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Evidence record 7217 · automatically gathered

Questionnaire Responses Do not Capture the Safety of AI Agents

As AI systems advance in capabilities, measuring their safety and alignment to human values is becoming paramount. A fast-growing field of AI research is devoted to developing such assessments. However, most current advances therein may be ill-suited for assessing AI systems across real-world deployments. Standard methods prompt large language models (LLMs) in a questionnaire-style to describe their values or behavior in hypothetical scenarios. By focusing on unaugmented LLMs, they fall short of

Record details

Published: 15 March 2026
Source: arXiv
Category: Research
Topics: Safety & alignment · Agents & autonomy
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: Agents & autonomy

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Shared topic: Safety & alignment · Shared topic: Agents & autonomy

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Shared topic: Safety & alignment · Shared topic: Agents & autonomy

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Shared topic: Safety & alignment · Shared topic: Agents & autonomy

Directional Embedding Smoothing for Robust Vision Language Models

arXiv · 16 March 2026

Shared topic: Safety & alignment · Shared topic: Agents & autonomy

Efficient Morphology-Control Co-Design via Stackelberg Proximal Policy Optimization

arXiv · 16 March 2026

Shared topic: Safety & alignment · Shared topic: Agents & autonomy

OMNIFLOW: A Physics-Grounded Multimodal Agent for Generalized Scientific Reasoning

arXiv · 16 March 2026

How to cite this record

ethics.ai (15 March 2026), “Questionnaire Responses Do not Capture the Safety of AI Agents,” evidence record 7217, https://ethics.ai/record/7217 (originally published by arXiv).

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