Self-Model or Self-Simulation? A Machine Self-Awareness Index Averages Sub-Scores With No Common Referent and No Fixed Sign, Why Persistent Identity, Goal Stability and Memory Continuity Are Not Evidence of Self-Access, and the Validity Tests Any Composite Would Have to Pass

Abstract

Some proposals to quantify machine self-awareness combine several sub-scores - persistent identity, goal stability, cross-session memory continuity, contradiction detection, uncertainty awareness, self-prediction and introspective access - into one index. This paper asks whether such an index measures one thing, using the construct-validity tradition from psychometrics as the standard. It argues that it does not, and that the failure is structural rather than a matter of weighting. Three of the seven sub-scores are not evidence of self-access: persistent identity tracks a post-trained persona that published work finds only loosely tethered and moved by the very meta-reflective questioning a self-awareness probe involves; goal stability has no fixed sign, because the persistence that is desired against environmental pressure is the persistence that alignment-faking and shutdown-resistance studies report against the principal; and memory continuity is a property of the retrieval scaffold. Two further sub-scores are behavioural, and nothing reviewed shows they require access a third party with the same inputs lacks. Only injection-style probes and controlled self-prediction are designed to test privileged access, and even they split by paradigm, move sharply with prompting and fine-tuning, and are contested by input-only baselines. The paper consolidates the published measurements, states a four-step admission test (referent, access, sign, then covariation net of capability and post-training) that any composite would have to pass, and names the multitrait-multimethod study that would settle the open part. No experiment was run.

The literature search, drafting and citation verification for this paper were carried out with AI assistance under the author's direction. Every arXiv citation was machine-verified against its live arXiv Atom API record, and every other citation against its Crossref or DOI record, during drafting (title and author list checked against the record returned). Every quantitative claim is taken from the abstract, full text or a figure of the source credited with it; full-text numbers were read from the sources' own arXiv HTML renderings rather than from summaries. No experiment was run and no number in this paper was measured by its author. Table 1 re-presents numbers published by the cited papers, each named on its row; Figure 1 plots published values from seven cited papers with no transformation. The referent / access / sign audit, Algorithm 1 and the validation design in Section 12 are original conceptual synthesis by the author, not empirical results, and are presented as such. Ten classic references (Bollen and Lennox 1991; Borsboom, Mellenbergh and van Heerden 2004; Campbell and Fiske 1959; Cronbach and Meehl 1955; Edwards and Bagozzi 2000; Fleming and Lau 2014; Gallup 1970; Maniscalco and Lau 2012; Messick 1995; Nisbett and Wilson 1977) were verified as records; where no deposited abstract was available they are cited only for the concept or distinction each is standardly credited with naming.

About the author

Pranay Mahendrakar is an ai specialist and large language model engineer based in Bengaluru, India. He builds production artificial intelligence systems and publishes open-access research on how those systems fail. See all 66 papers by Pranay Mahendrakar, or his ORCID record.