The paper "Why did My Robot Just Change Personality? Prompting Guidelines for a Grounded Robot Persona in LLM-Based HRI" addresses the lack of clear prompt design in human‑robot interaction using large language models. It proposes a framework and a structured prompt template with eight functional components to specify, bound, and adapt robot behavior. The authors base their guidelines on a review of prior work and on survey data from 27 HRI experts, highlighting issues such as unclear robot personality, the need for user adaptation, and ethical concerns around safety, deception, and governance.
arXiv:2607. 06344v1 Announce Type: cross Abstract: While personalisation is becoming a defining capability in human-robot interaction (HRI), the existing literature on responsible personalisation remains fragmented, offering isolated accounts of ethical risks without a structured understanding of how they emerge across interaction contexts.
While personalisation is becoming a defining capability in human-robot interaction (HRI), the existing literature on responsible personalisation remains fragmented, offering isolated accounts of ethical risks without a structured understanding of how they emerge across interaction contexts. This gap is particularly critical in HRI, where robots' embodiment and social presence can amplify and reshape such risks or generate new types of risks.
arXiv:2602. 12873v5 Announce Type: replace-cross Abstract: Generative social robots (GSRs) powered by large language models enable adaptive, conversational tutoring but also introduce risks such as misinformation, overreliance, and privacy violations.
Behaviora is a preliminary conceptual architecture that represents both external and internal behavior of robots and agents in an addressable form. It defines a Behavior Episode composed of components derived from behavior taxonomies, each assigned a persistent Internet of Behaviors (IoB) Address. The architecture also includes a Style Profile to describe how behavior is expressed, an Experience Profile to capture internal state influencing execution, and a Behavior Compiler to translate these representations into platform‑specific actions.
The paper examines whether fine‑tuning large language models (LLMs) with personality‑labelled data improves their ability to act as socially interactive agents. Two small open‑weight LLMs were fine‑tuned on a corpus of personality‑labelled social media posts and dialogues, and the resulting models were evaluated in various social interaction scenarios by independent LLM judges. The findings show that the fine‑tuned models do not outperform their baseline counterparts in role‑playing personalities, though they offer comparable text quality and increased linguistic diversity for the Qwen models; low inter‑rater agreement limits confidence in the results, suggesting future work should focus on training data quality and domain alignment.
The paper reviews how large language models have evolved into agents that can influence external environments through tool use, interface operation, delegation, state retention, virtual world inhabitation, and robotic control. It critiques the narrative of a single march toward autonomy, distinguishing model competence from system integration, persistence, and safe authority. The authors find that action-interface expansion is well documented, while robust completion, recovery, authorization, and independent verification remain less proven, and they propose a framework of justified delegation to guide future research.
arXiv:2601. 09869v2 Announce Type: replace Abstract: Anthropomorphisation -- the phenomenon whereby non-human entities are ascribed human-like qualities -- has become increasingly salient with the rise of large language model (LLM)-based conversational agents (CAs).
arXiv:2607. 24190v1 Announce Type: cross Abstract: Social robots that rely on large language models for conversation are unable to retain information across sessions.
arXiv:2609.14236v1 Announce Type: cross Abstract: With the rapid proliferation of large language model (LLM)-based systems, AI companions have emerged as conversational agents designed to cultivate e...
arXiv:2606. 28345v1 Announce Type: cross Abstract: LLM-governed social robots increasingly decide who receives real-world assistance first.
arXiv:2608. 09857v1 Announce Type: cross Abstract: Advances in advanced artificial intelligence tools have sparked research in robot autonomy, but the development of such systems has largely focused on execution rather than verifying the feasibility actions planning models propose.
arXiv:2609.37853v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly deployed as social agents, yet credible human-like interaction requires more than fluent responses or per...