The paper proposes a developmental framework for autonomous artificial agents that emphasizes learning social norms and alignment through direct interaction with dynamic environments. It argues that intrinsic motivations such as curiosity and competence can guide exploration, but also complicate alignment with human goals. By drawing parallels to child development, the authors suggest that regulatory sandboxes serve as pedagogical spaces where agents gradually acquire moral agency and adapt their behaviors through experience and cooperation.
arXiv:2604. 14990v2 Announce Type: replace Abstract: The prospect of Artificial General Intelligence (AGI) is increasingly driving institutional decisions, and alignment of AGI is a hard problem.
arXiv:2606. 23991v1 Announce Type: new Abstract: What is an agent?
arXiv:2507.11482v5 Announce Type: replace Abstract: Artificial learning systems are graduating from passive learners to increasingly autonomous agents, lending pragmatic urgency to the question of wh...
arXiv:2607. 13560v1 Announce Type: cross Abstract: Recent advances in generative and embodied AI have been driven by large-scale predictive learning over multimodal data.
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?
arXiv:2609.17325v1 Announce Type: new Abstract: Biological cells can be viewed as individual, interacting agents whose collective dynamics give rise to adaptive behaviour at multiple levels of organi...
arXiv:2601. 21570v2 Announce Type: replace Abstract: The field of Embodied AI is witnessing a rapid evolution toward general-purpose robotic systems, fueled by high-fidelity simulation and large-scale data collection.
arXiv:2608. 03910v1 Announce Type: new Abstract: As AI systems are deployed across increasingly diverse social contexts, alignment can no longer be framed as the optimization of a single, unified set of values.
arXiv:2607. 00001v1 Announce Type: new Abstract: Most approaches to AI alignment treat human preferences as fixed targets to be inferred and optimized.
The article introduces a mathematical model of the Motivated Emotional Mind cognitive architecture for embodied intelligent systems. It formalizes a re‑entrant loop that integrates feedforward processing, lateral interactions, and feedback pathways, along with representational selection mechanisms that govern adaptive responses. The model binds exteroceptive and interoceptive signals, bodily-motivational context, and memory traces into associative structures called semblions, enabling motivated learning that incorporates need thresholds, goal generation, and regulatory constraints.
The paper introduces Synthetic Linguistic Agency (SLA), a framework that defines linguistic agency in terms of embodiment, participation, and precariousness. It presents two studies: one that operationalizes SLA criteria and identifies existing systems, and another that builds an Embodied Mortal Agent (EMA) using mortality‑grounded reinforcement learning. Experiments show the EMA’s linguistic choices depend on its body and social history, influence partner behavior, and adapt over time, demonstrating SLA in an artificial agent.
arXiv:2406. 14373v3 Announce Type: replace Abstract: The emergence of Large Language Models (LLMs) and advancements in Artificial Intelligence (AI) offer an opportunity for computational social science research at scale.