Unverified50% confidenceFactExact time
The core principle of vector search is to convert text into high-dimensional numerical vectors via an embedding model, and then use approximate nearest neighbor algorithms (such as HNSW or IVF) to find the closest candidates.
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50%
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Long-term
Relevance
9/28/2026
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UnverifiedThe HNSW algorithm constructs a multi-layer graph structure that enables approximate nearest neighbor search in logarithmic time complexity.71% similarUnverifiedHNSW achieves logarithmic retrieval complexity by constructing a multi-layer graph structure, maintaining millisecond-level response times on million-scale vectors, and is one of the ANN algorithms with the best overall performance68% similarUnverifiedCommon techniques for improving model efficiency include quantization, pruning, and architecture search.67% similarUnverifiedLLM-based agentic optimization methods do not rely on predefined search spaces or scheduling templates, but instead leverage the model's code understanding and generation capabilities to explore more open-ended optimization paths.66% similarUnverifiedMultimodal large models can infer partially occluded text based on contextual semantics, with recognition accuracy and robustness far exceeding traditional OCR solutions.65% similar
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