Overview of the SISAP 2025 Indexing Challenge.

This paper summarizes the innovative solutions presented at the third edition of the SISAP Indexing Challenge held at SISAP 2025.
The challenge featured two distinct tasks involving vector embeddings derived from a large corpus using neural encoders. It proposed the following two tasks under strict memory and computational constraints:
– Task 1: Approximate nearest neighbor search achieving an average
recall of at least 0.7 for 30-NN, using out-of-distribution objects as
queries.
– Task 2: k-NN (k = 15) graph construction for large datasets, requiring an average recall of at least 0.8.
Both tasks required solutions to operate within strict resource limits: 16 GB of RAM, 8 virtual CPUs, and a 12-hour wall-clock time for the end-to-end pipeline (including data loading, pre-processing, indexing, and searching). Each task imposes different minimum quality requirements and ranking specifications. Participants developed strategies such as data compression, optimized indexing, and efficient search algorithms to meet these constraints. This paper details the challenge design, explains the evaluation framework, and provides an overview of the submitted solutions.