by jsendak | Apr 16, 2025 | AI
arXiv:2504.11301v1 Announce Type: new Abstract: Large Language Model (LLM)-based agents have demonstrated strong capabilities across a wide range of tasks, and their application in the medical domain holds particular promise due to the demand for high generalizability and reliance on interdisciplinary knowledge. However, existing medical agent systems often rely on static, manually crafted workflows that lack the flexibility to accommodate diverse diagnostic requirements and adapt to emerging clinical scenarios. Motivated by the success of automated machine learning (AutoML), this paper introduces a novel framework for the automated design of medical agent architectures. Specifically, we define a hierarchical and expressive agent search space that enables dynamic workflow adaptation through structured modifications at the node, structural, and framework levels. Our framework conceptualizes medical agents as graph-based architectures composed of diverse, functional node types and supports iterative self-improvement guided by diagnostic feedback. Experimental results on skin disease diagnosis tasks demonstrate that the proposed method effectively evolves workflow structures and significantly enhances diagnostic accuracy over time. This work represents the first fully automated framework for medical agent architecture design and offers a scalable, adaptable foundation for deploying intelligent agents in real-world clinical environments.
The article “Automated Design of Medical Agent Architectures: A Hierarchical and Expressive Framework” explores the potential of Large Language Model (LLM)-based agents in the medical domain. These agents have shown impressive capabilities in various tasks and are particularly promising in healthcare due to the need for high generalizability and interdisciplinary knowledge. However, current medical agent systems often lack flexibility and struggle to adapt to diverse diagnostic requirements and emerging clinical scenarios. In response, this paper introduces a novel framework inspired by automated machine learning (AutoML) for designing medical agent architectures. This framework defines a hierarchical and expressive agent search space that allows dynamic workflow adaptation through structured modifications at different levels. The proposed method conceptualizes medical agents as graph-based architectures composed of functional node types and supports iterative self-improvement guided by diagnostic feedback. Experimental results on skin disease diagnosis tasks demonstrate the effectiveness of the approach in evolving workflow structures and significantly enhancing diagnostic accuracy over time. This work represents the first fully automated framework for medical agent architecture design and provides a scalable and adaptable foundation for deploying intelligent agents in real-world clinical environments.
Automated Design of Medical Agent Architectures
Large Language Model (LLM)-based agents have proven to be highly capable in various tasks, making them particularly promising in the medical field, where high generalizability and interdisciplinary knowledge are crucial. However, existing medical agent systems often lack the flexibility to accommodate diverse diagnostic requirements and adapt to emerging clinical scenarios, relying instead on static, manually crafted workflows.
To address this limitation, this paper introduces a novel framework for the automated design of medical agent architectures, drawing inspiration from the success of automated machine learning (AutoML). The framework defines a hierarchical and expressive agent search space that enables dynamic workflow adaptation through structured modifications at the node, structural, and framework levels.
In this framework, medical agents are conceptualized as graph-based architectures composed of diverse, functional node types. These agents support iterative self-improvement guided by diagnostic feedback. By leveraging this feedback loop, the framework can evolve workflow structures and enhance diagnostic accuracy over time.
Experimental Results
To validate the effectiveness of the proposed method, experimental results on skin disease diagnosis tasks were conducted. The results demonstrated that the automated framework for medical agent architecture design significantly improves diagnostic accuracy over time.
Implications and Significance
This work introduces the first fully automated framework for medical agent architecture design. By offering a scalable and adaptable foundation, this framework opens up possibilities for deploying intelligent agents in real-world clinical environments. The automated design allows for the development of medical agents capable of adapting to new diagnostic requirements and clinical scenarios, enhancing patient care and outcomes.
Conclusion
The development of automated machine learning techniques has paved the way for innovations in various domains, and now, the medical field can benefit from these advancements. By introducing a novel framework for the automated design of medical agent architectures, this paper demonstrates the potential to revolutionize medical diagnosis and treatment. With the proposed method, medical agents can dynamically adapt to evolving requirements and enhance diagnostic accuracy, leading to improved patient care and outcomes in real-world clinical environments.
“The automated design of medical agent architectures offers a scalable and adaptable foundation for deploying intelligent agents in real-world clinical environments.”
The paper titled “Automated Design of Medical Agent Architectures” introduces a novel framework that aims to address the limitations of existing medical agent systems. These systems, although powerful, often rely on static workflows that cannot adapt to diverse diagnostic requirements or emerging clinical scenarios. The authors propose a hierarchical and expressive agent search space that enables dynamic workflow adaptation through structured modifications at different levels.
One notable aspect of this framework is its conceptualization of medical agents as graph-based architectures composed of diverse functional node types. This approach allows for flexibility and adaptability in the agent’s structure, enabling it to evolve over time. Additionally, the framework supports iterative self-improvement guided by diagnostic feedback, which is crucial for enhancing diagnostic accuracy.
The experimental results presented in the paper, focusing on skin disease diagnosis tasks, demonstrate the effectiveness of the proposed method. The evolved workflow structures significantly improve diagnostic accuracy over time. This is a promising finding as it suggests that the automated design of medical agent architectures can lead to better performance in real-world clinical environments.
The significance of this work lies in its potential to revolutionize the field of medical agent systems. By automating the design process, this framework offers a scalable and adaptable foundation for deploying intelligent agents in healthcare settings. This could have a profound impact on medical practice, as it would enable agents to keep up with evolving diagnostic requirements and adapt to new clinical scenarios.
However, there are several considerations to keep in mind when assessing the implications of this research. Firstly, the evaluation of the framework’s performance is limited to skin disease diagnosis tasks. It would be valuable to see how the automated design approach fares in other medical domains to assess its generalizability.
Furthermore, the paper does not discuss the potential ethical implications of deploying automated medical agents. As these agents interact directly with patients and make critical decisions, ensuring transparency, fairness, and accountability in their design and operation is crucial. Future research should address these ethical concerns to ensure the responsible and ethical deployment of automated medical agent systems.
In conclusion, the automated design framework proposed in this paper represents a significant step forward in the development of intelligent medical agent systems. By enabling dynamic workflow adaptation and iterative self-improvement, this framework has the potential to enhance diagnostic accuracy and improve patient care. Further research, including evaluation in different medical domains and addressing ethical considerations, will be essential to fully realize the benefits of this approach.
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by jsendak | Apr 16, 2025 | Computer Science
arXiv:2504.10739v1 Announce Type: new
Abstract: Comprehending extended audiovisual experiences remains a fundamental challenge for computational systems. Current approaches struggle with temporal integration and cross-modal associations that humans accomplish effortlessly through hippocampal-cortical networks. We introduce HippoMM, a biologically-inspired architecture that transforms hippocampal mechanisms into computational advantages for multimodal understanding. HippoMM implements three key innovations: (i) hippocampus-inspired pattern separation and completion specifically designed for continuous audiovisual streams, (ii) short-to-long term memory consolidation that transforms perceptual details into semantic abstractions, and (iii) cross-modal associative retrieval pathways enabling modality-crossing queries. Unlike existing retrieval systems with static indexing schemes, HippoMM dynamically forms integrated episodic representations through adaptive temporal segmentation and dual-process memory encoding. Evaluations on our challenging HippoVlog benchmark demonstrate that HippoMM significantly outperforms state-of-the-art approaches (78.2% vs. 64.2% accuracy) while providing substantially faster response times (20.4s vs. 112.5s). Our results demonstrate that translating neuroscientific memory principles into computational architectures provides a promising foundation for next-generation multimodal understanding systems. The code and benchmark dataset are publicly available at https://github.com/linyueqian/HippoMM.
HippoMM: A Biologically-Inspired Architecture for Multimodal Understanding
In the field of multimedia information systems, the challenge of comprehending extended audiovisual experiences has always been a major concern. Humans effortlessly integrate audio and visual information and make cross-modal associations through their hippocampal-cortical networks, which is a complex cognitive process. However, current computational systems struggle with this task.
In a recent study, researchers have introduced a novel architecture called HippoMM, which takes inspiration from the hippocampus, a brain region known for its role in memory formation and spatial navigation.
Key Innovations of HippoMM
- Hippocampus-inspired pattern separation and completion: HippoMM leverages the pattern separation and completion mechanisms observed in the hippocampus. This allows it to handle continuous audiovisual streams effectively.
- Short-to-long term memory consolidation: HippoMM converts perceptual details into semantic abstractions by consolidating them from short-term memory to long-term memory. This helps in transforming raw sensory information into meaningful representations.
- Cross-modal associative retrieval pathways: HippoMM facilitates modality-crossing queries by creating cross-modal associative retrieval pathways. This enables the system to provide integrated and contextually relevant responses.
Unlike existing retrieval systems that use static indexing schemes, HippoMM dynamically forms episodic representations by adapting temporal segmentation and dual-process memory encoding. This enables it to create a more cohesive and accurate understanding of multimodal content.
Implications and Future Perspectives
HippoMM demonstrates the potential of translating neuroscientific memory principles into computational architectures for advancing multimodal understanding systems. By incorporating hippocampal mechanisms, the system achieves a significantly higher accuracy of 78.2% compared to the state-of-the-art approaches that achieve 64.2% accuracy. Moreover, HippoMM also exhibits faster response times, taking only 20.4 seconds compared to 112.5 seconds with existing methods.
The interdisciplinary nature of this research is evident in the fusion of neuroscience, information systems, and artificial intelligence. By bridging these fields, HippoMM opens up new possibilities for applications in areas such as virtual reality, augmented reality, and artificial reality. The ability to comprehend and integrate audiovisual experiences is crucial in these domains, and HippoMM’s approach can significantly enhance the user experience and interaction.
The availability of the HippoVlog benchmark dataset and code on GitHub further promotes reproducibility and encourages researchers to build upon this work. It also enables the benchmarking of future multimodal understanding systems against the performance of HippoMM.
In conclusion, HippoMM represents a promising step towards developing next-generation multimodal understanding systems by leveraging the insights from neuroscience and computational modeling. The integration of audio and visual information through a biologically-inspired architecture brings us closer to bridging the gap between human-like understanding and computational systems.
References:
The original research paper and code can be accessed at:
https://arxiv.org/abs/2504.10739v1
The HippoMM benchmark dataset and code can be found on GitHub:
https://github.com/linyueqian/HippoMM
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by jsendak | Apr 16, 2025 | AI
arXiv:2504.10519v1 Announce Type: new
Abstract: AI Agents powered by Large Language Models are transforming the world through enormous applications. A super agent has the potential to fulfill diverse user needs, such as summarization, coding, and research, by accurately understanding user intent and leveraging the appropriate tools to solve tasks. However, to make such an agent viable for real-world deployment and accessible at scale, significant optimizations are required to ensure high efficiency and low cost. This paper presents a design of the Super Agent System. Upon receiving a user prompt, the system first detects the intent of the user, then routes the request to specialized task agents with the necessary tools or automatically generates agentic workflows. In practice, most applications directly serve as AI assistants on edge devices such as phones and robots. As different language models vary in capability and cloud-based models often entail high computational costs, latency, and privacy concerns, we then explore the hybrid mode where the router dynamically selects between local and cloud models based on task complexity. Finally, we introduce the blueprint of an on-device super agent enhanced with cloud. With advances in multi-modality models and edge hardware, we envision that most computations can be handled locally, with cloud collaboration only as needed. Such architecture paves the way for super agents to be seamlessly integrated into everyday life in the near future.
The Rise of Super Agents: Bridging the Gap Between AI and User Needs
AI Agents powered by Large Language Models have become an integral part of our daily lives. They have the potential to fulfill a wide range of user needs, from summarization and coding to research and much more. However, for these agents to be truly effective and accessible at scale, significant optimizations are required.
The Super Agent System, presented in this paper, aims to bridge the gap between user intent and agent capabilities. When a user prompt is received, the system first detects the intent behind the request. It then routes the request to specialized task agents equipped with the necessary tools or automatically generates agentic workflows. This process ensures that the agent can accurately understand user needs and efficiently solve tasks.
A key consideration in the design of the system is the deployment of AI assistants on edge devices such as phones and robots. This approach allows for faster response times and protects user privacy. However, the varying capabilities of different language models and the computational costs associated with cloud-based models pose challenges. To overcome this, the system explores a hybrid mode where the router dynamically selects between local and cloud models based on task complexity.
The introduction of an on-device super agent enhanced with cloud capabilities further enhances the potential of this architecture. With advancements in multi-modality models and edge hardware, a larger portion of computations can be handled locally, with cloud collaboration only utilized when necessary. This optimization not only improves efficiency and reduces costs but also paves the way for seamless integration of super agents into everyday life.
The concept of the Super Agent System is inherently multidisciplinary in nature. It combines elements of natural language processing, machine learning, cloud computing, edge computing, and user experience design. The successful implementation and deployment of such a system require close collaboration and expertise from these diverse fields.
In conclusion, the Super Agent System represents a significant step forward in making AI agents more powerful, efficient, and accessible. By accurately understanding user intent and leveraging appropriate tools, these agents can revolutionize the way we interact with technology. With continued advancements in technology, we can expect to see the seamless integration of super agents into our everyday lives in the near future.
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by jsendak | Apr 15, 2025 | Art
will explore the fascinating history of glassmaking, as well as showcase the exceptional works of contemporary glass artist, Hannah Gibson. By delving into the rich historical context of glass production and exploring Gibson’s innovative contributions to the field, Glass Renewed will offer visitors a comprehensive and inspiring experience.
Glass has a storied history that spans thousands of years, from its early origins in ancient Mesopotamia to its widespread use in various cultures throughout history. The invention of glassblowing techniques in the first century BCE revolutionized the production process, enabling the creation of intricate and delicate glass objects. It quickly became an indispensable material used in both functional and decorative applications.
Throughout history, glass has been employed in various ways, from the utilitarian glassware of the Industrial Revolution to the elaborate stained glass windows found in gothic cathedrals. Its versatility and ability to be molded into different shapes and colors have allowed for endless creative expressions. Glass has been used in architecture, art, and even as a medium for scientific experimentation.
Yet, the process of glassmaking has undergone significant changes over time. Technological advancements have made it possible to produce glass on a larger scale, opening up new opportunities for innovation. The industrialization of glass production during the 19th century brought mass-produced glassware to the masses. Still, it also posed challenges to traditional craftsmanship and artistic expression.
In highlighting Hannah Gibson’s work, Glass Renewed aims to showcase the resilience and reinvention of glass as an artistic medium. Gibson’s contemporary approach to glassmaking combines traditional techniques with a modern sensibility, pushing the boundaries of what is possible with the material. Her mastery of techniques such as glassblowing, kiln forming, and glass casting allows her to create breathtaking sculptures and installations that captivate the imagination.
Gibson’s works are informed by historical glassmaking traditions but also reflect her own unique artistic vision. She draws inspiration from the natural world, exploring themes of light, transparency, and movement. Her works often incorporate organic forms and textures, resulting in visually stunning pieces that blur the line between art and nature.
Glass Renewed offers an opportunity to reflect on the enduring appeal and evolving nature of glass as both a functional and artistic medium. By juxtaposing historical glass artifacts with contemporary works, visitors will gain a deeper understanding of the historical context and technical advancements that have shaped the art form. They will also witness the boundless potential for exploration and innovation that Gibson brings to her craft.
As the exhibition opens at the Museum of Brands in May, visitors will be able to witness the transformative power of glass firsthand. Through this immersive experience, they will undoubtedly come away with a renewed appreciation for the medium’s rich history and its exciting future in the hands of contemporary artists like Hannah Gibson.
Keywords: Glass Renewed, glassmaking, Hannah Gibson, history of glass, contemporary glass art, glassblowing, glass sculpture, Museum of Brands
The Museum of Brands to present a new exhibition opening end of May, Glass Renewed: Hannah Gibson & the History of Glass. This new exhibition
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by jsendak | Apr 15, 2025 | AI
arXiv:2504.08747v1 Announce Type: new
Abstract: The rapid growth of big data and advancements in computational techniques have significantly transformed sports analytics. However, the diverse range of data sources — including structured statistics, semi-structured formats like sensor data, and unstructured media such as written articles, audio, and video — creates substantial challenges in extracting actionable insights. These various formats, often referred to as multimodal data, require integration to fully leverage their potential. Conventional systems, which typically prioritize structured data, face limitations when processing and combining these diverse content types, reducing their effectiveness in real-time sports analysis.
To address these challenges, recent research highlights the importance of multimodal data integration for capturing the complexity of real-world sports environments. Building on this foundation, this paper introduces GridMind, a multi-agent framework that unifies structured, semi-structured, and unstructured data through Retrieval-Augmented Generation (RAG) and large language models (LLMs) to facilitate natural language querying of NFL data. This approach aligns with the evolving field of multimodal representation learning, where unified models are increasingly essential for real-time, cross-modal interactions.
GridMind’s distributed architecture includes specialized agents that autonomously manage each stage of a prompt — from interpretation and data retrieval to response synthesis. This modular design enables flexible, scalable handling of multimodal data, allowing users to pose complex, context-rich questions and receive comprehensive, intuitive responses via a conversational interface.
The rapid growth of big data and advancements in computational techniques have revolutionized the field of sports analytics. However, the integration of diverse data sources, including structured statistics, semi-structured sensor data, and unstructured media such as articles, audio, and video, presents significant challenges in extracting actionable insights. This type of data, known as multimodal data, requires a comprehensive approach to fully harness its potential.
Conventional systems in sports analytics often prioritize structured data and struggle to process and combine different content types effectively. This limitation hinders real-time sports analysis and prevents the extraction of meaningful insights from multimodal data.
To overcome these challenges, recent research emphasizes the significance of multimodal data integration to capture the complexity of real-world sports environments. One solution that addresses this issue is GridMind, a multi-agent framework introduced in this paper. GridMind utilizes Retrieval-Augmented Generation (RAG) and large language models (LLMs) to facilitate natural language querying of NFL data.
The approach taken by GridMind aligns with the evolving field of multimodal representation learning, where unified models are becoming increasingly crucial for real-time, cross-modal interactions. By unifying structured, semi-structured, and unstructured data, GridMind enables users to pose complex, context-rich questions and receive comprehensive, intuitive responses through a conversational interface.
The distributed architecture of GridMind employs specialized agents that autonomously handle each stage of a query, from interpretation and data retrieval to response synthesis. This modular design provides flexibility and scalability in handling multimodal data, making it possible to process and deliver comprehensive insights in real-time.
The concept of GridMind highlights the interdisciplinary nature of sports analytics and the importance of combining various data types. To fully leverage the potential of multimodal data, expertise from fields such as computer science, natural language processing, and data engineering is required. The integration of structured, semi-structured, and unstructured data underscores the need for a multi-disciplinary approach to sports analytics.
Moving forward, the field of sports analytics is likely to witness further advancements in multimodal data integration. The use of large language models and retrieval-augmented generation techniques will continue to enhance the natural language querying capabilities of analytics systems. Additionally, the development of more sophisticated conversational interfaces will enable users to interact seamlessly with sports analytics platforms, further democratizing access to valuable insights.
In conclusion, the integration of multimodal data poses significant challenges in sports analytics, but recent research, such as the GridMind framework, addresses these challenges by unifying structured, semi-structured, and unstructured data. This approach aligns with the evolving field of multimodal representation learning and highlights the multi-disciplinary nature of sports analytics. As the field continues to advance, further improvements in multimodal data integration and conversational interfaces can be expected, enabling more comprehensive and intuitive sports analysis.
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