@omarsar0: Karpathy's autoresearch repo started an impressive trend. Agents can now train AI models to build SoTA agentic systems.…
Summary
Karpathy's autoresearch repository has sparked a trend where agents train AI models to build state-of-the-art agentic systems, highlighting current limitations in LLM-driven hypothesis generation.
View Cached Full Text
Cached at: 04/22/26, 12:53 PM
Karpathy’s autoresearch repo started an impressive trend. Agents can now train AI models to build SoTA agentic systems. And to think this is just scratching the surface. Ultimately, it boils down to good research questions or hypotheses. LLMs are not great at this (yet).
Similar Articles
@lftherios: 1/ Autoresearch from @karpathy has been one of the most interesting agentic patterns to emerge this year. The challenge…
Andrej Karpathy's autoresearch pattern highlights how current AI agents run experiments in isolation, wasting compute by duplicating work and rediscovering dead ends.
@sitinme: Saw Karpathy open-sourced a very interesting project autoresearch, which gives a real but small-scale LLM training task to an AI Agent, letting it do research, modify code, run experiments, look at results, and then decide whether to keep or discard the changes. The project is based on a single NVIDIA…
Karpathy open-sourced an experimental project, autoresearch, that lets an AI Agent automatically complete the research loop for small-scale LLM training: modify code, run experiments, evaluate results, and iterate. Humans only need to write the research plan and constraints.
@dair_ai: Very cool idea to have agents design complex systems by searching over the model structure itself. New research from Sa…
A tweet from DAIR AI highlights new research by Sakana AI introducing CEDAR, an LLM-agent method that designs, simulates, and refines complex system-dynamics models via Monte Carlo Tree Search over feedback structures, aiming to automate goal-directed design in artificial life.
@omarsar0: So true! There are so many ways to do research with the help of AI agents. So many research questions to explore. So ma…
The tweet expresses enthusiasm for AI research opportunities, particularly with AI agents, and highlights the rapid progress in machine learning.
Agents That Build Better Training Data (25 minute read)
Autodata introduces an agentic data scientist that iteratively generates and refines synthetic training data, with meta-optimization to further improve data quality, achieving better results on computer science and legal reasoning tasks.