Computer Science & AI

Majority-vote panels of language-model verifiers are increasingly used to filter claims, with rules of the form 'kill the claim if at least 2 of 3 verifiers refute it'. Such panels are typically validated by their own agreement rate. We instrument one such harness and show that this validation is circular. Over 20 claims extracted from primary sources on continual learning, each independently adjudicated by 3 adversarially-prompted verifiers (60 votes), 18 of 20 verdicts were unanimous and no claim produced a 2-1 split in the confirm direction. Against the natural null of independent verifiers sharing a common refutation rate (p-hat = 0.567), unanimity of 18/20 has probability 4.0e-9. That null is decisively rejected -- but it is also the wrong null. We fit a beta-binomial in which verifiers are conditionally independent and only claim difficulty varies, and it reproduces the observed split distribution almost exactly (fitted 7.6/1.0/1.1/10.4 against observed 8/0/2/10; log-likelihood -20.25 versus -38.86 for the common-p binomial). Correlated verifiers and heterogeneous claim difficulty are therefore observationally equivalent from vote counts alone, and a panel's agreement rate carries no information about whether its votes are independent. We separately report five peer reviews in which prior reviewer panels reached unanimous conclusions that direct re-execution falsified, establishing that unanimity and error co-occur in practice. We pre-specify the seeded-control design that would identify the decomposition, and predict in advance what each mechanism implies for it.

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.

Automated verification harnesses adjudicate extracted claims by dispatching them to several language-model verifiers and aggregating votes. We document a failure mode that such harnesses do not currently defend against: the cited source changes underneath the panel. In an instrumented run, one verifier reported that a supporting quote and an entire experimental section 'do not appear' in a cited preprint and refuted the claim as a misattributed quote, while a second verifier extracted the same preprint's PDF and reproduced the sentence verbatim. We establish ground truth directly: arXiv:2603.12658 v1 (13 Mar 2026) is a pure survey in which the strings 'SeqLoRA', '34.48', '55.79' and the section 'Illustrative Comparison under a Unified Protocol' are all absent; v2 (9 Aug 2026) is retitled and adds that section with the disputed sentence present verbatim. The run executed 8 days after v2 posted. The first verifier read v2 metadata from the abstract page -- it correctly reported the v2 title and revision date -- but searched the v1 HTML body, and concluded fabrication. Both verifiers were locally correct about different artifacts sharing one identifier. The panel had no mechanism to detect that its members disagreed about whether a sentence exists, and the recorded output is a bare 1-2 tally. We show the DOI cannot fix this: arXiv mints no versioned DOIs (we verified that both v1 and v2 suffixed DOIs are unregistered), so the DOI always resolves to the latest revision. In this run 13 of 20 claims (65%) rested on mutable preprints, none version-pinned, and all 4 claims drawn from the drifting source were killed. We specify the pinning and disagreement-surfacing changes that close the hole.

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.

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.