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Agents can significantly enhance retrieval performance in visually rich document environments, achieving a 67.50% Recall@1 compared to just 37.50% for OCR-text methods.
UEmbed achieves a groundbreaking unification of dense and sparse embeddings, outperforming existing models in multimodal tasks while streamlining the retrieval process.
Traditional code retrieval methods falter in agentic coding, but CORE-Bench reveals that fine-tuning can bridge the gap and boost performance significantly.
