Retrieval-Augmented Generation (RAG) has substantially advanced knowledge-intensive question answering (QA). Existing methods typically follow a ’decompose and sequentially solve’ paradigm, mainly optimizing sub-question generation, retrieval, or search decisions, while lacking explicit maintenance of cross-step intermediate reasoning states. As a result, retrieval noise, query drift, and local reasoning errors introduced in early steps can be inherited and amplified along the reasoning chain, eventually causing reasoning failure. We propose **CloRe**, a state-based closed-loop reasoning framework for multi-hop question answering that explicitly maintains hop-level intermediate states and coordinates forward review with dependency-aware backward correction. In the forward phase, CloRe uses conditional query rewriting to mitigate query drift, stage-wise evidence acquisition to mitigate retrieval noise, and step-level review with local repair to filter unreliable intermediate answers before they are propagated. When forward correction is insufficient, CloRe enters a backward phase that uses downstream failure signals to backtrack along explicit dependencies and revise predecessor states that were locally acceptable under earlier review but become incompatible with later dependency constraints. Experiments on three representative multi-hop QA benchmarks show that CloRe achieves competitive performance against strong baselines, demonstrating the effectiveness of explicit intermediate-state maintenance for multi-hop reasoning.

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.