EP313: How ARTS reasons through its own failures

Title: Learning the ARTS of Search for Automated Discovery Source: http://arxiv.org/abs/2606.21891v1 Summary: This paper introduces ARTS, a novel agentic reasoning framework that uses LLMs to navigate search spaces by diagnosing execution failures and selecting hypotheses. It also presents a reasoning breakthrough by using test-time training to distill search tree knowledge directly into model weights, allowing small open-source models to match closed frontier models at much lower cost.