Publication Library
Physical AI Agents Integrating Cognitive Intelligence with Real-World Action
Description: Vertical AI Agents are revolutionizing industries by delivering domain-specific intelligence and tailored solutions. However, many sectors, such as manufacturing, healthcare, and logistics, demand AI systems capable of extending their intelligence into the physical world, interacting directly with objects, environments, and dynamic conditions. This need has led to the emergence of Physical AI Agents--systems that integrate cognitive reasoning, powered by specialized LLMs, with precise physical actions to perform real-world tasks. This work introduces Physical AI Agents as an evolution of shared principles with Vertical AI Agents, tailored for physical interaction. We propose a modular architecture with three core blocks--perception, cognition, and actuation--offering a scalable framework for diverse industries. Additionally, we present the Physical Retrieval Augmented Generation (Ph-RAG) design pattern, which connects physical intelligence to industry-specific LLMs for real-time decision-making and reporting informed by physical context. Through case studies, we demonstrate how Physical AI Agents and the Ph-RAG framework are transforming industries like autonomous vehicles, warehouse robotics, healthcare, and manufacturing, offering businesses a pathway to integrate embodied AI for operational efficiency and innovation.
Created At: 16 January 2025
Updated At: 16 January 2025
SITUATIONAL AWARENESS The Decade Ahead
Description: SITUATIONAL AWARENESS: The Decade Ahead
Created At: 01 January 2025
Updated At: 01 January 2025
A Survey on Multi-Generative Agent System Recent Advances and New Frontiers
Description: Multi-generative agent systems (MGASs) have become a research hotspot since the rise of large language models (LLMs). However, with the continuous influx of new related works, the existing reviews struggle to capture them comprehensively. This paper presents a comprehensive survey of these studies. We first discuss the definition of MGAS, a framework encompassing much of previous work. We provide an overview of the various applications of MGAS in (i) solving complex tasks, (ii) simulating specific scenarios, and (iii) evaluating generative agents. Building on previous studies, we also highlight several challenges and propose future directions for research in this field.
Created At: 24 December 2024
Updated At: 24 December 2024
Quantum teleportation coexisting with classical communications in optical fiber
Description: The ability for quantum and conventional networks to operate in the same optical fibers would aid the deployment of quantum network technology on a large scale. Quantum teleportation is a fundamental operation in quantum networking, but has yet to be demonstrated in fibers populated with high-power conventional optical signals. Here we report, to the best of our knowledge, the first demonstration of quantum teleportation over fibers carrying conventional telecommunications traffic. Quantum state transfer is achieved over a 30.2-km fiber carrying 400-Gbps C-band classical traffic with a Bell state measurement performed at the fiber’s midpoint. To protect quantum fidelity from spontaneous Raman scattering noise, we use optimal O-band quantum channels, narrow spectro-temporal filtering, and multi-photon coincidence detection. Fidelity is shown to be well maintained with an elevated C-band launch power of 18.7 dBm for the single-channel 400-Gbps signal, which we project could support multiple classical channels totaling many terabits/s aggregate data rates. These results show the feasibility of advanced quantum and classical network applications operating within a unified fiber infrastructure.
Created At: 23 December 2024
Updated At: 23 December 2024
PolyModel for Hedge Funds Portfolio Construction Using Machine Learning
Description: The domain of hedge fund investments is undergoing significant transformation, influenced by the rapid expansion of data availability and the advancement of analytical technologies. This study explores the enhancement of hedge fund investment performance through the integration of machine learning techniques, the application of PolyModel feature selection, and the analysis of fund size. We address three critical questions: (1) the effect of machine learning on trading performance, (2) the role of PolyModel feature selection in fund selection and performance, and (3) the comparative reliability of larger versus smaller funds. Our findings offer compelling insights. We observe that while machine learning techniques enhance cumulative returns, they also increase annual volatility, indicating variability in performance. PolyModel feature selection proves to be a robust strategy, with approaches that utilize a comprehensive set of features for fund selection outperforming more selective methodologies. Notably, Long-Term Stability (LTS) effectively manages portfolio volatility while delivering favorable returns. Contrary to popular belief, our results suggest that larger funds do not consistently yield better investment outcomes, challenging the assumption of their inherent reliability. This research highlights the transformative impact of data-driven approaches in the hedge fund investment arena and provides valuable implications for investors and asset managers. By leveraging machine learning and PolyModel feature selection, investors can enhance portfolio optimization and reassess the dependability of larger funds, leading to more informed investment strategies.
Created At: 19 December 2024
Updated At: 19 December 2024