MÖVE presents a new German-language benchmark evaluating 39 LLMs on performance and governance criteria using ten public-administration datasets.
& Liang, P
9 Pith papers cite this work, alongside 43 external citations. Polarity classification is still indexing.
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Ethical constraint evidence on open-weight AI models decays with a half-life of 1.31 derivation steps on Hugging Face, creating a governance horizon at seven generations where 80% of models lack traceable information.
The 2025 AI Agent Index catalogs technical and safety details for 30 deployed AI agents and finds low developer transparency on safety, evaluations, and societal impacts.
Empirical study of eight LLMs finds overuse of popular libraries like NumPy in up to 45% of unnecessary cases and strong default preference for Python even when suboptimal.
Generative LMs in laissez-faire open-ended prompting settings disproportionately generate subordinated portrayals of minoritized race, gender, and sexual orientation identities at rates hundreds to thousands of times higher than empowering ones.
TRUST is a decentralized AI auditing framework that decomposes reasoning into HDAGs, maps agent interactions via the DAAN protocol to CIGs, and uses stake-weighted multi-tier consensus to achieve 72.4% accuracy while proving a Safety-Profitability Theorem that rewards honest auditors.
Tonal variations in prompts cause systematic but model-dependent accuracy changes in LLMs on objective multiple-choice questions.
Introduces CRAI-MCF, an eight-module framework distilling 217 parameters from 240 projects into a quantitative sufficiency criterion for cross-model LLM comparison grounded in Value Sensitive Design.
Taxation of AI activities can correct externalities, redistribute costs and gains, and support regulation, though instruments like corporate taxes, consumption taxes, and excises vary in feasibility, measurement challenges, and effects on innovation.
citing papers explorer
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M\"OVE: A Holistic LLM Benchmark for the German Public Sector
MÖVE presents a new German-language benchmark evaluating 39 LLMs on performance and governance criteria using ten public-administration datasets.
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A governance horizon for ethical-use constraints in open-weight AI models
Ethical constraint evidence on open-weight AI models decays with a half-life of 1.31 derivation steps on Hugging Face, creating a governance horizon at seven generations where 80% of models lack traceable information.
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The 2025 AI Agent Index: Documenting Technical and Safety Features of Deployed Agentic AI Systems
The 2025 AI Agent Index catalogs technical and safety details for 30 deployed AI agents and finds low developer transparency on safety, evaluations, and societal impacts.
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A Study of LLMs' Preferences for Libraries and Programming Languages
Empirical study of eight LLMs finds overuse of popular libraries like NumPy in up to 45% of unnecessary cases and strong default preference for Python even when suboptimal.
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Laissez-Faire Harms: Algorithmic Biases in Generative Language Models
Generative LMs in laissez-faire open-ended prompting settings disproportionately generate subordinated portrayals of minoritized race, gender, and sexual orientation identities at rates hundreds to thousands of times higher than empowering ones.
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TRUST: A Framework for Decentralized AI Service v.0.1
TRUST is a decentralized AI auditing framework that decomposes reasoning into HDAGs, maps agent interactions via the DAAN protocol to CIGs, and uses stake-weighted multi-tier consensus to achieve 72.4% accuracy while proving a Safety-Profitability Theorem that rewards honest auditors.
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Mind Your Tone: Does Tone Alter LLM Performance?
Tonal variations in prompts cause systematic but model-dependent accuracy changes in LLMs on objective multiple-choice questions.
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Human-aligned AI Model Cards with Weighted Hierarchy Architecture
Introduces CRAI-MCF, an eight-module framework distilling 217 parameters from 240 projects into a quantitative sufficiency criterion for cross-model LLM comparison grounded in Value Sensitive Design.
-
Taxing Artificial Intelligence
Taxation of AI activities can correct externalities, redistribute costs and gains, and support regulation, though instruments like corporate taxes, consumption taxes, and excises vary in feasibility, measurement challenges, and effects on innovation.