arXiv:2603. 17863v2 Announce Type: replace-cross Abstract: Automating the development of machine learning algorithms has the potential to unlock new breakthroughs.
Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.
arXiv:2604.16625v2 Announce Type: replace-cross Abstract: Recent large language model (LLM) agents have shown promise in using execution feedback for test-time adaptation. However, robust self-improv...
arXiv:2603. 17216v2 Announce Type: replace Abstract: With the advent of AI agents, automated scientific discovery is becoming an increasingly plausible goal.
arXiv:2607. 06764v1 Announce Type: new Abstract: Recent progress on ARC-AGI-1 from disclosed architectures has come broadly from two regimes: heavy test-time compute over frontier models (evolutionary search, exhaustive sampling, extended chain-of-thought), or benchmark-specific training in which small models are fine-tuned on ARC data, often with task-specialized architectures.
Generative Adversarial Loops (GAL) is a framework that alternates between a discriminator generating adversarial data to expose weaknesses in current AI systems and a generator discovering algorithms to overcome those weaknesses. Applied to tasks such as KV compression, sparse video generation, sparse attention, and context extension, GAL’s discriminator identifies shortcomings in state‑of‑the‑art techniques, while the generator produces new algorithms that improve performance on both adversarial and established benchmarks. The study demonstrates significant gains, for example boosting CompactorPress on KV compression from 0.35 to 0.97 on the discriminator dataset and improving Dual Chunk Attention from 0.20 to 0.90.
arXiv:2609.36679v1 Announce Type: new Abstract: Machine learning engineering (MLE) agents have made substantial progress, but learning through ML experimentation remains costly in time and computatio...
arXiv:2607. 21971v1 Announce Type: new Abstract: Test-time scaling through iterative self-evolution with environment feedback, as demonstrated by AlphaEvolve, shows remarkable performance gains.