Model Detail
Meme-Qwen-7B-Instruct
—Meme-Qwen-7B-Instruct is an AI model with 7B parameters released by GaryYang123. Distributed under the permissive mit license.
Meme-Qwen-7B-Instruct ships with 7B parameters. The mit license is permissive, allowing commercial deployment and derivative work without per-seat fees, though attribution requirements still apply.
Meme-Qwen-7B-Instruct is best fit for general-purpose AI workloads. Treat this as a starting matrix rather than a benchmark verdict — the right deployment usually depends on the specific evaluation suite that mirrors your workload.
MemeLogos: Knowledge-Driven Retrieval-Augmented Reasoning for Harmful Meme Detection
arXiv:2610.15463v2 Announce Type: replace Abstract: With the rapid proliferation of social media, harmful memes have become a growing threat to online ecosystems. Their harmfulness is often implicitly conveyed through complex interactions among visual, textual, and contextual cues, making accurate d
MemeTrace: A Multimodal LLM-Based Agent for Safety-Aware Meme Provenance Analysis
arXiv:2610.19267v1 Announce Type: cross Abstract: Online memes are often carriers of unsafe content such as disinformation and propaganda. Their rapid evolution in response to news events and strong dependence on contextual information make it difficult for platforms and moderators to detect and mod
Decodable but Misrouted: Sparse Features Uncover a Readout Gap in Vision-Language Models for Harmful Meme Detection
arXiv:2609.18860v2 Announce Type: replace-cross Abstract: When large vision-language models misclassify harmful memes, the failure may reflect missing internal evidence or an inability to route represented evidence to their outputs. We distinguish these cases in Gemma-3 and Qwen3.5 using sparse auto
Small Cues, Big Consequences: Learning Pivotal Cues for Multimodal Meme Classification
arXiv:2609.26907v1 Announce Type: cross Abstract: Memes often derive their harmful, hateful, or sarcastic meaning from small but decisive visual, textual, or cross-modal cues. Existing multimodal classifiers can miss such evidence when relying mainly on global image-text representations. We introduc
Learn Before You Judge: Progressive Knowledge-to-Decision Alignment for Explainable Hateful Meme Detection
arXiv:2609.19778v1 Announce Type: new Abstract: Hateful memes spread abusive content through implicit interactions between images and text, posing serious threats to the safety of online communities. In recent years, multimodal large language models have been widely used for hateful meme detection a
