LENS: A Self-Improving Meta-Architecture for Autonomous Algorithm Discovery through Learning, Evolution, and Natural Language Synthesis

We introduce LENS (Learning, Evolution, and Natural Language Synthesis), a modular self-improving architecture for autonomous algorithm discovery. Unlike approaches relying on single-model reasoning or shallow combinations of reinforcement learning and language models, LENS decomposes the discovery process into three specialized modules: an RL strategy layer for meta-level decisions, an LLM synthesis layer for creative generation, and an evolution layer for systematic refinement. This structural decomposition enables a self-expanding action space where LLM-generated algorithms become new actions available to the RL policy, creating a persistent knowledge accumulation loop. Early discoveries enable more sophisticated later ones, overcoming the fundamental limitation of static action spaces found in traditional automated machine learning systems. We evaluate LENS on sorting, knapsack optimization, and the traveling salesman problem. The system autonomously evolved from naïve baselines to expert-like algorithms, independently discovering efficient divide-and-conquer sorting structures and demonstrating structural leaps from greedy heuristics to Dynamic Programming and Branch-and-Bound strategies. Experimental traces reveal emergent behaviors including strategic switching, selective refinement, and progressive structural convergence. LENS demonstrates that intelligence can emerge from architectural composition rather than scale alone, enabling systems that invent, test, and improve algorithms without human intervention.