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ZENODO
Preprint . 2025
License: CC BY
Data sources: ZENODO
ZENODO
Preprint . 2025
License: CC BY
Data sources: Datacite
ZENODO
Preprint . 2025
License: CC BY
Data sources: Datacite
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The Alchemist's Paradox N=1 Science: Unlocking Next-Gen AI via "Observer Transformation"

Authors: Ishibashi, Ryuhei;

The Alchemist's Paradox N=1 Science: Unlocking Next-Gen AI via "Observer Transformation"

Abstract

[NeurIPS 2025 Handout / Manifesto] Modern science has systematically excluded the "observer" to guarantee reproducibility. However, this exclusion has severed the connection to a reality that demands observer transformation. This manifesto argues that the current impasse in AI Scaling Laws and the conceptual voids in neuroscience stem directly from this omission. We propose a return to "N=1 Science"—utilizing outliers not as noise, but as ultimate observational instruments to capture the dynamic evolution of intelligence. Key Propositions: The Alchemist's Paradox: Re-evaluating alchemists not as failed chemists, but as scientists who correctly integrated the "transformation of the observer." Limits of Scaling Laws: Why mere statistical averaging of parameters ($N$) will never yield a qualitative leap in AI. The "Glass Box" Protocol: An experimental design involving 24/365 multi-modal monitoring (Time-Domain fNIRS, EEG, HRV) of outlier individuals to synchronize physiological states with cognitive "Eureka" moments. Integrated Coherence Verification: Operationalizing "expert intuition" as a falsifiable scientific variable for detecting high-potential outliers. This document serves as a blueprint for bridging the gap between subjective experience (qualia) and objective biometric data, aiming to unlock the next generation of Artificial Intelligence.

Keywords

N=1 Science, Scaling Laws, Artificial Intelligence, Twice-Exceptional (2E), Outliers, Kernel Flow, Phenomenology, fNIRS, Consciousness Studies, Micro-phenomenology, Gifted Education, Neuroscience

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average
Green