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PAC Learning with Bandit Feedback: Sharp Sample Complexity in the Realizable Setting Algorithms with Polynomially-Improved Approximation Factors for the $2 \rightarrow q$ Norm, and Applications A computational phase transition for learning-to-sample from Ising models Covering vertices by sequential stars Fermi-Dirac machines as quantizations of neurons A Comprehensive Evaluation of Vertex Elimination Algorithms for Algorithmic Differentiation A Tight Bound on Localization of Electrical Flows Optimal Dimension-Free Sampling for Regularized Classification Reducing the Randomness in Partition Oracles for Bounded Degree Minor-Free Graphs Beyond the Half-Approximation: Fair and Efficient Online Class Matching Efficient Uniform Sampling of Surjections via their Profiles Tractable Maximization of Budgeted Phylogenetic Diversity on Networks Utilizing Node Scanwidth Fairness in Aggregation: Optimal Top-$k$ and Improved Full Ranking Learning-Augmented Online Scheduling with Parsimonious Preemption Entropy Equivalence Testing Lumberjack: Better Differentially Private Random Forests through Heavy Hitter Detection in Trees The Secretary Problem with a Stochastic Precursor Polynomial-Time Robust Multiclass Linear Classification under Gaussian Marginals Efficient Banzhaf-Based Data Valuation for $k$-Nearest Neighbors Classification Block-Sphere Vector Quantization An Approximation Algorithm for Graph Label Selection Iterative Chow Filtering for Learning with Distribution Shift Complexity of Non-Log-Concave Sampling in Fisher Information Stochastic Matching via Local Sparsification Finite Sample Bounds for Learning with Score Matching What is Learnable in Valiant's Theory of the Learnable? Provable Quantization with Randomized Hadamard Transform Min-Max Optimization Requires Exponentially Many Queries Fast and Compact Graph Cuts for the Boykov-Kolmogorov Algorithm A proximal gradient algorithm for composite log-concave sampling
Adaptive encodings for small and fast compressed suffix a...
Diego Díaz-Domínguez, Veli Mäkinen · 2026-02-19 · via cs.DS updates on arXiv.org

Compressed suffix arrays (CSAs) index large repetitive collections and are key in many text applications. The r-index and its derivatives combine the run-length Burrows-Wheeler Transform (BWT) with suffix array sampling to achieve space proportional to the number of equal-symbol runs in the BWT. While effective for near-identical strings, their size grows quickly as variation increases, since the number of BWT runs is sensitive to edits. Existing approaches typically trade space for query speed, or vice versa, limiting their practicality at large scale. We introduce variable-length blocking (VLB), an encoding technique for BWT-based CSAs that adapts the amount of indexing information to local compressibility. The BWT is recursively divided into blocks of at most w runs (a parameter) and organized into a tree. Compressible regions appear near the root and store little auxiliary data, while incompressible regions lie deeper and retain additional information to speed up access. Queries traverse a short root-to-leaf path followed by a small run scan. This strategy balances space and query speed by transferring bits saved in compressible areas to accelerate access in incompressible ones. Backward search relies on rank and successor queries over the BWT. We introduce a sampling technique that guarantees correctness only along valid backward-search states, reducing space without affecting query performance. We extend VLB to encode the subsampled r-index (sr-index). Experiments show that VLB-based techniques outperform the r-index and sr-index in query time, while retaining space close to that of the sr-index. Compared to the move data structure, VLB offers a more favorable space-time tradeoff.