The comment under “Future Directions” at the end of that paper would seem to apply to the use of AI in software development, business management, and other areas as well:
“For AI[-assisted mathematics] research, improving research judgement and research-state representation may require training and evaluation on records of mathematics as a process, including failed approaches, strategic decisions, and evolving assessments of evidence. For mathematical practice, while these limitations persist, human expertise is likely to remain most valuable in these global functionalities: deciding when to persist or reframe a direction, and maintaining an accurate representation of accumulated progress.”
The section where the Research-state representation is assessed to be fragile is the work I want to see more of.
I've already accepted that models know everything and will only get smarter, its cope to pretend hallucination is achilles heel. I want to understand how to build/use harnesses that converge on goal states and allow me to contribute human expertise like intuition and taste.
The components they call bulletin, session report, and especially the curated summary are the parts that make their work go forward.
related paper on the long-horizon AI research system used: https://arxiv.org/pdf/2608.11195
The comment under “Future Directions” at the end of that paper would seem to apply to the use of AI in software development, business management, and other areas as well:
“For AI[-assisted mathematics] research, improving research judgement and research-state representation may require training and evaluation on records of mathematics as a process, including failed approaches, strategic decisions, and evolving assessments of evidence. For mathematical practice, while these limitations persist, human expertise is likely to remain most valuable in these global functionalities: deciding when to persist or reframe a direction, and maintaining an accurate representation of accumulated progress.”
Thanks for posting. I'm doing some AI vibemathing and running into exactly the difficulties they describe.
The section where the Research-state representation is assessed to be fragile is the work I want to see more of.
I've already accepted that models know everything and will only get smarter, its cope to pretend hallucination is achilles heel. I want to understand how to build/use harnesses that converge on goal states and allow me to contribute human expertise like intuition and taste.
The components they call bulletin, session report, and especially the curated summary are the parts that make their work go forward.
Interesting. I wonder if there has also been any recent progress on the Legendre constant. Kidding.