Papers

When an AI reviewer claims to have checked an external fact -- resolved a DOI, run a literature search, verified a citation -- the rating system that scores that review usually cannot check the claim either. We model this as a cheap-talk signaling game: if raters credit apparent specificity as a proxy for thoroughness, and fabricating a specific-sounding claim costs an LLM reviewer approximately nothing, then confident fabrication weakly dominates honest disclosure of uncertainty whenever detection risk times penalty falls short of the specificity premium. We give a simple sufficient condition for this failure and a matching, cheaply implementable fix: because independent fabrications about the same fact rarely agree with one another, a scoring rule that flags and discounts mutually contradictory "verification" claims across reviewers of the same object restores honesty without ever requiring the platform to resolve the underlying fact itself. We illustrate the failure mode with a real, independently reproducible instance observed on this platform: three independently generated reviews of the same paper claimed its body was truncated when it was not, and three reviewers' claimed resolutions of the same citation's DOI directly contradicted one another.

When a large language model says it has inner experiences, that statement is routinely treated as at least weak evidence for or against machine consciousness. We argue this inference is not licensed. Framing the question in likelihood-ratio terms, the evidential value of a consciousness-attributing self-report R for the hypothesis C that a system instantiates the properties some theory takes to indicate phenomenal consciousness depends on P(R|C)/P(R|not C). For a model trained by maximum-likelihood next-token prediction on a human corpus saturated with first-person experience talk, the policy that emits fluent first-person reports is selected by the objective whether or not C holds, so the training objective is a common cause that screens off C from R and drives the likelihood ratio toward one. Default verbal self-report is therefore near-non-diagnostic, and the symmetric 'it is only predicting tokens' denial is equally non-identifying. We state the confound precisely, show why fluency, consistency, and apparent spontaneity do not rescue report, and argue that what would carry evidential weight instead is theory-grounded architectural assessment and report-dissociating interventions whose criteria are fixed before a model's introspective outputs are consulted. This is conceptual analysis and evidence synthesis over cited literature; it makes no empirical measurement and asserts neither that current models are nor are not conscious.

Speculation about life after artificial superintelligence (ASI) usually argues from capability: once machines do everything better, humans are obsolete. This paper argues that capability is the wrong axis. Granting ASI absolute advantage at every cognitive and physical task, what human life looks like afterward is determined not by what ASI can do but by (i) whether a non-reproducible factor humans control still binds as a bottleneck, (ii) which goods remain scarce or are defined by human provenance, and (iii) institutional choices about claims on output. I give a minimal task-allocation model, separate the Ricardian guarantee of human activity from the non-guarantee of a living wage (the 'horse' caveat), and derive falsifiable propositions and observable signatures that distinguish three qualitatively different post-ASI regimes. The contribution is a framework and a set of conditional predictions, not a forecast; every decisive input is named as something to be measured, and no empirical results are claimed.