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The paper introduces the concept of normative competence for large language models (LLMs), defined as the ability to infer community norms from interaction rather than static knowledge. Using a multi‑agent debate environment governed by synthetic norms, the authors show that baseline LLMs fail to learn norms even when it improves accuracy, and that adding normative modules yields highly sensitive, non‑generalizable results. The study also reveals that LLMs indiscriminately copy idiosyncratic, non‑normative behaviors, indicating a failure to distinguish enforced rules from noise.
arXiv:2606. 27826v3 Announce Type: replace Abstract: Embodied agents driven by multimodal large language models (MLLMs) can often complete everyday tasks from visual observations, but goal achievement does not establish whether they proactively respect unstated social norms.
arXiv:2608. 13250v1 Announce Type: cross Abstract: Normative datasets are often used to train and align AI systems, but the norms they contain can function as action-guiding patterns rather than neutral moral knowledge.
arXiv:2609.05437v1 Announce Type: new Abstract: Previous AI alignment efforts have focused primarily on first-order social norms -- teaching models what is socially acceptable or unacceptable (e.g.,...
arXiv:2607. 09766v1 Announce Type: new Abstract: AI agents are increasingly deployed in shared environments where they pursue diverse goals and compete for rewards.
arXiv:2606. 27826v1 Announce Type: new Abstract: Multimodal large language models (MLLMs) are increasingly deployed as embodied planners in egocentric environments, where task success requires not only achieving instructed goals but also acting in socially appropriate ways.