Ziming Liu proposes OPHIS framework for mechanistic auto-research
The system scales insights instead of optimizing metrics blindly.
Current Auto-Research are Shakespeare's monkeys, typing randomly, hoping brilliance emerge from chaos. Today, we @MetaCircle_AI propose "Mechanistic Auto-Research", not using any LLMs, but surpassing RSI's results. Scale insight, not compute. https://meta-circle.com/blog/ophis-a-new-paradigm-for-autoresearch
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1 postThis is obviously in the right direction (I think many of us @Recursive_SI would agree and are already doing this). Research is about discovering insights, rather than just optimizing a metric blindly. To improve on an existing method, you need to first understand what each component does and what are their weaknesses, and then try to target those weaknesses to propose new interventions. Exposing full training dynamics and diagnostic metrics to agents is one way of approaching this (eg, a lot of times we want agents to look at load balancing in MoE rather than just val loss). I’m hopeful that a lot of the deep learning mystery will become much better understood with the help of autoresearch, as long as we set the goal as understanding-maxxing rather than purely bench-maxxing, and a lot of these insights will end up being more transferable than specific tricks discovered on nanogpt.
Current Auto-Research are Shakespeare's monkeys, typing randomly, hoping brilliance emerge from chaos. Today, we @MetaCircle_AI propose "Mechanistic Auto-Research", not using any LLMs, but surpassing RSI's results. Scale insight, not compute. https://meta-circle.com/blog/ophis-a-new-paradigm-for-autoresearch