3arXiv cs.CL (Computation and Language)·
HCIG: Hierarchical Cross-Modal Incongruity Graph Network for Multimodal Sarcasm and Cyberbullying Detection
Researchers propose HCIG, a graph attention network framework that models cross-modal incongruity between text and images at token, phrase, and global levels for detecting sarcasm and cyberbullying in social media. A complementary architecture, GCCN, uses contradiction-aware pooling for efficient multimodal reasoning. HCIG achieves 85.74% accuracy on the MMSD sarcasm benchmark and 69.62% accuracy on the MultiBully cyberbullying dataset, outperforming conventional fusion-based approaches. The work addresses a narrow but practically relevant NLP task in content moderation.
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RDS Fusion: Hybrid neuro-symbolic gating with compressed CoT for zero-shot irony detection
Researchers introduce the Robust Dual-Signal (RDS) Fusion framework, a hybrid neuro-symbolic architecture that compresses Chain-of-Thought reasoning without supervised fine-tuning for irony and sarcasm detection in social media text. Evaluated on TweetEval (N=734) and iSarcasm, the zero-shot system matches fine-tuned BERTweet performance and outperforms supervised SemEval transformer ensembles on the imbalanced iSarcasm dataset. A statistical ablation shows that only the full concurrent fusion of all three signals yields a validated improvement, with individual components providing no significant standalone gain.
Social Gaze Consistency as a Semantic Cue for AI-Generated Image Detection
This paper introduces Social Gaze Consistency (SGC), a high-level semantic detection axis based on the mutual coherence of gaze direction, head-eye alignment, and pupil placement between interacting individuals in images. The authors construct a controlled diagnostic dataset with region-specific gaze perturbations and a Block-Compositional Caption Supervision scheme to train detectors without generator-fingerprint memorization shortcuts. Cross-architecture validation shows +3.7 pp improvement on the COCOAI Interaction subset when applied to FakeVLM, with gains transferring from a single inpainter (FLUX.1-Fill) to multi-generator suites. The work argues that diffusion models share a spectral weakness in periocular structure, making gaze coherence a robust, backbone-agnostic detection signal orthogonal to existing low-level artifact methods.
Introducing ConTextual: Benchmark for Joint Text-Image Reasoning in Text-Rich Scenes
Hugging Face introduces ConTextual, a new benchmark evaluating multimodal models on their ability to jointly reason over text and images in text-rich scenes. The benchmark targets a specific capability gap where models must integrate visual and textual information simultaneously rather than treating them independently. A leaderboard accompanies the benchmark to track model progress on this task.
Label Context Classifier (LCC) improves GNN node classification on heterophilous graphs
A new arXiv preprint proposes the Label Context Classifier (LCC), a method for improving node classification in graph neural networks on heterophilous graphs where connected nodes tend to have different class labels. LCC generates label context embeddings via four types of directed walks to capture higher-order class label connectivity, and can be integrated with any existing GNN architecture. Experiments show GNNs augmented with LCC outperform state-of-the-art methods on heterophilous directed graphs.
Symbal: Automated detection of systematic misalignments in MLLM-generated image captions
Researchers from Stanford introduce Symbal, a dual-stage framework using off-the-shelf foundation models to detect recurring, visually-correlated errors in multimodal LLM-generated captions. They also release SymbalBench, a benchmark of 1.7 million image-text pairs across 420 vision-language datasets covering natural and medical imaging domains. Symbal correctly identifies systematic misalignments in 63.8% of datasets, nearly 4x better than the closest baseline, and works without access to the underlying MLLM. The work addresses a practical data-quality auditing problem relevant to anyone building or evaluating vision-language datasets.
SeRIn: Decomposed multimodal fusion architecture achieves state-of-the-art on sentiment analysis benchmarks
Researchers propose SeRIn (Segregate, Refine, Integrate), a multimodal language model fusion scheme that separates modality-specific refinement from cross-modal interaction via distinct architectural pathways. The design defers full cross-modal interaction to a final prediction step, with ablations showing structured interaction rather than added capacity drives performance gains. SeRIn achieves state-of-the-art results on CH-SIMS and CMU-MOSEI benchmarks, and exhibits emergent modality reweighting under visual corruption without explicit supervision.
PromptGNN-sim: Bidirectional GNN-LLM fusion framework for text-attributed graph learning
Researchers introduce PromptGNN-sim, a bidirectional structure-semantic fusion framework that jointly trains a Graph Attention Network and an LLM for text-attributed graph learning. The system uses GAT-based neighborhood selection to generate structure-aware prompts for the LLM, with cross-modal contrastive learning and cross-attention aligning both components during training. Evaluated on six datasets including Cora, Pubmed, and WikiCS, it outperforms classical GNNs, standalone LLMs, and prior GNN-LLM fusion methods on cross-task transfer, cross-dataset generalization, and sparse perturbation settings.
Multimodal neurons in artificial neural networks
OpenAI researchers discovered neurons in CLIP that respond to the same concept across literal, symbolic, and conceptual representations. This finding parallels multimodal neurons previously observed in biological brains and helps explain CLIP's ability to classify unusual visual renditions of concepts. The work is presented as a step toward understanding the associations and biases learned by CLIP and similar vision-language models.