The field is witnessing significant developments in probabilistic modeling and algorithmic techniques, with a focus on improving the accuracy and efficiency of various systems. Researchers are exploring new methods for aligning probabilistic models with real-world systems, developing innovative algorithms for solving complex problems, and creating more efficient data structures for indexing and querying large datasets. Notable papers in this area include: Alignment monitoring techniques that utilize tools from sequential forecasting to construct monitors for measuring alignment scores. Learned adaptive indexing approaches that build indexes on the fly as queries are submitted, utilizing learned models for indexing data and query workload prediction techniques. Identity testing frameworks for stochastic languages, which establish a rigorous framework for testing whether an unknown distribution matches a known reference. Sublinear-time algorithms for counting distinct squares in packed strings, which leverage novel approaches to extract squares from runs and compute locations of Lyndon roots. Ephemeral edit operations in text indexing and pattern matching, which support subsequent pattern matching queries with ephemeral substring insertions, deletions, or substitutions in the text.