---
id: ai-memory-palaces-and-superdidactic-ai/01-summary
title: 'AI memory palaces and superdidactic AI: 1. Summary'
report: https://ai.hiina.space/reports/ai-memory-palaces-and-superdidactic-ai.md
section: 1
version: 1
updated: '2026-10-27'
author: Hiina
---

Index of everything on this site: https://ai.hiina.space/index.md

# AI memory palaces and superdidactic AI: 1. Summary

- Section 1 of 9 of the report "AI memory palaces and superdidactic AI" by Hiina (v1, updated 2026-10-27)
- Report overview and contents: https://ai.hiina.space/reports/ai-memory-palaces-and-superdidactic-ai.md
- Next: https://ai.hiina.space/reports/ai-memory-palaces-and-superdidactic-ai/02-context-reinstatement-prompt-engineering-the-human.md

## 1. Summary

| Part of the spectrum | What's actually true | Status |
| --- | --- | --- |
| Memory palaces | Large effects for ordered lists only; a richer room doesn't help | Proven, wrong target |
| Project re-entry cues | Own-words cues \~90% vs 5–17%; automated traces 2x notes; showing agent reasoning catches bad plans 81% vs 55% | Strong pieces, never assembled; gap is real |
| AI tutoring | 0.1–0.3 SD at scale, \~0.6 in a tight trial; 2 sigma was never real | Real, modest |
| AI persuasion as mirror | Information density drives it; personalization adds \~0.4 pp | Teaching gets the dense part, not the retrieval |
| ML spaced repetition | Neural schedulers beat FSRS on log loss; LLM cards need human-picked targets; varied cues beat fixed | Scheduler solved, deck replacement promising |
| Sleep learning and TMR | New learning in sleep is tiny and implicit; TMR g ≈ 0.13–0.29; slow-wave stimulation ≈ 0 | Mostly no |
| Nudges and JITAIs | Bias-corrected effects small and decaying; capture and specific cues matter more than timing | Real, small |
| Neural UIs and BCI | Pixel-rendered desktops can't hold state or text; BCIs reached \~mouse parity at \~10 bits/s | Far |
| Uploads and "the soul" | Emulation not close even for a worm; distilling a work policy has 50 years of precedent | Policy yes, self no |

The most promising build is a re-entry card on each decision-queue item (section 7, build 1). It's a weekend of work, and the queue's own history gives the baseline: in the motivating setup, asks answered live took minutes, while asks left in notes took 12–62 days. This report builds on a companion report on personal web-surfing models and an ought-driven tab feed, and doesn't repeat the companion reports on AI enfeeblement and idle supervisors, or on AI meditation and flow steering.

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Text licensed CC BY 4.0 (attribution: Hiina, ai.hiina.space). Source: https://github.com/hiinaspace/ai-hiina-space