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Introduces SAGE, the first end-to-end LLM-driven multi-agent framework for fraud detection, using a Data Diagnostic Tree and Markov decision process with natural-language gradients to optimize models under class imbalance. Experiments show significant F1 improvements over baselines across five datasets.
Introduces the autoresearch project, which breaks down the AI research process into a verifiable loop (fixed environment, single editable file, fixed metric, Git rollback), enabling AI agents to perform controllable and reproducible experiment iterations; also mentions the 12-factor-agents checklist.
This paper introduces yvsoucom-iterkit, a deterministic, log-driven AutoML framework for reproducible pipeline optimization in healthcare risk prediction, evaluated on diabetes and stroke datasets with over 18,000 pipeline configurations, achieving strong performance and revealing structured search spaces with component redundancy.
Researchers from Google and Meta propose AutoTTS, a framework using AI agents to automatically discover and refine test-time scaling strategies for LLMs without human intervention. The agent successfully identified complex, coordinated reasoning mechanisms that outperformed manual baselines at a low computational cost.