The future of human-AI integration in high-stakes environments-ranging from defense operations to emergency response-requires AI systems that function as intuitive teammates rather than isolated tools. Yet a persistent perspective gap remains: video understanding models rely on third-person, broadcast-style views, while multi-agent reinforcement learning (MARL) systems operate on egocentric inputs but often depend on centralized critics during training, reducing the need for decentralized policies to internalize teamcentric structure. As a result, agents may optimize behavior without learning policy-level representations of the tactical picture grounded in their own observations. To address this gap, we propose Egocentric Team AI, a research direction centered on learning implicit, distributed Common Operating Pictures from first-person views. Building on our prior work with the X-Ego-CS dataset and Cross-Ego Contrastive Learning (CECL), we outline how cross-egocentric representation alignment can be extended from passive video understanding to active multi-agent control. Specifically, we propose integrating cross-ego contrastive objectives into MARL within a multi-agent Doom-based environment as a testbed for decentralized, team-aware policy learning. By positioning crossegocentric alignment as an inductive bias for decentralized coordination, this work charts a path toward embodied systems capable of adaptive cooperation without explicit communication.
Agentic AI refers to autonomous systems powered by Large Language Models (LLMs) that can perceive environmental states, reason through complex decisions, and execute actions to achieve business objectives with minimal human intervention. It can assist in building a scalable framework for autonomous decision-making in business applications. This paper proposes a framework for designing and implementing autonomous agents that solve real-world business problems. The extensive pretraining of LLMs enables agents to navigate vast problem spaces more effectively, facilitating accelerated problem-solving. The methodology adopts a modular architecture that divides the complex business problems into sub-tasks, each of which can be handled by sub-agents. Furthermore, the integration of learning and adaptation mechanisms allows these systems to modify their responses to environmental feedback, thus improving performance over time. Our findings indicate that while Agentic AI outperforms manual baseline and traditional rule-based approaches in terms of operational speed and adaptability, challenges related to LLM stochastic behavior in edge cases persist, necessitating human-AI collaboration. Ultimately, this architecture enables the deployment of autonomous decision-making agents capable of navigating the ever-evolving business requirements of modern enterprises.
As machine intelligences become more sophisticated, individuals and societies will encounter myriad attributions of consciousness to machines. Some of these attributions may be correct; many will be illusory. This paper argues that computationalist functionalist theories of machine consciousness can and should seek to explain illusions of machine consciousness. A theory that accounts for illusions reduces the risk of producing false positives---theories that explain merely the appearance of consciousness rather than genuine consciousness. Further, given the consequential nature of consciousness attributions, whether veridical or illusory, such a theory helps society navigate issues of blame and recourse when mistaken attributions cause harm.
Earth-observing satellite constellations are increasingly expected to operate autonomously in dynamic, resource-constrained, and failure-prone environments. As constellation size and heterogeneity grow, centralized and static task scheduling paradigms struggle to provide the adaptability required to maintain mission continuity under disruptions, intermittent connectivity, and limited onboard storage. This paper presents a decentralized task rescheduling framework inspired by a service-industry scheduling analogy that models satellites as resource constrained agents operating under memory restrictions, spatial access limitations, and intermittent offloading opportunities. A multi-criteria redistribution algorithm prioritizes memory availability, proximity, capability, and workload to reassign tasks following agent loss. Through large-scale simulation, we demonstrate that the proposed framework preserves high-priority task throughput and fair workload distribution despite irreversible capacity loss. The resulting system provides a physically motivated abstraction for studying resilient, decentralized scheduling and establishes a bridge between intuitive heuristic policies and future learning-based autonomy for heterogeneous low Earth orbit satellite constellations.
The rapid integration of generative AI into education, industry, and everyday creative practices has introduced significant efficiencies, and yet it has also raised ethical concerns around authenticity, originality, and the erosion of human creativity. Many generative AI systems prioritize speed and content production over user development. Simply delivering synthesized outputs tends to limit opportunities for users to focus on the creative process and creative thinking. Additionally, users might unintentionally plagiarize others' ideas from the synthesized output from AI systems without realizing it. In this paper, we present InspireTrace, an early-stage generative AI prototype designed to support creativity through guided inspiration rather than direct solution, intentionally leaving synthesis and ideation to the user. By explicitly attributing source materials, InspireTrace aims to reduce plagiarism risks and foster a healthier, more trustworthy creativity ecosystem. We discuss the system's design rationale and its implications from creative practice in human-AI collaboration.
