The field of recommendation systems is moving towards incorporating multimodal information, such as images and text, to enhance the effectiveness of recommendations. This approach has shown significant gains in representation quality, but also presents challenges in terms of fusing and aligning different modalities. Recent work has focused on developing frameworks that can integrate multiple modalities, such as language and vision, to capture complementary cues and avoid correlation bias. Notable papers in this area include: PolyRecommender, which introduces a multimodal discovery framework that integrates chemical language representations with molecular graph-based representations. SRGFormer, which presents a structurally optimized multimodal recommendation model that captures the overall behavior patterns of users and enhances structural information by embedding multimodal information into a hypergraph structure. PreferThinker, which proposes a reasoning-based personalized image preference assessment framework that follows a predict-then-assess paradigm. VLIF, which presents a vision-language and information-theoretic fusion framework that enhances multimodal recommendation through fine-grained visual enrichment and information-aware fusion. DRCSD, which proposes a novel GNN-based CF model that includes collaborative signal decoupling and order-wise denoising modules to address the limitations of existing noise-removal approaches.