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Evidence record 1313 · automatically gathered

Auditing Proprietary Alignment in Large Language Models: A Comparative Framework Without a Ground-Truth Standard

Large language models (LLMs) are increasingly released and deployed through opaque development and deployment pipelines, enabling model providers to inject intentional, provider-specific policies without officially announcing them. As a result, various models have been reported to generate responses reflecting proprietary rules and organizational interests, leading to censorship or misinformation on controversial topics. However, systematic identification of such alignment remains a fundamental

Record details

Published: 7 June 2026
Source: arXiv
Category: Research
Topics: Safety & alignment · Misinformation · Transparency
Retrieved: 14 July 2026

source-onlyevidence status

These records share source-supplied organisations, an exact publisher byline, automatic topics or regions. The reason is shown on every link; related does not mean supporting, agreeing with or verifying this record.

Shared topic: Safety & alignment · Shared topic: Misinformation

Sycophancy as a Multilingual Alignment Failure: How Safety Degrades Across Languages, Topics, and Models

arXiv · 7 June 2026

Shared topic: Safety & alignment · Shared topic: Transparency

Explaining Black-Box Language Models: Learning to Optimize Linguistically-Structured Word Subsets

arXiv · 7 June 2026

Shared topic: Safety & alignment · Shared topic: Transparency

Agent Economics: An Entropy-Controlled Pluralistic Alignment Framework for Preventing Artificial Hivemind in Autonomous Agents

arXiv · 8 June 2026

Shared topic: Safety & alignment · Shared topic: Transparency

Position: Preventing AI-Generated CSAM Necessitates New Approaches to AI Safety

arXiv · 9 June 2026

Shared topic: Safety & alignment · Shared topic: Transparency

Is Fairness Truly Fair? Towards Reliable Lipschitz Fairness in Multi-Task Learning via Fixed-\texorpdfstring{$δ$}{delta} Alignment

arXiv · 9 June 2026

Shared topic: Misinformation · Shared topic: Transparency

Ethical and Technical Limits of Deepfake Speech Datasets

arXiv · 9 June 2026

How to cite this record

ethics.ai (7 June 2026), “Auditing Proprietary Alignment in Large Language Models: A Comparative Framework Without a Ground-Truth Standard,” evidence record 1313, https://ethics.ai/record/1313 (originally published by arXiv).

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