Hypergraph-based RAG systems surpass traditional graph-based approaches by organizing complex n-ary atomic facts among entities, rather than relying solely on binary relationships. Despite the advancements in multimodal large language models (MLLMs) with enhanced visual capabilities, current hypergraph-based RAG frameworks predominantly restrict knowledge retrieval and reconstruction to a unimodal, text-centric paradigm. This limitation prevents them from fully leveraging the powerful visual perception capabilities of modern MLLMs. To address this gap, we systematically explore the integration of hypergraph awareness in RAG systems through visual cues. By incorporating visual representations of hypergraphs into the RAG pipeline, we introduce VizRAG, the first RAG system to support visual hypergraph structure awareness. Experimental results demonstrate that VizRAG significantly outperforms strong baselines, validating the promising potential of hypergraph visualization as a novel approach for RAG systems.

Medical language models are increasingly evaluated on open-ended clinical reasoning, where good responses must combine factual recall, evidence integration, decision making, and synthesis. However, standard reward models compress these different standards into one reward score, while medical RL with answer keys mainly rewards multiple-choice correctness. This makes it difficult to improve non-multiple-choice medical reasoning without losing performance on exam-style benchmarks. In this paper, we propose a Prefix Mixture-of-Experts Reward Model (Prefix-MoE RM), which routes each query-response pair across recall, analysis, decision, and synthesis prefix experts, softly fuses their trainable prefixes, and then predicts one reward score. The MoE structure allows different abilities to use different scoring experts to reduce reward conflicts, while continuous prefixes provide a more expressive conditioning signal than fixed hard prompts. The reward model is trained on non-multiple-choice medical preferences and then used in mixed-data curriculum GRPO, where multiple-choice prompts provide additional answer-key feedback. On held-out reward-model evaluation, Prefix-MoE RM improves pairwise accuracy from 79.8 to 93.0 over a shared RM; with the RL pipeline fixed, replacing it with a shared RM or soft expert-head MoE lowers HealthBench from 43.8 to 24.8 and 34.6, respectively. With Qwen3-8B as the policy backbone, the full system outperforms the larger Qwen3-32B base on MedXpertQA and HealthBench, while the Qwen3-32B version reaches 50.1 HealthBench and 4.55 LLMEval-Med, remaining competitive with stronger closed general models and retaining an advantage on non-multiple-choice metrics.

Large Vision-Language Models (LVLMs) consistently require new arenas to guide their expanding boundaries, yet their capabilities with hypergraphs remain unexplored. In the real world, hypergraphs have significant practical applications in areas such as life sciences and social communities. Recent advancements in LVLMs have shown promise in understanding complex topologies, yet there remains a lack of a benchmark to delineate the capabilities of LVLMs with hypergraphs, leaving the boundaries of their abilities unclear. To fill this gap, in this paper, we introduce HyperGVL, the first benchmark to evaluate the proficiency of LVLMs in hypergraph understanding and reasoning. HyperGVL provides a comprehensive assessment of 12 advanced LVLMs across 84,000 vision-language question-answering (QA) samples spanning 12 tasks, ranging from basic component counting to complex NP-hard problem reasoning. The involved hypergraphs contain multiscale synthetic structures and real-world citation and protein networks. Moreover, we examine the effects of 12 textual and visual hypergraph representations and introduce a generalizable router WiseHyGR that improves LVLMs in hypergraph via learning adaptive representations. We believe that this work is a step forward in connecting hypergraphs with LVLMs.