Sumanth (@Sumanth_077) “Self Improving AI (SIA) beats Karpathy's autoresearcher agent by improving itsel” — TopicDigg

Self Improving AI (SIA) beats Karpathy's autoresearcher agent by improving itself! SIA is a Self Improving AI framework to autonomously improve the performance of any AI system (Model / Agent) on a benchmark task. Most agent frameworks are static. Fixed harness, fixed model weights, fixed memory layer. They plan, act, and use tools. SIA operates on a different layer entirely. SIA focuses on one problem: how do you design structured feedback loops that allow an agent to evaluate its own performance, adapt its strategy, and get better over time? After every run, SIA evaluates itself and improves three things. It updates its own harness. Updates the weights of its underlying model. Updates its own memory layer to handle new complexities. The agent rewrites itself based on what it learned. On MLE-Bench, OpenAI's benchmark for evaluating an agent's ability to train ML models, SIA climbed to the top of the leaderboard. Beat every specialized ML research agent including MLEvolve and AIRA-dojo. Then kept improving and displaced its own previous versions on the leaderboard. I've shared the link to the paper and the repo in the replies!