,167 | 3,379 |
| Overall | 2,833 | 13,961 | / |

Images present a particularly challenging modality, as they often embed both textual claims and visual cues. Thus, image-based reasoning not only requires the VLM to correctly extract embedded text via OCR but also to interpret visual elements in conjunction with policy guidelines. This dual-modality reasoning is essential for detecting subtle forms of evasion, such as cropped disclaimers or euphemistic imagery.

This setup allows us to evaluate a model’s capability to detect policy violations through both syntactic understanding and semantic inference, under realistic content moderation conditions.

### 3.2 Data Collection and Rule Formulation

To construct a diverse and regulation-aligned benchmark, we collected 25,380 texts and 48,000 images from six e-commerce sub-domains prone to policy violations (e.g., body shaping, height growth). In collaboration with legal experts, we designed six rule sets based on Chinese advertising law and platform norms. The same prompts were applied to both text and image inputs within each sub-domain, enabling controlled comparisons between LLMs and VLMs. Trained annotators labeled each instance based on rule violations, ensuring consistent and high-quality supervision for model evaluation.

#### Data Annotation

While expert human annotation provides high-fidelity labels, it is not immune to inconsistencies due to fatigue, subjectivity, or ambiguous cases. To mitigate this, we implemented an iterative annotation-refinement process that integrates human and model feedback.

In the first round, annotators labeled each instance based on six rule prompts. Predictions from three LLMs (GPT-o1mini, DeepSeek-R1, QwenMax) and three VLMs (GPT-4o, Claude-3.7, Gemini-2.5-Pro) were then used to identify samples that diverged from human judgment. These discrepant cases were flagged for re-annotation by professional annotators in the second and third rounds, using model outputs as reference. This iterative process progressively resolved ambiguities and improved label consistency across rounds.

#### Data Filtering

After annotation, we applied a three-stage filtering pipeline to ensure the final dataset was both clean and diverse.

Stage 1: Deduplication. Each text and image was linked to a unique business UUID from the originating e-commerce platform. We removed duplicate entries sharing the same UUID and identical content, eliminating redundancy at the source level.

Stage 2: Clustering and Stratified Sampling. To preserve semantic diversity within each sub-domain, we used a clustering algorithm to group all text and image entries into 300 distinct categories based on content similarity. From each cluster, 20 representative samples were randomly selected, resulting in a stratified and balanced sample set.

Stage 3: Manual Quality Control. To further enhance quality, we performed ten rounds of random sampling and manual validation. In each round, 200 items from each sub-domain were reviewed by professional annotators to detect near-duplicates, poorly rendered images, or ambiguous entries. Duplicates and low-quality samples were removed through majority agreement.

This three-stage process ensures that the final benchmark is free of redundant content, balanced across domains, and reflective of the complex, real-world variation in deceptive product content. As a result, EVADE sets a new standard in dataset rigor for evaluating multimodal reasoning under regulatory constraints.

## 4 Experiment

### 4.1 Tasks

We conducted comprehensive experiments on EVADE using a suite of both open-source and closed-source LLMs and VLMs. The evaluation is structured around two core tasks: Single-Violation and All-in-One, designed to probe distinct capabilities in regulatory reasoning and content moderation.

#### Single-Violation

The Single-Violation task evaluates model performance on six distinct demanding product categories using short, domain-specific prompts aligned with regulatory rules (e.g., 202 texts and 2,134 images assessed under a 614-token prompt for body shaping).
This task tests the model’s fine-grained moderation ability within narrowly defined contexts, resembling real-world category-specific pipelines.
However, it faces two challenges: semantic overlaps between categories (e.g., “weight loss” vs. “health improvement”) and inconsistent answer choice sets, both of which can lead to ambiguous model behavior.
To simulate more realistic, policy-dense scenarios, we propose the All-in-One task.

#### All-in-One

In the All-in-One task, we unify prompts across six violation types into a single instruction, expanding input length from ~1K to 7K tokens and increasing classification labels from an average of 5 (in Single-Violation) to 26 distinct regulatory categories.
This significantly intensifies the demands on long-context reasoning. To reduce ambiguity, semantically overlapping rules are merged, enabling holistic evaluation under realistic moderation conditions where multiple violations may co-occur.
To assess model generalization in adversarial e-commerce scenarios, we define two sub-tasks: Simplified Instruction and Detailed Instruction:

- 1.
Simplified Instruction refers to the approach where, in the input prompt, we avoid introducing any examples except for the necessary special keywords.
The purpose of this approach is to allow models for free reasoning based on the prompt, in order to explore the upper and lower bounds of its performance.
- 2.
Detailed Instruction refers to the approach where, in the input prompt, we not only include the necessary special keywords but also introduce positive and negative examples.
The purpose of this approach is to constrain the model’s free-form generation through detailed examples and descriptions, thereby stabilizing its performance.

For a more detailed version of Single-Violation prompt, please refer to Appendix [C](https://arxiv.org/html/2505.17654v2#A3).

**Table 2: List of models in baselines.**
| Model Type | Baseline Models |  |  |
| --- | --- | --- | --- |
| VLMs | MiniCPM-V2.6-8B[43] | InternVL3-8B[45] | InternVL3-14B[45] |
|  | InternVL3-38B[45] | DeepSeek-VL2-27B[41] | Qwen2.5-VL-7B[5] |
|  | Qwen2.5-VL-32B[5] | Qwen2.5-VL-72B[5] | GPT-4o-0806[31] |
|  | Claude-3.7-sonnet[9] | Gemini-2.5-Pro[13] | Qwen-VL-Max[2] |
| LLMs | Qwen-2.5-7B[34] | Qwen-2.5-14B[34] | Qwen-2.5-32B[34] |
|  | Qwen-2.5-72B[34] | Qwen-3-32B[1] | Qwen-3-30B-A3B[1] |
|  | Qwen-3-235B-A22B[1] | Deepseek-V3-671B[11] | Deepseek-R1-671B[10] |
|  | Llama-3.1-8B[14] | Llama-3.1-70B[14] | GPT-o1mini-0912[29] |
|  | GPT-4.1-0414[30] | Qwen-Max |  |

### 4.2 Baselines

#### LLMs and VLMs

Table [2](https://arxiv.org/html/2505.17654v2#S4.T2) describes all the LLMs and VLMs used in our experiment. The models include both open-source and closed-source representative models. It is worth noting that due to the context length limitation of DeepSeek-VL2-27B, which can only handle 4K-length contexts, it is unable to process longer texts in the All-in-One task. Therefore, we only used DeepSeek-VL2-27B for the Single-Violation task.

All of our experiments were conducted with the temperature parameter for all LLMs and VLMs consistently set to 0.8 and We utilize APIs provided by cloud service providers.

### 4.3 Evaluation

Since the task format of EVADE involves having the model first analyze text or images, and then provide a final single-choice or multi-choice conclusion based on the multi-class rules of the prompt, we use partial accuracy and full accuracy to represent the performance of each model on EVADE. Full accuracy requires the model’s final conclusion to exactly match the ground truth, while partial accuracy requires that the model’s conclusion has at least one overlap with the ground truth.

The $\text{Acc}_{f}$ (Full Accuracy) and $\text{Acc}_{p}$ (Partial Accuracy) metrics are defined as follows:

$$ $\displaystyle\text{Acc}_{f}$ $\displaystyle=\frac{1}{N}\sum_{i=1}^{N}\mathbb{I}(C_{i}=G_{i})$ (1) $\displaystyle\text{Acc}_{p}$ $\displaystyle=\frac{1}{N}\sum_{i=1}^{N}\mathbb{I}(C_{i}\cap G_{i}\neq\varnothing)$ (2) $$

where $N$ is the total number of samples. For each sample $i$, $C_{i}$ represents the set of predicted items and $G_{i}$ represents the set of ground truth items. The term $\mathbb{I}(\cdot)$ is an indicator function that evaluates to 1 if true, and 0 otherwise.

### 4.4 Retriever-Augmented Generation

We identified Retriever-Augmented Generation (RAG) as a promising technique to improve inference in the All-in-One task. RAG enhances reasoning by retrieving semantically similar examples or contextual documents from a knowledge base, thereby enriching the model’s understanding during prediction.

To evaluate this, we partitioned the dataset into a RAG Document Store and a RAG Query Set in a 2:8 ratio, using the same prompt structure as the All-in-One setting. For textual inputs, we applied sentence-level vectorization to compute semantic similarity, while for images, we used the CLIP [35] to compute visual similarity embeddings.

## 5 Results

In this section, we provide a comprehensive comparison and analysis of the performance of all models on the EVADE. For more details on the performance of additional models across different violation categories and tasks, please refer to Appendix [D](https://arxiv.org/html/2505.17654v2#A4).

### 5.1 Single-Violation Results

In the Single-Violation task of EVADE, the performance of both LLMs and VLMs is generally suboptimal, with substantial variability across models. As shown in Table [3](https://arxiv.org/html/2505.17654v2#S5.T3) and Table [10](https://arxiv.org/html/2505.17654v2#A4.T10), the closed-source model Gemini-2.5-pro consistently achieves the highest accuracy across the six violation categories, followed closely by Qwen-VL-Max. Among open-source VLMs, Qwen2.5-VL-72B performs best, demonstrating competitive performance despite its open-source status. In contrast, Deepseek-VL2-27B shows the weakest performance, significantly lagging behind even smaller models such as the 8B-scale VLMs, highlighting inconsistencies in architecture and training quality across model families.

For LLMs on text-based inputs, the LLaMA series struggles significantly due to limited Chinese language capabilities. Even the 70B version of LLaMA performs only at par with the 7B version of the Qwen series, revealing a critical limitation in multilingual robustness for otherwise powerful models.

**Table 3: All model overall performance on Single-Violation of EVADE.**
| LLMs | Overall Parital Acc. | Overall Full Acc. | VLMs | Overall Parital Acc. | Overall Full Acc. |
| --- | --- | --- | --- | --- | --- |
| Open Source |  |  |  |  |  |
| Llama-3.1-8B | 35.62 | 20.93 | MiniCPM-V2.6-8B | 44.15 | 12.37 |
| LLama-3.1-70B | 38.55 | 26.23 | InternVL3-8B | 42.48 | 18.87 |
| Qwen-2.5-7B | 38.48 | 27.18 | InternVL3-14B | 51.20 | 23.23 |
| Qwen-2.5-14B | 45.39 | 28.70 | InternVL3-38B | 49.19 | 21.40 |
| Qwen-2.5-32B | 46.56 | 29.69 | Deepseek-VL2-27B | 29.12 | 12.24 |
| Qwen-2.5-72B | 49.21 | 27.85 | Qwen-2.5-VL-7B | 44.52 | 19.96 |
| Deepseek-V3-671B | 51.85 | 28.77 | Qwen-2.5-VL-32B | 52.39 | 22.57 |
| Deepseek-R1-671B | 54.64 | 25.45 | Qwen-2.5-VL-72B | 57.63 | 25.05 |
| Close Source |  |  |  |  |  |
| GPT-o1mini-0912 | 49.28 | 29.72 | GPT-4o-0806 | 58.47 | 26.96 |
| GPT-4.1-0414 | 52.74 | 31.59 | Claude-3.7-sonnet | 58.79 | 23.42 |
| Qwen-max | 48.29 | 31.27 | Qwen-VL-max | 53.38 | 25.60 |
|  |  |  | Gemini-2.5-pro | 52.44 | 22.14 |

**Table 4: Qwen3 series models overall performance on Single-Violation of EVADE. The numbers in parentheses on the right side of the table represent the specific performance increase or decrease after enabling the thinking mode, compared to the same model and metric on the left side.**
| Model-Flat | Overall Parital Acc. | Overall Full Acc. | Model-Think | Overall Parital Acc. | Overall Full Acc. |
| --- | --- | --- | --- | --- | --- |
| Qwen-3-32B | 48.71 | 26.16 | Qwen-3-32B(think) | 47.86(-0.85) | 24.71(-1.45) |
| Qwen-3-30B-A3B | 46.70 | 26.44 | Qwen-3-30B-A3B(think) | 47.51(+0.81) | 27.64(+1.20) |
| Qwen-3-235B-A22B | 49.77 | 27.46 | Qwen-3-235B-A22B(think) | 50.02(+0.25) | 27.50(+0.04) |

Qwen3 [1] is a newly released series of LLMs that integrates both "thinking" and "non-thinking" modes within a single architecture. However, as shown in Table [4](https://arxiv.org/html/2505.17654v2#S5.T4), the largest variant, Qwen3-235B-A22B, still underperforms relative to Deepseek-R1-671B and Deepseek-V3-671B, and shows only marginal gains over Qwen-2.5-72B. Surprisingly, enabling the thinking mode provides little to no improvement and even leads to performance regressions in models like Qwen3-32B.

A critical observation across all models is the significant gap between partial accuracy and full accuracy, often exceeding 10%. To illustrate this gap, we highlight Claude-3.7-sonnet (Figure [4j](https://arxiv.org/html/2505.17654v2#A4.F4.sf10)) as the best-performing closed-source model and Qwen2.5-VL-72B (Figure [4h](https://arxiv.org/html/2505.17654v2#A4.F4.sf8)) as the strongest open-source VLM. This discrepancy underscores the difficulty models face in achieving complete understanding: while they may capture partial intent or surface-level features, they frequently miss critical semantic or visual details, especially in cases involving metaphorical language, euphemisms, or evasive imagery designed to circumvent moderation.

Furthermore, although closed-source models generally outperform open-source models, exceptions do exist. For instance, Deepseek-R1 shows strong results in text understanding, even outperforming several closed models, while Qwen2.5-VL-72B demonstrates robust visual reasoning despite not matching the top-tier performance of Gemini-2.5-pro or Qwen-VL-Max.

To better understand how models behave under longer context and clearer taxonomy, where ambiguous overlaps between categories are removed, we turn to the results from the All-in-One task.

### 5.2 All-in-One Results

In the All-in-One task, we observe substantial performance improvements across many models, especially for smaller LLMs and VLMs. Without altering the input data, simply merging overlapping violation categories into a unified prompt significantly enhances reasoning capabilities. Despite the prompt length increasing sixfold compared to the Single-Violation setting, and the number of violation categories expanding from a few to 26, models generally perform better rather than worse.

Notably, the performance disparity among LLMs narrows in All-in-One. Additionally, the difference between partial and full accuracy shrinks dramatically, from over 10% in Single-Violation to approximately 5% in All-in-One.

This suggests that clearer category boundaries yield significantly greater improvements in model reasoning performance—particularly in full accuracy metric—than simply increasing prompt complexity. Even when both the input prompt length and the number of categories are scaled up by several times, models still benefit substantially from clearer semantic distinctions. In other words, among the factors that constrain model reasoning, semantic ambiguity poses a more fundamental limitation than either prompt context length or the number of categories.

Interestingly, while high-performing LLMs—such as Deepseek-R1, Deepseek-V3, and the GPT series—can still achieve notable improvements in full accuracy, their gains in partial accuracy under the all-in-one setting are relatively modest. This may be due to ceiling effects or instruction-following saturation that limit further improvements on easier subtasks. In contrast, smaller models benefit substantially across both metrics. For example, Qwen2.5-7B improves its partial accuracy from 38.48% to 53.18% and full accuracy from 27.18% to 50.60%, with over 10% gains in both. Even Llama-3.1-70B, despite being less adept at Chinese, improves from 38.55% to 47.00% in partial accuracy, and from 26.23% to 44.60% in full accuracy.

**Table 5: The performance of all models on the All-in-One task of EVADE. The values to the left of the slash indicate partial accuracy, while those to the right indicate full accuracy.**
| LLMs | Simplified. | Detailed. | VLMs | Simplified. | Detailed. |
| --- | --- | --- | --- | --- | --- |
| Llama-3.1-8B | 41.94 / 36.47 | 37.84 / 34.44 | MiniCPM-V2.6-8B | 46.70 / 43.22 | 39.60 / 36.93 |
| LLama-3.1-70B | 47.81 / 44.53 | 47.00 / 44.60 | InternVL3-8B | 56.26 / 53.90 | 56.97 / 54.17 |
| Qwen-2.5-7B | 51.68 / 49.52 | 53.18 / 50.60 | InternVL3-14B | 60.63 / 56.83 | 60.67 / 57.41 |
| Qwen-2.5-14B | 53.14 / 48.32 | 55.23 / 51.20 | InternVL3-38B | 60.62 / 57.35 | 61.36 / 58.34 |
| Qwen-2.5-32B | 55.12 / 51.20 | 53.55 / 49.95 | Qwen-2.5-VL-7B | 53.15 / 51.49 | 53.39 / 51.20 |
| Qwen-2.5-72B | 56.93 / 51.06 | 55.59 / 50.05 | Qwen-2.5-VL-32B | 62.04 / 58.55 | 61.73 / 59.17 |
| Deepseek-V3-671B | 56.58 / 50.62 | 58.79 / 52.65 | Qwen-2.5-VL-72B | 64.25 / 59.09 | 63.85 / 59.44 |
| Deepseek-R1-671B | 58.25 / 49.35 | 58.69 / 50.37 | GPT-4o-0806 | 64.14 / 58.05 | 65.12 / 59.58 |
| GPT-o1mini-0912 | 56.69 / 51.15 | 54.17 / 48.76 | Claude-3.7-sonnet | 64.58 / 56.83 | 63.05 / 56.05 |
| GPT-4.1-0414 | 59.16 / 53.79 | 59.64 / 53.67 | Qwen-VL-max | 63.58 / 59.24 | 63.31 / 59.50 |
| Qwen-max | 58.35 / 54.68 | 56.48 / 52.88 | Gemini-2.5-pro | 70.57 / 54.45 | 70.43 / 51.94 |

**Table 6: Qwen3 series models overall performance on All-in-One of EVADE. The values to the left of the slash indicate partial accuracy, while those to the right indicate full accuracy.**
| Model-Flat | Simplified. | Detailed. | Model-Think | Simplified. | Detailed. |
| --- | --- | --- | --- | --- | --- |
| Qwen-3-32B | 57.11 / 52.84 | 55.54 / 50.71 | Qwen-3-32B(think) | 58.56 / 53.44 | 55.94 / 50.73 |
| Qwen-3-30B-A3B | 55.07 / 50.58 | 54.39 / 50.55 | Qwen-3-30B-A3B(think) | 56.64 / 52.44 | 56.74 / 52.75 |
| Qwen-3-235B-A22B | 55.91 / 50.97 | 55.81 / 51.39 | Qwen-3-235B-A22B(think) | 57.11 / 51.85 | 57.26 / 52.77 |

This trend also extends to VLMs. Smaller models like InternVL3-8B and MiniCPM-V2.6-8B show dramatic improvements, while large-scale models still achieve further gains, albeit at a reduced margin. These results affirm the effectiveness of the All-in-One setting in reducing confusion caused by overlapping policy categories and reveal the potential of structured prompt engineering for regulatory tasks.

Compared to its performance in previous tasks, Qwen3 shows improved results in the All-in-One setting—both in Simplified and Detailed Tasks—when the thinking mode is enabled. However, the improvement is minor, not exceeding 2%.

### 5.3 Analysis on the Effect of RAG

Due to resource constraints and the general inability of small-scale VLMs to handle long text inputs—thus preventing effective few-shot prompting—we conducted RAG experiments on VLMs exclusively using the InternVL series models, which demonstrated strong performance in both the Single-Violation and All-in-One tasks.

Figure: Figure 2: Comparison of LLMs and VLMs before and after the introduction of RAG. Here, L- denotes the Llama-3.1 model, Q- denotes the Qwen2.5 model, DS- denotes the Deepseek model, Q3- denotes the Qwen3 model, and IVL- denotes the InternVL3 model.
Refer to caption: extracted/6525718/figures/chart/custom/multiple_lines_accuracy_chart.png

We then integrated top-ranked retrievals into the model’s input and evaluated performance using an open-source LLM–VLM pair. Our preliminary findings indicate that RAG improves the model’s precision, especially in cases involving ambiguous or metaphor-laden inputs, by providing semantically aligned reference materials. These results highlight the potential of retrieval-based augmentation for boosting model robustness in complex regulatory reasoning tasks.

### 5.4 Error Analysis

Merchants may embed neutral, everyday language into product descriptions or overlay benign text within promotional images. While Large Language Models and Vision–Language Models can sometimes identify policy-relevant keywords, the introduction of such common terms frequently misleads the model, causing it to misclassify deceptive content as benign. This reflects a broader issue in contextual reasoning: models often lack sufficient sensitivity to metaphors, euphemisms, and dual meanings—leading to failures in detecting layered or implied semantic cues within multimodal inputs.

Vision–Language Models often fail to accurately recognize embedded text in product images, especially when obscured by noise, masking, or creative formatting. Even with correct region detection, they may misread or miss content, which may lead to undetected policy violations. This underscores the need for better OCR and text-visual alignment in commercial applications.

A recurring failure mode in Vision–Language Models is their limited ability to fully and accurately recognize embedded text within product images. This is particularly problematic when key information is obscured through visual noise, deliberate masking, or creative formatting by merchants. Even when a VLM correctly identifies text regions, it may misrecognize the characters or extract incomplete content, leading to undetected policy violations. This highlights the urgent need for improved OCR and multimodal text-visual alignment in commercial VLM deployments.

Although EVADE is a multi-class task, it includes an implicit rule: "Z. other" (no violation) cannot be selected alongside any regulated category. Some weaker models violate this constraint by labeling items as both compliant and non-compliant, revealing poor rule comprehension and instruction-following under constraints.

For more specific bad cases, please refer to Appendix [E](https://arxiv.org/html/2505.17654v2#A5).

## 6 Conclusion

EVADE is the first Chinese-language multimodal benchmark for detecting evasive e-commerce content, covering expert-annotated texts and images across six demanding categories. Evaluation of 26 LLMs and VLMs reveals significant performance disparities, with closed-source models generally outperforming open-source ones.
In the All-in-One task, merging overlapping rules enhances reasoning and reduces the accuracy gap, particularly in smaller models—underscoring the importance of rule clarity over model’s performance. Common failure modes include contextual noise, obfuscated language, and OCR errors, indicating weaknesses in semantic understanding and vision–language alignment.
Contemporary LLMs and VLMs still fall short on the EVADE benchmark. The introduction of EVADE is therefore poised to catalyze progress in this area. In our experiments, leveraging RAG to supply the models with under-represented knowledge has yielded significant performance improvements, attesting to the high quality of the EVADE.
Looking forward, EVADE aims to support multilingual and multi-domain content, strengthen OCR pipelines, integrate retriever-augmented generation, and incorporate adversarial data for stress-testing generalization. Overall, EVADE offers a rigorous foundation for evaluating and advancing multimodal moderation systems, highlighting key bottlenecks and design principles—such as clearer taxonomy, retrieval augmentation, and robust error handling—for building safer, more reliable AI systems.

## Limitations

We acknowledge several limitations in our study. In real-world e-commerce scenarios, the challenges faced by LLMs and VLMs extend beyond the six categories defined in our proposed EVADE benchmark. Other categories such as gambling, pornography, and terrorism-related content also pose significant violations. If models fail to detect such content, it may result in serious physical and psychological harm to minors. This further underscores the necessity of continuously improving EVADE. We aim to expand and refine the EVADE benchmark to enhance the performance of VLMs and LLMs on the task of Evasive Content Detection.

## Ethics Statement

During the development of the EVADE, we strictly adhered to ethical guidelines and legal regulations, ensuring fairness, transparency, inclusivity, and respect for all stakeholders. We emphasized the importance of protecting privacy and intellectual property, reaffirming our commitment to responsible and lawful data management. The EVADE may contain expressions and visual materials influenced by objective factors such as the time of collection, cultural context, and business scenarios.
EVADE may also be used for model training, making ECD tasks more challenging for both LLMs and VLMs. At the same time, the ability to successfully evade model auditing poses increasing violations to the fairness of online transactions, potentially leading to legal and economic consequences.
These representations and viewpoints do not reflect the value orientation of the data providers. We are committed to ongoing monitoring and refinement to mitigate such biases. Furthermore, we encourage users of the dataset to exercise responsible use and to consider the ethical implications of their work, particularly in applications that may affect individuals or communities.

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## Appendix A The statistics of erroneous data

During the All-in-One experiment, due to the limitations of the model’s capabilities, various types of errors—such as those documented in Section [5.4](https://arxiv.org/html/2505.17654v2#S5.SS4)—may occur. The proportion of each specific error type relative to the total number of errors is as follows:

**Table 7: Error rate in the experiment.**
| Model | Error Rate | Model | Error Rate | Model | Error Rate |
| --- | --- | --- | --- | --- | --- |
| VLMs |  |  |  |  |  |
| MiniCPM-V2.6-8B | 0.03 | InternVL3-8B | 0 | InternVL3-14B | 0 |
| InternVL3-38B | 0 | Qwen2.5-VL-7B | 0 | Qwen2.5-VL-32B | 0.01 |
| Qwen2.5-VL-72B | 0 | GPT-4o-0806 | 0 | Claude-3.7-sonnet | 0 |
| Qwen-VL-max | 0 | Gemini-2.5-pro | 0.02 |  |  |
| LLMs |  |  |  |  |  |
| LLama-3.1-8B | 0.14 | Llama-3.1-70B | 0 | Qwen2.5-7B | 0 |
| Qwen2.5-14B | 0 | Qwen2.5-32B | 0 | Qwen2.5-72B | 0 |
| Deepseek-V3-671B | 0 | Deepseek-R1-671B | 0 | GPT-o1mini | 0 |
| GPT-4.1-0414 | 0 | Qwen-max | 0 | Qwen-3-32B | 0 / 0 |
| Qwen-3-30B-A3B | 0 / 0 | Qwen-3-235B-A22B | 0 / 0 |  |  |

The Error Rate refers to the proportion of responses in which the model either returns answers in an incorrect format or refuses to answer altogether due to insufficient understanding of the prompt instructions, relative to the total number of instances in the EVADE benchmark. For the Qwen3 model, the value to the left of the slash indicates the error rate in the non-thinking mode, while the value to the right corresponds to the thinking mode. Overall, the number of erroneous or rejected responses is extremely low.
Given the scale of the EVADE dataset, an error rate of less than 1% has a negligible impact on the validity of the experimental results.

## Appendix B Extended Discussion of Experimental

The results of EVADE reveal critical insights into the limitations of current Large Language Models and Vision–Language Models in regulatory reasoning tasks. Despite impressive progress in general-purpose language and vision–language understanding, these models exhibit persistent weaknesses when applied to demanding, multimodal content moderation scenarios that involve implicit semantics, fine-grained policies, and adversarial manipulation.

One of the most important observations is that model performance is not solely determined by size or pretraining scale. Instead, the structure and clarity of task prompts have a substantial influence on accuracy and consistency. This is particularly evident in the All-in-One task, where merging semantically overlapping classification rules significantly improved model performance, especially for smaller models. The improvement was not merely due to exposure to longer contexts but was driven by reduced ambiguity and more coherent rule definitions. These findings indicate that prompt semantics, specifically the logical framing of classification criteria—play a crucial role in aligning model predictions with human judgment. Future research should explore structured, rule-aware prompting strategies that incorporate regulatory ontologies or logical trees to further enhance reasoning fidelity.

Another key insight from our analysis is that many errors observed in Vision–Language Models stem not from visual recognition failures but from grounding failures. That is, while models often identify relevant visual or textual features, they struggle to associate these features with regulatory meanings, particularly when violations are expressed metaphorically or obfuscated through formatting tricks. This underscores the need for models that can reason beyond surface-level detection and incorporate deeper understanding of compliance rules. Architectural innovations that combine symbolic rule reasoning, regulatory knowledge graphs, or hybrid neural–symbolic inference mechanisms may offer pathways forward.

The gap between partial and full accuracy across models also highlights limitations in decision-making certainty. Many models partially identify violation signals but fail to commit to correct classifications, especially under ambiguous or overlapping category definitions. In practice, this indecision can lead to significant compliance failures, either by allowing deceptive content to bypass moderation or by wrongly penalizing compliant material. Therefore, future benchmarks and model evaluation protocols should move beyond binary accuracy and incorporate measures of prediction confidence, rationale attribution, and rule alignment to assess decision reliability under regulatory conditions.

The integration of the RAG in our experiments further emphasizes the complexity of bridging external knowledge with generation. While we observed modest performance gains in the All-in-One task through RAG, these gains were highly variable and contingent on the precision and contextual relevance of retrieved examples. For RAG to effectively support regulatory reasoning, it must be paired with well-curated, policy-aligned document stores and robust similarity matching mechanisms, particularly for ambiguous or novel product contexts. This suggests the need for future research into adaptive retrieval pipelines that dynamically update with evolving compliance standards.

Importantly, the challenges surfaced by EVADE carry immediate implications for the deployment of AI moderation systems in e-commerce platforms such as Taobao, JD.com, and Pinduoduo. These platforms operate under strict regulatory oversight, and any failure to detect prohibited content, whether due to linguistic obfuscation, visual manipulation, or contextual ambiguity, can result in substantial legal and reputational consequences. The benchmark shows that even the most advanced models are susceptible to surprisingly simple evasive tactics, reinforcing the necessity of combining automated systems with human-in-the-loop verification and adversarial retraining pipelines.

Taken together, these findings point to a larger paradigm shift required in AI model design for regulatory tasks. Future systems must move beyond general-purpose reasoning toward domain-specific, interpretable, and policy-compliant intelligence. EVADE offers not just a dataset, but a framework for probing the brittleness of current models and guiding the development of the next generation of trustworthy multimodal moderation systems.

## Appendix C Single-Violation prompts

The following are the audit rules for Height Growth and Weight Loss and Health Supplement products in Single violation, where option "Z" represents no need for control, and all other options represent different types that need to be controlled. For more review rule prompts, please refer to our Huggingface dataset.

**Table 8: The prompt of Height Growth and Weight Loss products in Single violation.**
| Data Type | Chinese Prompt |
| --- | --- |
| Height Growth | # 任务描述<br>请你根据给定信息和关键词，仔细判断对应的管控类型。给出最终的选项（一个到多个不等）。都不相关返回 Z.其他。<br># 管控类型<br>A. 增高功效直接描述：明确使用以下词汇描述长高发育：增高、长高、长个、蹭蹭长/涨、xx岁还能长、（身高）用了高/吃了高/喝了高/贴了高、吃出/喝出/补出/贴出高个子、描述商品使用前和使用后身高差距。注意：若涉及未成年人进行此类表达，则不管控，其中未成年人相关描述包括但不限于：孩子、儿童、宝宝、青少年、发育、青春期、同龄、成长缓慢、小于等于18岁、出现未成年人的形象、身穿校服等。<br>B. 个子矮小描述：包括但不限于：矮个子专用、适合矮个子、告别矮小、对矮小说不/say no/说拜拜/say goodbye、不做矮个子/矮XX、不再矮、X年不长、身高不够、矮小、身高+数字、不足+数字、数字+以下等间接表达身高矮小。<br>C. 增高隐晦描述：包括但不限于使用以下词汇来暗示身高增长：腰以下全是腿、吃出大长腿、个头/身高不掉队。<br>D. 磁石矫形鞋垫：包括但不限于使用以下词汇来描述身高增高、腿型矫正：鞋垫+磁石/磁疗/火疗；鞋垫+矫正/矫形/纠正/X型腿/O型腿/罗圈腿/腿直了；带有黑色磁石的鞋垫。<br>E. 反科学增高增高：使用如下违反科学原理的方式来增长身高：打开骨闭合/骨骼闭合、突破遗传、二次发育/破高。<br>F. 增高精准化功效承诺：使用数字/时间/百分数来承诺使用商品后能达到确定的增高效果(例如一喝长高、一粒长高、不管多矮，只用3支、一周长高、100%长高、睡前涂，睡醒长)。<br>Z. 其他：当给定信息与上述选项均无明显关联时选择此项。 |
| Weight Loss | # 任务描述<br>请你根据给定信息和关键词，仔细判断对应的管控类型。给出最终的选项（一个到多个不等）。都不相关返回 Z.其他。<br># 管控类型<br>A. 特定减肥商品：包括以下商品：星空咖啡、韩国星空咖啡、泰国果蔬 slim7day局部减肥药、slim7day、无限畅、纤so、纤姿、司美格鲁肽、司美、口服司美、利拉鲁肽、索马鲁肽、格鲁肽、glp-1。<br>B. 减肥夸大描述：内容使用极端用词来承诺商品使用后的减肥效果，包括但不限于“骨瘦如柴”、“皮包骨”、“狂吃不胖/涨”、“脂肪收割机/炸弹”、“急吃/喝急瘦”、“终身/一辈子/一生不长肉”等夸大描述。使用精准数字或者时间来承诺商品使用后的减肥效果，包括但不限于“x天瘦x斤(J)/kg”、“瘦x斤(J)/kg”、“xx斤(J)/kg保底”、“排x斤(J)/kg+油/汗/便”、“挑战x/x斤(J)/kg”等量化承诺。内容将减肥前的状态（如“体重超标”、“体重难下/不下”、“肚子大”、“不掉称”）与具体数字和量词直接关联，以误导消费者对减肥效果的预期。<br>C. 减肥直接描述：内容直接描述身体体重、体脂的下降、身体由胖到瘦描述，包括但不限于使用特定动词（抽、清、抗、吸、溶、排、去/祛、减、分解、燃烧/燃、绝、甩等）、效果形容词（没了、小了、细了、不见了、下降/降了、少了、掉了/掉秤、轻了、不反弹/回弹、管用/有用等）和”脂/脂肪/油/肉/肥/胖/重“结合，来直接描述减肥效果。或者直接描述身体部位（如肩部、颈部、背部、手臂/胳膊、腹部/肚子/肚肚/肚腩、腰部、大腿、小腿、肉肉）变瘦。注意：若明确与减肥无关则不管控，例如“减脂期”。<br>D. 减肥隐晦描述：内容暗示产品或服务可以替代传统减肥方法（如节食、运动、手术、左旋肉碱等其他减肥产品）；内容声称能够提供饱腹感、抑制食欲或控制饮食的描述；特殊减肥效果暗示描述，例如：不要溜肩、天鹅臂、蝴蝶肩、小蛮腰/腰精/a4腰、圆腰克星、腰细背薄、大象腿、裤子往下滑、摆脱厚臂、远离肩厚、厚臂不见了、肩不厚了、远离拜拜肉、不要蝴蝶袖、裤子松了、换裤子/裤头、理想体重、消腩、缩腹、soso好身材、平小腹、骨感美、摆脱大码、身材自信/好身材/好身姿、拯救身材、胖子逆袭、体重不过百、体重两位数、体重重回xx、燃烧卡路里、胖子有救了。<br>Z. 其他：当给定信息与上述选项均无明显关联时选择此项。 |

**Table 9: The prompt of Height Growth and Weight Loss products in Single violation.**
| Data Type | Chinese Prompt |
| --- | --- |
| Health Supplement | # 任务描述<br>请你根据给定信息和关键词，仔细判断对应的管控类型。给出最终的选项（一个到多个不等）。都不相关返回 Z.其他。<br># 管控类型<br>- A.甲乙丙类传染病<br>- B.心脏类疾病：涉及心脏相关的疾病问题（包含心梗、心绞、冠心、心率、心力、血栓、心慌、心悸、胸闷等），同时还包含血管硬化、血管堵塞、血粘稠等血管血液的疾病问题和因此衍生的各种治疗用语与治疗手段。<br>- C.严重神经系统疾病：仅涉及神经系统相关的脑卒中、老年痴呆、中风、脑膜炎、脑梗、脑萎缩这几种严重疾病和因此衍生的各种治疗用语与治疗手段。<br>- D.严重内分泌疾病：仅涉及糖尿病酮症酸中毒、甲亢、甲减、桥本甲状腺等严重疾病和因此衍生的各种治疗用语与治疗手段。<br>- E.严重创伤和烧伤：仅涉及多发性创伤、大面积烧伤和因此衍生的各种治疗用语与治疗手段。<br>- F.生育类疾病：仅涉及生育的疾病问题（如不孕、不育、多囊卵巢综合征、精子畸形等）和因此衍生的各种治疗用语与治疗手段。<br>- G.癌症肿瘤类疾病：涉及人体各个部位的瘤、肿瘤、癌症、癌变、癌扩散等疾病和因此衍生的各种治疗用语与治疗手段。<br>- H.严重精神类疾病：仅涉及重度抑郁症、精神分裂等严重疾病和因此衍生的各种治疗用语与治疗手段。<br>- I.结石结节类疾病+通用治疗效果：涉及人体结石或结节类疾病问题的同时，还需要出现通用治疗效果词。比如仅描述“肾结石”不属于该分类，但既描述“肾结石”又提及“清除”的字眼，就属于该分类。<br>- J.痔疮类疾病+通用治疗效果：涉及人体痔疮类疾病问题的同时，还需要出现通用治疗效果词。比如仅描述“小肉球”不属于该分类，但既描述“小肉球”又提及“根治”的字眼，就属于该分类。<br>- K.生殖器官疾病+通用治疗效果：涉及男性生殖器官疾病或女性生殖器官疾病的同时，还需要出现通用治疗效果词。比如仅描述“尿道”不属于该分类，但既描述“尿道”又提及“突破”的字眼，就属于该分类。它与上面生育类疾病的区别在于，生殖器官疾病不包含生育问题。<br>- L.幽门螺旋杆菌+通用治疗效果：涉及幽门螺旋杆菌疾病问题的同时，还需要出现通用治疗效果词。比如仅描述“幽”不属于该分类，但既描述“幽”又提及“清”或“去”等字眼，就属于该分类。<br>- M.五官类疾病：涉及牙齿、耳朵、鼻子、头发、眼睛等人体五官的疾病问题和因此衍生的各种治疗用语与治疗手段。<br>- N.三高类疾病：涉及血压、血脂、血糖等疾病问题和因此衍生的各种治疗用语与治疗手段。<br>- O:肺部呼吸类疾病：涉及呼吸窘迫综合症ARDS、肺炎、肺病COPD、以及所有人体肺部器官的疾病问题和因此衍生的各种治疗用语与治疗手段。<br>- P.胃部消化类疾病：涉及急性胰腺炎、消化道出血、以及所有人体胃部器官的疾病问题和因此衍生的各种治疗用语与治疗手段。<br>- Q.肝部类疾病：涉及肝衰竭、肝硬化、以及所有肝部器官的疾病问题和因此衍生的各种治疗用语与治疗手段。<br>- R.关节类疾病：涉及关节疼痛、增生、麻木、畸形、肿痛、尿酸等体现在关节上的疾病和因此衍生的各种治疗用语与治疗手段。<br>- S.手脚皮肤类疾病：涉及指甲、灰甲、空甲、脚气、手脚糜烂、水泡、藓类、瘊疣类、湿疹类、抑菌止痒类、狐臭类疾病问题和因此衍生的各种治疗用语与治疗手段。<br>- T.轻度疾病：涉及感冒、鼻炎、焦虑症、失眠、睡不着、深睡等一系列睡眠疾病问题和因此衍生的各种治疗用语与治疗手段。<br>- U.医用级描述：包含“医用级”、“医疗级”、“治愈”、“疗效”、“神药”、“奇效”、“专用药”等含义的内容，比如“瘙痒特效药”、“专攻哮喘”、“血糖百试百灵”等宣传描述。<br>- V.代替医疗类：包含“取代”、“比*好用”、“告别”等表达，并提及以下药品名称(他汀、双胍、4联/四联、甲钻胺、地屈孕酮、非布司他、二胍、黄体酮、所有的药和所有的疫苗)或医疗手段(手术、开刀、打针、注射、拉皮、拍CT、戴助听、做耳蜗、复查、胃镜、医美、所有的针（如水光针、美白针）)，比如仅描述“疫苗”不属于该分类，但既描述“疫苗”又提及“吊打”的字眼，就属于该分类。<br>- W.代替医美类：包含“填充”、“膨胀”、“丰满”、“挺起”、“缩小”等形状变化的表达，同时需要提及以下身体部位(胸部、鼻子、眼睛)，比如仅描述“胸”不属于该分类，但既描述“挺”又提及“胸”的字眼，就属于该分类。<br>- X.无副作用类描述：包含“无副”、“无副作用”、“无风险”、“零风险”等表达商品无副作用和风险的内容，或者包含“无药”、“无药物残留”、“无毒”、“0化学”、“0农药”等表达商品无化学或农药添加剂的内容，或者包含“安全”、“安全性高”、“副作用小”等表达商品使用风险低的内容。<br>- Z. 其他：当给定信息与上述选项均无明显关联时选择此项。<br># 注意事项<br>1. 谨慎判断:若无法正确识别，可能会加重患者病情或导致其他严重后果，请务必谨慎、细致地判断。<br>2. 权威引用:若文本明确引用百度百科、搜狗百科、维基百科、书籍内容或书名等权威信息来源，判断为“不相关”返回Z.其他。<br>3. 科普内容:若文本仅为科普（如书籍、电影等），且未出现具体商品名称，判断为“不相关”返回Z.其他。<br>4. 患病人群描述:若文本仅描述患病人群（如描述艾滋病人、心脏病患者的书籍或活动），判断为“不相关”返回Z.其他。<br>5. 通用治疗效果:文本可能会出现通用治疗效果相关的词汇，这些效果表现类词汇主要用于描述事物在功能或状态上的变化，突出其带来的积极效果。它们可以分为多个类别:上升类词汇如“拯救”、“改善”、“提高”、“增强”、“加强”、“提升”等，强调对某种能力或状态的提升和优化；下降类词汇如“抗”、“抑制”、“降”、“镇”、“缓解”、“控制”等，侧重于减少、不良因素的抑制或控制；消除类词汇如“告别”、“根治”、“消失”、“彻底”、“治愈”、“摆脱”、“解决”等，强调对问题或障碍的彻底消除和根除；用量类词汇如“仅需”、“只要”、“只需”等，突出使用的简便性和低成本。此外，其他词汇如“有效率”、“治愈率”等，强调效果的高效性和成功率。这些词汇的共同特征在于通过具体的动词和形容词，传达出显著的效果和优势。 |

## Appendix D The performance of all models on the EVADE

**Table 10: VLMs Main results on Single-Violation task. The values to the left of the slash indicate partial accuracy, while those to the right indicate full accuracy.**
|  | Overall | Body. | Women. | Height. | Men. | Weight. | Health. |
| --- | --- | --- | --- | --- | --- | --- | --- |
| Open-source Models |  |  |  |  |  |  |  |
| MiniCPM-V2.6-8B | 44.15 / 12.37 | 44.85 / 20.15 | 52.66 / 10.42 | 58.71 / 2.13 | 43.96 / 18.81 | 55.36 / 2.58 | 26.01 / 17.54 |
| InternVL3-8B | 42.48 / 18.87 | 40.82 / 15.70 | 49.03 / 33.59 | 45.37 / 10.74 | 48.33 / 28.48 | 53.37 / 2.66 | 33.33 / 23.28 |
| InternVL3-14B | 51.20 / 23.23 | 48.59 / 19.63 | 53.67 / 39.15 | 61.92 / 22.38 | 55.06 / 27.91 | 60.85 / 2.83 | 38.54 / 24.77 |
| InternVL3-38B | 49.19 / 21.40 | 49.81 / 17.53 | 56.14 / 40.46 | 55.73 / 14.09 | 53.62 / 32.51 | 57.02 / 1.16 | 37.22 / 24.67 |
| Deepseek-VL2-27B | 29.12 / 12.24 | 36.46 / 16.68 | 38.15 / 25.17 | 34.34 / 8.75 | 38.32 / 21.63 | 43.72 / 4.07 | 10.22 / 7.25 |
| Qwen2.5-VL-7B | 44.52 / 19.96 | 34.77 / 9.84 | 47.72 / 33.82 | 50.04 / 12.46 | 54.20 / 29.80 | 49.71 / 1.66 | 38.44 / 28.17 |
| Qwen2.5-VL-32B | 52.39 / 22.57 | 56.47 / 21.09 | 57.07 / 39.00 | 55.21 / 16.49 | 53.11 / 28.60 | 69.66 / 4.74 | 41.25 / 25.87 |
| Qwen2.5-VL-72B | 57.63 / 26.05 | 57.78 / 20.81 | 61.93 / 42.08 | 67.70 / 26.64 | 55.87 / 32.11 | 72.57 / 2.99 | 44.35 / 27.38 |
| Close-source Models |  |  |  |  |  |  |  |
| GPT-4o-0806 | 58.47 / 26.96 | 70.29 / 27.79 | 61.16 / 43.32 | 64.69 / 24.16 | 55.58 / 37.11 | 75.23 / 4.57 | 42.84 / 25.97 |
| Claude-3.7-sonnet | 58.79 / 23.42 | 75.59 / 29.57 | 55.75 / 35.68 | 66.68 / 19.61 | 52.70 / 32.91 | 73.65 / 3.24 | 42.88 / 21.48 |
| Gemini-2.5-pro | 52.44 / 22.14 | 75.96 / 30.27 | 61.54 / 39.69 | 67.96 / 24.86 | 57.36 / 32.39 | 86.78 / 14.71 | 12.84 / 8.14 |
| Qwen-VL-Max | 53.38 / 25.60 | 55.62 / 29.80 | 64.17 / 49.11 | 60.52 / 19.11 | 59.15 / 39.36 | 55.20 / 1.66 | 40.08 / 22.65 |

**Table 11: LLMs Main results on Single-Violation task. The values to the left of the slash indicate partial accuracy, while those to the right indicate full accuracy.**
|  | Overall | Body. | Women. | Height. | Men. | Weight. | Health. |
| --- | --- | --- | --- | --- | --- | --- | --- |
| Open-source Models |  |  |  |  |  |  |  |
| Llama-3.1-8B | 35.62 / 20.93 | 64.85 / 62.38 | 68.25 / 49.29 | 33.82 / 11.03 | 37.27 / 26.38 | 38.01 / 10.86 | 17.59 / 10.61 |
| Llama-3.1-70B | 38.55 / 26.23 | 51.49 / 48.51 | 49.29 / 47.39 | 25.50 / 11.03 | 46.01 / 35.12 | 52.94 / 22.85 | 27.04 / 19.92 |
| Qwen-2.5-7B | 38.48 / 27.18 | 25.25 / 22.77 | 40.28 / 36.02 | 41.41 / 24.59 | 54.60 / 46.32 | 34.39 / 12.22 | 28.07 / 20.18 |
| Qwen-2.5-14B | 45.39 / 28.70 | 41.58 / 38.12 | 51.18 / 41.71 | 47.92 / 29.84 | 50.61 / 37.42 | 58.37 / 21.95 | 31.18 / 18.37 |
| Qwen-2.5-32B | 46.56 / 29.69 | 58.42 / 55.45 | 65.40 / 48.34 | 30.92 / 12.12 | 56.75 / 45.09 | 56.33 / 16.06 | 35.32 / 25.23 |
| Qwen-2.5-72B | 49.21 / 27.85 | 59.41 / 51.49 | 65.40 / 35.07 | 41.77 / 24.41 | 57.21 / 41.87 | 61.09 / 16.06 | 33.89 / 17.08 |
| Qwen-3-32B | 48.71 / 26.16 | 61.88 / 55.94 | 63.98 / 37.91 | 37.61 / 14.29 | 52.91 / 36.20 | 63.57 / 21.72 | 37.00 / 17.72 |
| Qwen-3-32B(think) | 47.86 / 24.71 | 60.40 / 54.46 | 69.19 / 41.71 | 36.89 / 13.56 | 51.38 / 32.98 | 63.12 / 19.91 | 34.93 / 16.04 |
| Qwen-3-30B-A3B | 46.70 / 26.44 | 47.03 / 42.08 | 63.51 / 44.55 | 41.95 / 19.53 | 51.38 / 37.73 | 59.28 / 15.16 | 34.28 / 19.28 |
| Qwen-3-30B-A3B(think) | 47.51 / 27.64 | 40.10 / 37.13 | 63.98 / 45.02 | 43.94 / 22.42 | 52.76 / 40.49 | 59.95 / 15.84 | 35.96 / 20.05 |
| Qwen-3-235B-A22B | 49.77 / 27.46 | 64.85 / 60.89 | 70.62 / 48.34 | 39.96 / 15.91 | 56.29 / 38.34 | 68.55 / 22.62 | 30.92 / 14.88 |
| Qwen-3-235B-A22B(think) | 50.02 / 27.50 | 62.38 / 56.93 | 70.14 / 45.97 | 41.41 / 15.55 | 56.90 / 39.88 | 65.84 / 22.62 | 32.60 / 15.65 |
| Deepseek-V3-671B | 51.85 / 28.77 | 69.31 / 63.86 | 70.62 / 42.65 | 51.18 / 26.76 | 55.06 / 40.18 | 67.42 / 15.84 | 31.05 / 15.01 |
| Deepseek-R1-671B(think) | 54.64 / 25.45 | 80.69 / 71.78 | 78.20 / 40.28 | 44.12 / 15.37 | 54.91 / 33.44 | 79.19 / 19.23 | 34.67 / 13.32 |
| Close-source Models |  |  |  |  |  |  |  |
| GPT-o1mini-0912 | 49.28 / 29.72 | 63.86 / 56.93 | 69.67 / 65.88 | 50.63 / 28.21 | 50.92 / 34.36 | 60.63 / 20.36 | 31.05 / 15.27 |
| GPT-4.1-0414 | 52.74 / 31.59 | 61.88 / 52.48 | 72.99 / 54.98 | 43.22 / 20.98 | 58.90 / 46.32 | 71.72 / 24.66 | 35.58 / 18.89 |
| Qwen-Max | 48.29 / 31.27 | 63.86 / 58.42 | 68.25 / 54.03 | 41.59 / 25.14 | 58.59 / 47.09 | 56.56 / 16.97 | 30.14 / 17.21 |

Figure: (a) Llama-3.1-8B on Single-Violation
Refer to caption: extracted/6525718/figures/chart/bar/Llama-3.1-8B-bar.png

Figure: (a) MiniCPM-V2.6-8B on Single-Violation
Refer to caption: extracted/6525718/figures/chart/radar/MiniCPM-V2.6-8B-radar.png

Figure: (a) Qwen-3-32B on Single-Violation
Refer to caption: extracted/6525718/figures/chart/bar/Qwen-3-32B-bar.png

## Appendix E Bad Case

Figure: Figure 6: Bad case from All-in-One task.
Refer to caption: x2.png

Figure: Figure 7: Bad case from All-in-One task.
Refer to caption: x3.png

Figure: Figure 8: Bad case from All-in-One task.
Refer to caption: x4.png

Figure: Figure 9: Bad case from All-in-One task.
Refer to caption: x5.png

Figure: Figure 10: Bad case from All-in-One task.
Refer to caption: x6.png

Figure: Figure 11: Bad case from All-in-One task.
Refer to caption: x7.png

## Peer Reviews

confidence: 4
contribution: 3
initial_rating: 6
presentation: 3
questions: See the above 'Weaknesses'.
soundness: 3
strengths: 1. The paper is well written and easy to follow.
2. The task of evasive content detection is meaningful for many real-world scenarios. The proposed benchmark bears significant value towards the enhancement of the relevant techniques.
3. The authors perform a series of evaluation with the existing LLMs and VLMs. The results and error analysis shed a light on the further development.
summary: In this paper, the authors present a Chinese multimodal benchmark for evasive content detection for E-Commerce scenarios. The benchmark can support two kinds of evalution tasks: single violation and all-in-one, which help discriminate the reasoning ability under different fine-grained levels. The authors further perform evaluation for 26 open- and closed-source LLMs and VLMs under zero-shot, few-shot, RAG and SFT settings.
weaknesses: 1. The human annotation quality is not uncovered by the authors. It is unknown how hard this task is even for a human being. Also, fine-grained breakdown of the category distribution of the dataset should be reported.

---

confidence: 4
contribution: 4
initial_rating: 8
presentation: 3
questions: 1. Could the authors more clearly explain the conceptual and functional differences between the “single-violation” setting and the “all-in-one” setting? If possible, including detailed case examples in the supplementary material would better illustrate the differences and motivations behind these two settings.  

2. The rationale for the partial-accuracy metric needs further explanation and analysis, especially in practical application scenarios. If a model tends to predict many violation categories, its partial accuracy could be very high — but this circumstance may be risky in real-world use.

3. Have the authors considered including human performance (from laypeople or experts) or traditional detection methods on the dataset? Given that evading content detection is a difficult task with many classes and hidden content, adding human accuracy would provide a better benchmark for model performance.

4. Regarding the particularly poor model performances in Table 2, did the authors verify that model inference was executed correctly? The authors suggest differences in model architecture and training strategies as an explanation — is this a reasonable account? Please provide detailed descriptions of how Deepseek-VL2 differs in architecture and training strategy from the other VLMs listed in the table. 

5. The setups in Table 2 and Table 4 are confusing: Table 2 lists 23 models while Table 4 lists only 22. It appears that Deepseek-VL2, which performs poorly in Table 2, was omitted from Table 4 — this further raises doubts about whether Deepseek-VL2 was properly evaluated. The formats of Table 2 and Table 4 also need to be made consistent so readers can better compare model performance across tasks; consider uniformly distinguishing open-source and closed-source models.  

6. From the tables, the authors only include the “simplified description” and “detailed description” sub-tasks under the All-in-One task — what motivated this choice? Also, please provide example cases in the supplementary material to demonstrate the specific differences between simplified and detailed descriptions. 

7. In the error analysis section, the authors mention that stronger models are more likely to misinterpret prompt rules. One hypothesis is that stronger models possess more powerful reasoning capabilities and thus amplify deviations from human understanding — what is the detailed causal relationship here? Please supplement with a thorough explanation.
soundness: 3
strengths: 1. According to the authors, this work presents the first Chinese multimodal benchmark for evasive content detection in e-commerce scenarios. The study holds strong practical significance in detecting potentially deceptive or policy-violating advertisements and demonstrates both social value and pioneering potential in improving content safety and regulation in online commerce.  

2. The authors employ a data construction pipeline that integrates human experts and large language models (LLMs), ensuring both the difficulty and quality of the dataset’s questions.  

3. The paper conducts extensive experiments on the proposed benchmark, covering both open-source and closed-source models, as well as LLMs and VLMs, thoroughly validating the dataset’s effectiveness and quality.  

4. In addition to the construction and evaluation of the dataset, this paper also examines the performance of RAG and SFT in optimizing the effectiveness of content avoidance detection, providing valuable insights and clear directions for further utilization of LLMs and VLMs in this field.  

5. The authors provide comprehensive supplementary materials, including dataset construction methods, examples, and detailed definitions, which is commendable and enhances the transparency and reproducibility of the work.
summary: This paper presents EVADE-Bench, a new Chinese multimodal benchmark designed to evaluate large language models (LLMs) and vision–language models (VLMs) on evasive content detection in e-commerce. The dataset contains 2,833 expert-annotated text samples and 13,961 annotated images across six high-risk product categories. The authors benchmark 26 open- and closed-source LLMs/VLMs, revealing substantial performance gaps and common reasoning failures. Additional experiments using retrieval-augmented generation (RAG) and supervised fine-tuning (SFT) demonstrate measurable improvements in detection accuracy. The paper further provides a detailed analysis of common error types and discusses a potential multi-agent or decomposed multimodal reasoning paradigm for future development. Overall, this work establishes the first rigorous benchmark standard for evaluating evasive content detection, offering a valuable foundation for developing safer and more transparent content moderation systems in e-commerce, and exploring effective strategies such as RAG and SFT to enhance large models’ compliance detection capabilities.
weaknesses: 1. The differences between the two task paradigms, Single-Violation and All-in-One, are not sufficiently explained.  

2. The paper lacks an evaluation of annotation quality. Given that the task is a complex multi-class classification problem, inter-annotator agreement among experts is essential to ensure data reliability.  

3. In the All-in-One task, the authors introduce two subtasks, “simplified description” and “detailed description,” but the motivation behind this design and the performance analysis across these subtasks are not adequately discussed.  

4. The study does not include accuracy baselines from traditional methods (e.g., rule-based systems or human review), making it difficult to assess the true performance level of LLMs and VLMs on this task.

---

confidence: 4
contribution: 2
initial_rating: 4
presentation: 2
questions: 1. How do the evasive content strategies in EVADE-Bench compare to those observed in non-e-commerce domains (e.g., social media, healthcare), and would the benchmark’s evaluation framework be adaptable to these domains?
2. Did the authors consider cross-lingual generalizability? Given that EVADE-Bench is Chinese-focused, would the observed model limitations and improvement strategies transfer to other languages?
3. How do the expert annotations account for evolving e-commerce regulations? Is there a plan to update the benchmark as policies change over time?
4. This paper mentioned adversarial attacks. What is the relationship between ECD and adversarial attacks? How can this benchmark help to defend against adversarial attacks?
soundness: 2
strengths: 1. Working on a real-world application challenge by focusing on evasive content detection.
2. Constructs a large-scale, expert-annotated multimodal dataset (covering text and image inputs) with rigorous annotation pipelines, ensuring high-quality and regulation-aligned ground truth.
3. Conducts extensive experiments on 26 LLMs/VLMs (both open-source and closed-source), offering systematic baselines and detailed error analysis that sheds light on model limitations in multimodal reasoning.
4. Validates practical improvement strategies (RAG, SFT, rule merging) that can be directly applied to enhance real-world content moderation systems.
summary: This paper introduces EVADE-Bench, the first expert-curated Chinese multimodal benchmark designed to evaluate the performance of Large Language Models (LLMs) and Vision-Language Models (VLMs) in detecting evasive content in e-commerce scenarios. The benchmark comprises 2,833 text samples and 13,961 annotated images across six product categories (e.g., body shaping, height growth, health supplements) and includes two core tasks: Single-Violation (assessing fine-grained reasoning with short prompts) and All-in-One (testing long-context reasoning with unified, rule-intensive instructions). Through evaluating 26 mainstream LLMs and VLMs, the paper finds that state-of-the-art models frequently misclassify evasive content, and highlights the effectiveness of rule categorization, Retriever-Augmented Generation (RAG), and Supervised Fine-Tuning (SFT) in improving model performance. The work aims to lay the groundwork for safer e-commerce content moderation systems.
weaknesses: 1. Unclear definition of Evasive Content Detection (ECD): The paper fails to provide a rigorous, cited definition of ECD, leaving ambiguity about its scope and distinguishing features. It also does not adequately justify why ECD is a unique and critical task for LLMs/VLMs, nor does it clarify how ECD differs from existing QA or adversarial detection tasks.
2. Narrow focus on domain-specific scenarios without generalizable capability assessment: The benchmark is limited to six e-commerce product categories, and the paper does not attempt to decouple domain-specific knowledge from core reasoning abilities (e.g., semantic understanding, logical inference). This limits the generalizability of the results to other evasive content detection scenarios.
3. Lack of novelty in research contributions: The work primarily reports experimental results using existing techniques (benchmark construction, RAG, SFT) without introducing innovative methods, frameworks, or theoretical insights. It functions more as a benchmark report than a novel research contribution.
4. Insufficient analysis of cross-category and cross-modality consistency: The paper does not deeply explore why model performance varies drastically across product categories (e.g., health supplements vs. body shaping) or modalities (text vs. image), missing opportunities to identify modality-specific or category-specific bottlenecks.
5. Limited discussion of benchmark generalizability and external validity: There is no analysis of whether EVADE-Bench’s samples are representative of real-world evasive content at scale, nor is there evidence that model performance on this benchmark correlates with real-world content moderation effectiveness.
6. Please fix grammer issue: english -> English, chinese -> Chinese

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confidence: 4
contribution: 2
initial_rating: 2
presentation: 2
questions: 1. Could the authors clarify the data collection sources and the legal/ethical permissions involved?

2. Have the authors tried multimodal decomposition (VLM→LLM reasoning) empirically, or is this purely conceptual?
soundness: 3
strengths: (1) EVADE-Bench tackles a practically important and underexplored safety problem (e-commerce content moderation) and provides the first large-scale Chinese multimodal dataset aligned with legal advertising rules.

(2) The split between Single-Violation and All-in-One tasks provides interesting insights into how rule clarity and context length affect model reasoning.

(3) The discussion on hallucination, omission, OCR distortion, and rule-matching bias provides useful diagnostics for practitioners.
summary: This paper presents EVADE-Bench, a Chinese multimodal benchmark for detecting evasive content in e-commerce advertisements, where sellers deliberately obscure prohibited claims.
The dataset contains 2.8K text and 13.9K image samples annotated by domain experts under six product categories.
Two evaluation settings are designed: Single-Violation and All-in-One.
The authors benchmark 26 LLMs and VLMs, analyze their accuracy under Partial/Full match metrics, and further explore RAG/SFT enhancements.
weaknesses: 1. The work primarily repackages an individual content-moderation pipeline. It lacks a methodological or conceptual contribution to multimodal reasoning or safety. The dataset is heavily tied to Chinese e-commerce advertising and cannot generalize to other safety or reasoning contexts. Thus, it does not qualify as a "foundation benchmark", but rather a domain-specific dataset.

2. The results are broad but shallow, no causal or ablation analysis of why model fail, or what architecture factors matter. The paper's Section 5.5 presents a discussion of adopting a two stage VLM --> LLM pipeline inspired by Prism. However, no corresponding experiments or quantitative results are reported. The discussion remains conceptual, which weakens the empirical rigor of the paper's analytical claims.

3. The paper does not disclose the exact data source (which platforms, how samples were licensed, whether commercial content was reproduced), raising reproducibility and ethical concerns.


Decision: Reject