Generative Adversarial Loops

Research progress in AI can be understood as the interaction between two complementary processes: benchmark creation and method discovery. Historically, both processes were driven by human intelligence. However, recent advances in AI have significantly accelerated automated method discovery, while automated benchmark creation has received comparatively less attention. To realize truly self advancing systems, we propose Generative Adversarial Loop (GAL), a generator discriminator framework that alternates between two agentic searches: (1) a discriminator that generates adversarial data to expose weaknesses in current systems, and (2) a generator that discovers algorithms to overcome them. We apply this framework to approximation algorithms for efficient inference. Unlike existing auto research systems, which primarily focus on algorithm discovery, GAL introduces a discriminator agent that automates goalpost setting by continually searching for weaknesses in the current algorithm. We demonstrate adversarial data generation across four tasks: KV compression, sparse video generation, sparse attention, and context extension, where the discriminator identifies weaknesses in state of the art techniques. We further show that GAL enables autonomous improvement for KV compression and context extension, with newly discovered algorithms improving not only on adversarially generated data, but also, in some cases, on established benchmarks. GAL thus provides a path toward autonomous goalpost setting and algorithmic improvement, where AI systems continually discover their own weaknesses and develop methods to overcome them. Code: https://anonymous.4open.science/r/generative-adversarial-loops