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.
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.
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.
Researchers introduce ContextRL, a reinforcement learning method that trains LLMs to select the context that supports a given query-answer pair from two highly similar candidates, rather than supervising only final answers. The approach constructs contrastive context pairs in two domains: coding agent trajectories (1k pairs) and multimodal image pairs (7k pairs). ContextRL achieves +2.2% average gains over standard GRPO on 5 long-horizon benchmarks and +1.8% across 12 visual QA benchmarks, with ablations showing the gains stem from the context-selection objective rather than the contrastive data alone.
Researchers introduce CHARM, a graph foundation model designed for zero-shot transfer across multimodal graphs containing text, images, and other node-associated data. The model replaces isolated node representations with hierarchical graph contexts that capture cross-modal relations and map domain-specific patterns to shared high-level concepts, reducing the need for target-domain fine-tuning. A modality-aware encoder converts these representations into graph tokens fed to a large language model backbone. Experiments show consistent improvements on zero-shot multimodal graph tasks, addressing a gap where existing GNN-based GFMs require downstream adaptation and LLM-based methods are largely unimodal.
A new arXiv preprint introduces Neural Classification Trees (NCT), a framework that encodes subgroup structure in a tree-shaped architecture to address spurious correlations in ML classifiers. By routing samples to 'easy' or 'hard' nodes based on prediction correctness and reusing routes as pseudo-labels iteratively, NCT disentangles conflicting subgroups without requiring subgroup annotations. The method is evaluated on five benchmarks covering binary and multi-class spurious correlations, achieving competitive robustness while providing interpretable mappings between model architecture and latent data structure.
FedLAB is a new federated learning framework for multimodal graph foundation models that organizes knowledge into typed hierarchical codebooks covering modality evidence, node semantics, and topology context. The system enables semantic traceability under strict data isolation, addressing a gap where existing methods exchange knowledge through parameters or embeddings without exposing how evidence jointly supports predictions. Evaluated on 10 benchmarks and 6 downstream tasks, FedLAB improves over state-of-the-art baselines by up to 7.53% while keeping raw data local.
A new arXiv preprint introduces GLM-RAG, a retrieval-augmented generation framework using graph language model (GLM)-based retrievers over knowledge graphs. The authors compare GLM-based, GNN-based, and vector-search retrievers across single- and multi-hop QA settings, finding that finetuned GLM retrievers generalize better to unseen domains and achieve state-of-the-art on two multi-hop benchmarks. GNN-based retrievers offer better graph coverage with efficient training, while vector search excels at single-hop tasks. The work provides a systematic comparison of retriever architectures for knowledge-graph RAG with practical implications for domain transfer.
A new arXiv paper proposes using graph neural networks over argument-mining-derived reasoning graphs to attribute authorship of LLM-generated text, moving beyond surface-level linguistic features. The approach outperforms a Longformer baseline by up to 27 percentage points under paraphrasing and backtranslation obfuscation attacks, and by 19 points when generalizing to unseen model versions. The work addresses a practical gap in AI-generated text detection: robustness to adversarial rewriting and model version drift.