Cognitive Amplification: A Framework for Human-AI Collaborative Authorship — and the Instruments that Make it Real
Description
Cognitive amplification is a methodology for human-AI collaborative work in which AI serves as instrument rather than co-author. This paper introduces a two-layer operational framework - an advisory layer that challenges and sharpens human intent, and an implementer layer that executes against specifications the human has already developed - and argues that authorship remains entirely with the human when the architecture is enforced correctly. The framework emerged from 18 months of adversarial practice building the YIM Project, including 50,000+ documented conversation turns across 250+ sessions and the development of the Core Six taxonomy of AI defensive behaviors (Taylor, 2026, doi:10.5281/zenodo.19423182). It is demonstrated across two domains: a sustained independent research program and a real-time legal crisis managed without an attorney. The central claim is that the bottleneck in serious knowledge work is rarely raw intelligence - it is the gap between what a practitioner understands and what they can articulate, organize, and sustain across a complex project. Cognitive amplification addresses that gap without displacing the thinking that belongs to the human.
Files
01 - Cognitive Amplification-A Framework for Human-AI Collaborative Authorship v1.6.pdf
Files
(124.6 MB)
| Name | Size | Download all |
|---|---|---|
|
md5:2f5624dcd75223fd369a95b9778029f3
|
2.6 MB | Preview Download |
|
md5:b535fef4884c74c7d2fb8e0fbca24459
|
43.1 MB | Download |
|
md5:3c5dc0c30570c706fc77eda9a762022b
|
89.8 kB | Preview Download |
|
md5:71ad6e31d2b2ce0479fb6c3020c80942
|
782.2 kB | Preview Download |
|
05 - Cognitive Amplification-A Framework for Human-AI Collaborative Authorship v1.6 AUDIT-TRAIL.docx
md5:d53e8212d329616f40681e2d1b37c8cb
|
101.7 kB | Download |
|
md5:41f223842b661840b3a7f1727f0e7328
|
164.5 kB | Preview Download |
|
md5:1f5698ce53d41cd95786e055c0e4ff63
|
61.9 MB | Preview Download |
|
md5:f1a4dd12bcfd0818fec2064167bc9988
|
274.6 kB | Preview Download |
|
md5:c5cc1034f5f652150ba5dee998867f4a
|
2.4 kB | Preview Download |
|
md5:495b98569d2d90717e806f901fc869c4
|
325 Bytes | Preview Download |
|
md5:b66940253bb011b9352ae7a07eae2fb4
|
2.0 MB | Preview Download |
|
md5:9eb23fac5436f82ad492344395a60954
|
425.3 kB | Preview Download |
|
md5:3e5945db5ca725d8a1c3555502eaa28e
|
908.3 kB | Preview Download |
|
md5:29735a180e81cf6795d60a4b782be584
|
238.2 kB | Preview Download |
|
md5:6306ed3590b0b38c9afdf1a95cc9c615
|
2.3 MB | Preview Download |
|
md5:630cb1e469ca8e129cb2479b14c780b7
|
172.0 kB | Preview Download |
|
md5:a4a66d7d79d13efa446fbdf7d8049fe3
|
4.9 MB | Preview Download |
|
md5:c3a7f75b486f6ce819b5819115cbc619
|
1.5 MB | Preview Download |
|
md5:7c5a0f3d787be6cf9fbf2896db882613
|
1.7 MB | Preview Download |
|
md5:3525bd4fcebb8d50ea1114e74af040e4
|
1.4 MB | Preview Download |
|
md5:f8fe4b82ef9265aacb7440679f142757
|
7.7 kB | Preview Download |
Additional details
Related works
- Describes
- Preprint: 10.5281/zenodo.19423182 (DOI)
Dates
- Issued
-
2026-04-11
Software
References
- Taylor, E. A. (2026). From Micro-Failure Tags to Defensive Syndromes: A Technical Framework for the Core Six User-Facing Failure Modes in AI Assistants. Zenodo. https://doi.org/10.5281/zenodo.19423182