SIA — фреймворк самоулучшения ИИ-агентов

★ 1.9K

SIA (Self-Improving AI) is a research framework for autonomous self-improvement of AI systems: a loop where a language-model agent iteratively updates both the agent's harness and the target model's weights to raise performance on a specific benchmark task. Reach for it in a research setting when you want to automatically optimize a model or agent for a task — Kaggle ML pipelines, GPU-kernel speedups, scientific problems — without hand-tuning configurations. It coordinates three roles: a Meta-Agent that generates an initial target agent from the task description, a Target Agent that attempts the task and logs its actions, and a Feedback Agent that reviews the logs and improves the target agent, so the system refines itself generation over generation. It is the official implementation of a research paper and a tool for experiments and benchmarks, not a ready-made agent for everyday tasks: it needs a defined benchmark, a metric, and compute for repeated runs.