This research highlights the heightened threats to data integrity and stakeholder trust in these evolving ecosystems through an intensive examination of the literature, initiating a pioneering discourse emphasizing fostering a foundation for developing secure and trustworthy Liquid AI environments.
M. Agbese, Niko Mäkitalo, Muhammad Waseem et al.· IoT· 6 citations· ⚡1
Carbon-Aware Governance Gates (CAGG), an architectural extension that embeds carbon budgets, energy provenance, and sustainability-aware validation orchestration into human-AI governance layers, is proposed.
M. Abbasi, T. Mikkonen, Petri Ihantola et al.· 2026 IEEE 23rd International...· 0 citations
This work proposes prediction-powered evaluation, a framework that combines limited human judgments with large-scale automatic scores to obtain data-efficient system comparisons that are provably unbiased, and introduces the Prediction-Powered Saving Ratio (PPSR), a meta-metric that measures how much human annotation a...
Mingqi Gao, Anthony B. Sicilia, Weiye Shi· 1 citation
In an application to 363 U.S. metropolitan areas, embedding-based clusters of LLM-generated economic descriptions recover interpretable economic archetypes and separate local employment dynamics more sharply than clustering on model residuals, or on a curated set of industry and demographic covariates.
S. Freyaldenhoven· 0 citations
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Simulations with an exact belief-propagation baseline on the same trees show that the pairwise rule's error curve is mildly non-monotone in $\ell, so an optimal finite depth exists and BP saturates strictly faster, at an effective per-layer ratio below $\kappa$ that the authors identify.
Language models prompted with cultural personas increasingly stand in for human respondents in cross-cultural research. Their responses separate personas cleanly, and that separation is read as evidence of a cultural point of view. We show that the separation is real, that the point of view is not, and that one criteri...
Self-Organized Conformal Prediction (SOCP), a calibration scheme that discovers input-space groups with an unsupervised Self-Organizing Map (SOM) trained without calibration labels, is introduced, providing a concise route to group-local calibration without supervised partitions or predictor retraining with a diagnosti...
Louis Berthier, A. Shokry, M. Moreaud et al.· arXiv.org· 0 citations
It is argued that, even when an LLM has been well aligned in (post-)training, it may still fail to maximise the aligned value in reasoning, and the utility discrepancy between a model's deployed reasoning strategy and its rational counterpart whose responses maximise utility in the steepest direction is formalised.
Comprehensive empirical evaluations demonstrate PEAR significantly improves average accuracy over the strongest debate baselines, and theoretically characterize PEAR as an equivariant sparse router: it preserves accuracy under agent relabeling while reducing routing complexity and improving generalization.
Yang Feng, Ziwei Xu, Xia Hu et al.· arXiv.org· 0 citations
Under a manifold model with the frame bundle structure, it is shown that it can accurately recover the parallel transport with landmark-constrained diffusion from a point cloud, and hence asymptotically LA-VDM converges to the connection Laplacian.
Numerical experiments demonstrate competitive accuracy and lower computational cost than expectation-maximization across a range of multidimensional settings.
This work introduces an RKHS localization method that learns a data-adaptive weight from covariates and reformulates the target conditional moment at the target point as a weighted unconditional moment, yielding a prediction-powered estimator and confidence interval that reduce variance when the predictor is informativ...
Yang Sui, Jin Zhou, Hua Zhou et al.· arXiv.org· 2 citations
A machine-learning approach called CheMeleon provides a practical way to develop new drugs or other molecules with much less detailed, expensive experimentation.
