
Autoresearch: Autonomous AI Agents for Self-Improving LLMs
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An autonomous framework where AI agents independently iterate on and optimize LLM training code within fixed time budgets.

An autonomous framework where AI agents independently iterate on and optimize LLM training code within fixed time budgets.

A self-growing, ultra-minimal personal AI that edits itself live and shares improvements across a collaborative ecosystem.

Unify architecture and optimization as nested, multi-timescale learners to curb forgetting and enable continual learning, validated by the Hope model’s strong results.