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The AI Alignment Toolkit Research Assistant is designed to augment AI alignment researchers by addressing two key challenges: proactive insight extraction from new research and automating alignment research using AI agents. This project establishes an end-to-end pipeline where AI agents autonomously complete tasks critical to AI alignment research

Cite this work:

@misc {

title={

@misc {

},

author={

Luciano Hanyon Wu

},

date={

7/29/24

},

organization={Apart Research},

note={Research submission to the research sprint hosted by Apart.},

howpublished={https://apartresearch.com}

}

Reviewer's Comments

Reviewer's Comments

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Jacques Thibodeau

I think this project is a great start! Pulling from the research database and seeing how different documents relate to your project makes sense. I think the things that need to be improved are the following: 1) making it more consumable, researchers are not gonna read that much text for each paper so there needs to be a way to show only the things that would interest the individual researchers and then allowing them to dive deeper if they would like to 2) using some cross-paper brainstorming that don’t just look at one paper, but multiple papers to come up with ideas 3) Allow the researcher to provide direction at different parts of the pipeline in order to improve the overall quality of the output (”I want more papers like this and less like that”, “only tell me about how x relates to my work, ignore the rest”, “once you come across a high-relevance paper, do a deep dive looking at other things to improve the output even more”). I think this kind of project will be useful infrastructure that we can later throw more capable AI agents at to help us generate better research ideas. It could also be done periodically as you are working through projects so that it pro-actively suggests new things for you based on what you are working on and your open questions. Lastly, improving the prompts from the model so that it is providing more alignment-relevant outputs would be valuable, instead of leaving the prompt to be more general (e.g. if the project is related to mech interp, then you pull up mech interp specific prompt that makes the model better at gathering insights).

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This work was done during one weekend by research workshop participants and does not represent the work of Apart Research.
This work was done during one weekend by research workshop participants and does not represent the work of Apart Research.