An AI system's output is not the fact or world state it appears to describe, but rather an engineered representation. We propose a semantic framework to describe AI systems, to be able to examine the correctness of such representations. To do so, we distinguish what is justified by accepted domain knowledge, what reference sources say, and what the system can currently use. This allows us to give precise definitions to common failures: extrapolation, refuted or unsupported assertion, sources versus knowledge mismatch, stale or refuted source, added hypotheses, unsupported use... We hope our framework gives a useful vocabulary for specifying and checking AI systems whose outputs, citations, tool calls, and world-changing actions must be justified by reliable claims and explicit authority rather than apparent fluency.
We perform a late-time cosmological study, we compare the performance of two Dirac-Born-Infeld (DBI) type k-essence scalar field extensions of the model to the standard framework and a scenario using the Chevallier-Polarski-Linder (CPL) equation of state parametrization. We solve background dynamics numerically as functions of redshift and incorporate them into a Bayesian inference pipeline accelerated by machine learning. We use a Flax-based surrogate emulator to replace repeated direct integrations of the ODE system, reducing computational cost. A hybrid scheme that combines stochastic variational inference (SVI) with No-U-Turn Hamiltonian Monte Carlo constrains cosmological parameters using the Pantheon+SH0ES Type Ia supernova sample, DESI BAO (DR2) data, and cosmic chronometer measurements without CMB-based priors. In both DBI k-essence formulations, present-day dark energy equations of state are consistent with cosmic acceleration, indicating a -like regime with a modest redshift dependence. The model is marginally favored by conventional model selection measures such as , AIC, BIC, and DIC, which are based on goodness of fit and penalized. However, Bayesian predictive measures like WAIC and PSIS-LOO show no significant differences between , , and DBI k-essence scenarios. All have similar model weights and out-of-sample predictive performance for the datasets. Thus, DBI k-essence models mimic the success of the classic paradigm while allowing controlled, redshift-dependent deviations from a strict cosmological constant that are consistent with present late-time observations.
A BSTRACT Introduction: Stroke is a major cause of death and disability globally, with significant functional and cognitive impairments affecting survivors. Rehabilitation strategies are crucial in improving functional outcomes such as muscle strength, cognitive function, and activities of daily living (ADL). This study evaluates the effectiveness of Argigold Max supplementation in improving functional recovery in stroke patients. Materials and Methods: This observational study involved 218 poststroke patients who received Argigold Max supplementation once daily for 3 months, alongside a standard rehabilitation program. Functional outcomes were assessed using the manual muscle testing (MMT) scale for muscle strength, the brief interview for mental status (BIMS) for cognitive function, and the Barthel index (BI) for ADL. Ethics approval was obtained from the Independent Ethics Committee (Study number 01/2024). Descriptive statistics and paired t -tests were used for data analysis. Results: Significant improvements were observed in all measured parameters after Argigold Max supplementation for 3 months. The MMT scores showed a marked increase from a baseline mean of 2.29 ± 0.47–3.48 ± 0.54 ( P < 0.0001). Cognitive function, assessed by BIMS, improved significantly from a baseline mean of 8.23 ± 2.74–12.44 ± 2.04 ( P < 0.0001). The BI scores also demonstrated improvement (18.17 ± 2.24–92.71 ± 1.68), indicating better ADL independence. Global assessments revealed 98.3% of patients reporting improved or very much improved health status. Furthermore, 98.4% expressed satisfaction with the Argigold Max treatment. Conclusion: This study suggests that Argigold Max supplementation with standard rehabilitation significantly improves muscle strength, cognitive function, and ADL performance in stroke patients, supporting its role in enhancing recovery and quality of life.
