Large Language Models (LLMs) excel at text generation, but still struggle to produce accurate, long-form documents grounded in large knowledge bases. Existing “top-down” approaches—which plan before retrieving evidence—often result in fragmented or unfaithful content due to misalignment between the plan and available knowledge.

To overcome this, we introduce a “bottom-up” framework based on a “Retrieval-First for Knowledge, Clustering for Structure” strategy. Our method begins by establishing the “knowledge boundaries” of the source corpus through comprehensive retrieval, followed by unsupervised clustering to group evidence into distinct “knowledge clusters.” These clusters objectively inform a hierarchical outline and subsequent text generation, ensuring strict adherence to the source and significantly reducing hallucination.

Experimental results on both 14B- and 32B-parameter models demonstrate that our method achieves performance comparable to or exceeding state-of-the-art baselines, and is expected to demonstrate unique advantages in knowledge-constrained scenarios that demand high fidelity and structural coherence. Our work presents an effective paradigm for generating reliable, structured, long-form documents, paving the way for more robust LLM applications in high-stakes, knowledge-intensive domains.

Relation extraction (RE) aims to identify semantic relations between entities in unstructured text. Although recent work extends traditional RE to multimodal scenarios, most approaches still adopt classification-based paradigms with fused multimodal features, representing relations as discrete labels. This paradigm has two significant limitations: (1) it overlooks structural constraints like entity types and positional cues, and (2) it lacks semantic expressiveness for fine-grained relation understanding. We propose Retrieval Over Classification (ROC), a novel framework that reformulates multimodal RE as a retrieval task driven by relation semantics. ROC integrates entity type and positional information through a multimodal encoder, expands relation labels into natural language descriptions using a large language model, and aligns entity-relation pairs via semantic similarity-based contrastive learning. Experiments show that our method achieves state-of-the-art performance on the benchmark datasets MNRE and MORE and exhibits stronger robustness and interpretability.