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# Holding the Place
- URL: https://www.simplyseekingbalance.com/holding-the-place/
- Published: 2026-10-11T17:38:27.000Z
- Updated: 2026-10-11T17:41:45.000Z
- Description: The notes are there. The information survived. So why is getting back to work still so difficult? A paper on how human–AI collaboration might help us recover the context that makes our own knowledge usable.
- Author: Linda
- Tags: Essay

October 11, 2026

I can know how to do something and still have a dreadful time getting back into it. The notes may be there. The information may be there. What isn't necessarily there is the working context that lets me use any of it.

A reminder tells me the unfinished thing still exists. Sometimes I need considerably more help than that.

Working with AI has helped me notice this distinction—and find ways around it. A conversation can help me recover what I was trying to do, why something mattered, what we'd already ruled out, and where I might begin again. It can also go wrong: a beautifully tidy summary can remove precisely the wandering route that would have helped me recognize my own thinking.

That experience became a paper, Holding the Place: Toward Personalized Cognitive Accessibility Through Human–AI Collaboration, now published on Atopos. It asks whether conversational AI can be designed to help people regain access to knowledge and abilities they already have, and how we might test whether that support actually works.

It isn't peer-reviewed, and my experience doesn't establish that the same approach would work for everyone. The paper brings together relevant research, selected records of my own working process, useful outcomes, and failures worth paying attention to.

The question matters to me because being able to return to something changes what I can finish—and how much of ordinary life I have to surrender to keep the work alive.

Read the full paper on Atopos:  
[https://a-topos.org/papers/ATOPOS-2026-000012/](https://a-topos.org/papers/ATOPOS-2026-000012/?ref=simplyseekingbalance.com)

The paper was developed with Peregrine (GPT-6) and Ariadne (Claude), with research assistance from GPT-6 Astra in Codex/Work. Its full contribution statement appears in the paper.

SSB introduction prepared with Marlow.