arXiv:2510.00566v4 Announce Type: replace-cross Abstract: Approximate Nearest-Neighbor Search (ANNS) pipelines for high-dimensional neural embeddings spend the bulk of their query time in candidate verification, making it the primary bottleneck in the search process. In this paper, we present PANORAMA, a state-of-the-art refinement technique that accelerates verification by exploiting the inherent spectral decay of these embeddings. Using PCA to compact signal energy, PANORAMA evaluates candidate distances incrementally, computing at each step a strict lower bound on the full-vector distance a
The continuous growth in high-dimensional neural embeddings in AI systems necessitates more efficient search algorithms to maintain performance and scalability. This research addresses a critical bottleneck in current Approximate Nearest-Neighbor Search (ANNS) pipelines by optimizing candidate verification.
This development is crucial for any AI application relying on similarity search, potentially accelerating complex AI operations from recommendation engines to large language models. It represents a tangible step towards more efficient and scalable AI infrastructure, directly impacting the performance and cost of AI at scale.
The primary bottleneck in Approximate Nearest-Neighbor Search (ANNS) for high-dimensional neural embeddings shifts from candidate verification to other stages, or is significantly alleviated. This enables faster query times and potentially larger, more complex embedding spaces to be efficiently searched.
- · AI compute infrastructure providers
- · Large language model developers
- · Companies utilizing recommendation systems
- · Autonomous AI agent developers
- · Companies with less efficient ANNS implementations
- · Legacy search algorithm developers
Significantly faster query times for AI systems employing high-dimensional neural embeddings, such as large language models and retrieval-augmented generation systems.
Reduced operational costs for AI services due to increased efficiency, allowing for more complex AI applications to become economically viable.
Enhanced capabilities for AI agents and autonomous systems by improving their ability to quickly access and process vast amounts of contextual information.
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Read at arXiv cs.AI