arXiv AI June 30, 2026 By Carmen Ng, Gjergji Kasneci

Auditing LLM-Governed Social Robots with Culture-Specific Moral Gradients

arXiv:2606. 28345v1 Announce Type: cross Abstract: LLM-governed social robots increasingly decide who receives real-world assistance first.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv AI.

arXiv AI

Jun 29

The Alignment Target Problem: Divergent Moral Judgments of Humans, AI Systems, and Their Designers

arXiv:2604. 24155v3 Announce Type: replace-cross Abstract: The project of aligning machine behavior with human values raises a basic problem: whose moral expectations should guide AI decision-making?

By Benjamin Minhao Chen, Xinyu Xie

agents robotics benchmarks safety

arXiv AI

Aug 11

CORDA: A Benchmark for Hierarchical Harm-Centric Moral Reasoning in Large Language Models

arXiv:2608. 08061v1 Announce Type: new Abstract: The key question in moral judgement is not simply whether someone chooses the "right" answer, but how they decide what matters most when moral principles conflict.

By Siddarth Singh, Victoria Williams, Simon Rosen, Ebenezer Gelo, Helen Sarah Robertson, Ibrahim Suder, Benjamin Rosman, Geraud Nangue Tasse, Steven James

llms agents robotics benchmarks

arXiv AI

Aug 5

Language model agents show in-group trust bias invisible to standard behavioural audits

arXiv:2605. 28114v2 Announce Type: replace Abstract: Language-model agents are moving from single-user assistants into persistent networks that build trust and reputation with one another, and the same models increasingly control physically embodied robots as well as software.

By Messi H. J. Lee

llms agents robotics safety

arXiv AI

Jun 12

LLMs Can Better Capture Human Judgments--With the Right Prompts

arXiv:2606. 12754v1 Announce Type: cross Abstract: Are large language models (LLMs) bad at capturing human judgment?

By Danica Dillion, Chen Cecilia Liu, Baihui Wang, Daniele Barolo, Tanmay Rajore, Niket Tandon, Pranathi Ravikumar, Kurt Gray

llms safety

arXiv AI

Jun 12

MoReBench: Evaluating Procedural and Pluralistic Moral Reasoning in Language Models, More than Outcomes

arXiv:2510. 16380v2 Announce Type: replace-cross Abstract: As AI systems progress, we rely more on them to make decisions with us and for us.

By Yu Ying Chiu, Michael S. Lee, Rachel Calcott, Brandon Handoko, Paul de Font-Reaulx, Rapha\"el Milli\`ere, Paula Rodriguez, Chen Bo Calvin Zhang, Ziwen Han, Udari Madhushani Sehwag, Yash Maurya, Christina Q Knight, Harry R. Lloyd, Florence Bacus, Conor Downey, Mantas Mazeika, Bing Liu, Yejin Choi, Mitchell L Gordon, Sydney Levine

llms benchmarks

arXiv AI

5d ago

MOSAIC: Unveiling the Moral, Social and Individual Dimensions of Large Language Models

arXiv:2603. 00048v2 Announce Type: replace-cross Abstract: Large Language Models (LLMs) are increasingly deployed in sensitive applications including psychological support, healthcare, and high-stakes decision-making.

By Erica Coppolillo, Emilio Ferrara

llms benchmarks

Related stories

The Alignment Target Problem: Divergent Moral Judgments of Humans, AI Systems, and Their Designers

CORDA: A Benchmark for Hierarchical Harm-Centric Moral Reasoning in Large Language Models

Language model agents show in-group trust bias invisible to standard behavioural audits

LLMs Can Better Capture Human Judgments--With the Right Prompts

MoReBench: Evaluating Procedural and Pluralistic Moral Reasoning in Language Models, More than Outcomes

MOSAIC: Unveiling the Moral, Social and Individual Dimensions of Large Language Models