TL;DR
Pail is proposed, a 𝑘 NN search algo-rithm for sets that supports a wide range of similarity functions and implements the positional filter directly into a novel index structure to effectively prune candidates.
Recently, academia and industry have considered the problem of approximate nearest neighbor search (ANNS) with filters. In this setting, each object consists of a high-dimensional vector and attribute values. Given a query vector, filters for attribute values, and $k$, this problem retrieves $k$ vectors approximately n...
Approximate nearest neighbor search (ANNS) retrieves the most similar vectors to a query vector in high-dimensional space. Label-filtering ANNS (LFANNS) extends ANNS with a label filter that the labels of base vectors must satisfy a set relation (e.g., equality, containment, or overlap) with the query labels. Existing...
Modern retrieval systems increasingly require filtered vector search under arbitrary predicate constraints, where users filter results by attributes such as category, price, location, keywords, and their combinations. Existing solutions either specialize in a single predicate type (e.g., range or equality filters), rel...
LLM-Guided Graph Pruning (LGP), a general framework that addresses this mismatch directly by leveraging LLM reasoning to refine an existing ANN graph index itself, and consistently improves end-to-end retrieval performance over both vanilla greedy graph search and LLM-based reranking across widely used graph-based ANN...
In typical search tasks, the goal is to return a list of k documents that are ordered by their relevance to a user-issued query. However, not all information that is contained in a collection should be made available to users i.e., sensitive information should not be revealed. Sensitivity-Aware Search (SAS) is a bi-obj...
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