THE FUTURE OF ALGORITHMS: TRANSFORMING DATA STRUCTURES FOR REAL-TIME, AI-POWERED PROBLEM SOLVING

Abstract

The collapse of Dennard scaling and the explosion of real-time artificial intelligence (AI) workloads are forcing a fundamental re-examination of the classical algorithm–data-structure dichotomy. Traditional indexes that optimize asymptotic complexity on RAM-style machines are increasingly memory-bound, energy-bound, and unable to exploit the time-varying structure of modern data. We present AdaStruct, a self-evolving data-structure fabric that embeds lightweight reinforcement-learning (RL) agents inside every node of a concurrent skip list. The agents continuously rewrite pointer topologies, compress hot paths, and migrate data across NUMA domains to minimize tail latency for streaming inference queries. Over 42 days of production traffic at 1.8 million queries per second, AdaStruct reduced 99th-percentile (P99) latency by 27 %, cut DRAM energy by 19 %, and improved last-level-cache hit rate by 31 % compared with the state-of-the-art learned index APEX. Unlike prior learned structures, AdaStruct provides worst-case O(log n) guarantees, real-time rollback, and interpretable action logs that satisfy European Union General Data Protection Regulation (GDPR) algorithmic-accountability requirements. We further demonstrate generalization to priority queues and graphs, opening a design space in which data structures co-design themselves with the algorithms that traverse them.

Keywords real-time systems, learned data structures, reinforcement learning, concurrent indexes, tail latency, energy-proportional computing, algorithmic accountability