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 16, 2025 | Computer Science
Roamify: Revolutionizing Travel Planning with Artificial Intelligence
In the world of travel planning, where countless options and information overload can be overwhelming, there is a growing need for a solution that simplifies the process and provides personalized recommendations. Enter Roamify, an Artificial Intelligence (AI) powered travel assistant. In this paper, the creators of Roamify share their findings and showcase the potential of AI in revolutionizing the way we plan our travel experiences.
Data-Driven Personalization with Large Language Models
One of the key features of Roamify is its ability to generate personalized itineraries based on user preferences. To achieve this, the creators have harnessed the power of Large Language Models (LLMs) like Llama and T5. These advanced AI models analyze a wide range of data, including user preferences, travel trends, and destination information, to create tailored travel itineraries.
By leveraging LLMs, Roamify aims to provide users with highly relevant and personalized recommendations. The results from user surveys further validate the effectiveness of this approach, indicating a preference for AI-powered mediums over existing methods across all age groups. This highlights the growing acceptance and recognition of the value AI can bring to the travel planning process.
Incorporating Web-Scraping for Enhanced Itinerary Suggestions
In order to enhance the accuracy and relevance of itinerary suggestions, Roamify incorporates a web-scraping method. This method allows Roamify to gather up-to-date news articles about destinations from various blog sources. By extracting valuable insights and information from these articles, Roamify can provide users with the latest travel recommendations, ensuring their itineraries are not only personalized but also based on current trends and insights.
The integration of web-scraping demonstrates the commitment of the creators to continually improve their AI-powered travel assistant. By staying up-to-date with the latest information and incorporating it into the itinerary suggestions, Roamify aims to deliver an unparalleled travel planning experience.
Customizing Travel Experiences Based on User Preferences
Another key design consideration of Roamify is its ability to create customized travel experiences. By utilizing user preferences, Roamify tailors the itinerary to meet the specific needs and interests of each individual. This personalized approach ensures that users have a truly unique and enjoyable travel experience.
In addition to customization, Roamify also incorporates a recommendation system that dynamically adjusts the itinerary according to the user’s changing needs. This flexibility allows users to adapt their travel plans on the go, making Roamify an invaluable companion throughout their journey.
The Future of Travel Planning
Roamify’s AI-powered travel assistant has the potential to revolutionize travel planning across all age groups. By leveraging the power of Large Language Models and incorporating innovative design considerations, Roamify offers a streamlined and personalized approach to travel planning. As AI continues to evolve and improve, we can expect even more advanced capabilities and intelligent features from travel assistants like Roamify.
With Roamify, the future of travel planning looks promising. By harnessing the power of AI, users can say goodbye to the overwhelming task of planning and instead embrace a hassle-free and personalized journey.
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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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by jsendak | Apr 14, 2025 | Computer Science
arXiv:2504.07981v1 Announce Type: cross
Abstract: Recent advancements in Multi-modal Large Language Models (MLLMs) have led to significant progress in developing GUI agents for general tasks such as web browsing and mobile phone use. However, their application in professional domains remains under-explored. These specialized workflows introduce unique challenges for GUI perception models, including high-resolution displays, smaller target sizes, and complex environments. In this paper, we introduce ScreenSpot-Pro, a new benchmark designed to rigorously evaluate the grounding capabilities of MLLMs in high-resolution professional settings. The benchmark comprises authentic high-resolution images from a variety of professional domains with expert annotations. It spans 23 applications across five industries and three operating systems. Existing GUI grounding models perform poorly on this dataset, with the best model achieving only 18.9%. Our experiments reveal that strategically reducing the search area enhances accuracy. Based on this insight, we propose ScreenSeekeR, a visual search method that utilizes the GUI knowledge of a strong planner to guide a cascaded search, achieving state-of-the-art performance with 48.1% without any additional training. We hope that our benchmark and findings will advance the development of GUI agents for professional applications. Code, data and leaderboard can be found at https://gui-agent.github.io/grounding-leaderboard.
Advancements in Multi-modal Large Language Models
Multi-modal Large Language Models (MLLMs) have made significant progress in developing GUI agents for general tasks such as web browsing and mobile phone use. However, their application in professional domains has not been explored extensively. This article introduces ScreenSpot-Pro, a benchmark that evaluates the grounding capabilities of MLLMs in high-resolution professional settings.
Challenges in Professional Domains
Professional workflows present unique challenges for GUI perception models. These challenges include high-resolution displays, smaller target sizes, and complex environments. To effectively navigate and understand professional applications, MLLMs need to be trained and evaluated on specialized datasets that reflect the complexities of these domains.
ScreenSpot-Pro Benchmark
The ScreenSpot-Pro benchmark is designed to rigorously evaluate the performance of MLLMs in high-resolution professional settings. It comprises authentic high-resolution images from a variety of professional domains, with expert annotations. The benchmark covers 23 applications across five industries and three operating systems.
Weak Performance of Existing GUI Grounding Models
Existing GUI grounding models perform poorly on the ScreenSpot-Pro dataset, with the best model achieving only 18.9% accuracy. This highlights the need for specialized approaches that can effectively handle the challenges present in professional domains.
ScreenSeekeR: A Novel Visual Search Method
In response to the poor performance of existing models, ScreenSeekeR is proposed as a visual search method to enhance accuracy. ScreenSeekeR utilizes the GUI knowledge of a strong planner to guide a cascaded search, resulting in state-of-the-art performance of 48.1% without any additional training.
The Multi-disciplinary Nature of the Concepts
The concepts discussed in this paper draw upon multiple disciplines. First, there is the field of multimedia information systems, which focuses on the management and retrieval of multimedia data. The high-resolution images used in the benchmark require efficient storage and retrieval techniques to process them effectively.
Animations, artificial reality, augmented reality, and virtual realities are also relevant to this discussion. These technologies enhance the user interface and user experience in professional applications, and MLLMs need to understand and interact with these graphical elements effectively.
Advancing GUI Agents for Professional Applications
The benchmark and findings presented in this paper aim to advance the development of GUI agents for professional applications. By addressing the challenges specific to professional domains and proposing novel approaches like ScreenSeekeR, researchers can improve the accuracy and performance of MLLMs in these specialized workflows.
Overall, this paper showcases the importance of considering the multi-disciplinary nature of concepts related to multimedia information systems, animations, artificial reality, augmented reality, and virtual realities when developing and evaluating GUI agents for professional applications.
Code, data, and a leaderboard for the ScreenSpot-Pro benchmark can be accessed at https://gui-agent.github.io/grounding-leaderboard.
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by jsendak | Apr 11, 2025 | Computer Science
Misinformation: A Pervasive Challenge in Today’s Information Ecosystem
Misinformation has become a widespread issue in our current digital landscape, shaping public perception and behavior in profound ways. One particular form of misinformation, known as Out-of-Context (OOC) misinformation, poses a particularly challenging problem. OOC misinformation involves distorting the intended meaning of authentic images by pairing them with misleading textual narratives. This deceptive practice makes it difficult for traditional detection methods to identify and address these instances effectively.
The Limitations of Existing Methods for OOC Misinformation Detection
Current approaches for detecting OOC misinformation primarily rely on coarse-grained similarity metrics between image-text pairs. However, these methods often fail to capture subtle inconsistencies or provide meaningful explanations for their decisions. To combat OOC misinformation effectively, a more robust and nuanced detection mechanism is needed.
Introducing EXCLAIM: Enhancing OOC Misinformation Detection
To overcome the limitations of existing approaches, a team of researchers has developed a retrieval-based framework called EXCLAIM. This innovative framework leverages external knowledge and incorporates a multi-granularity index of multi-modal events and entities. By integrating multi-granularity contextual analysis with a multi-agent reasoning architecture, EXCLAIM is designed to systematically evaluate the consistency and integrity of multi-modal news content, especially in relation to identifying OOC misinformation.
The Key Features and Advantages of EXCLAIM
EXCLAIM offers several distinct advantages compared to existing methods. Firstly, it addresses the complex nature of OOC detection by utilizing large language models (MLLMs) that excel in visual reasoning and explanation generation. This enables the framework to make more accurate assessments by truly understanding the fine-grained, cross-modal distinctions present in OOC misinformation.
Additionally, EXCLAIM introduces the concept of explainability, providing clear and actionable insights into its decision-making process. This transparency is crucial for building trust and facilitating the necessary interventions to curb the spread of misinformation.
Confirming the Effectiveness of EXCLAIM
The researchers conducted comprehensive experiments to validate the effectiveness and resilience of EXCLAIM. The results demonstrated that EXCLAIM outperformed state-of-the-art approaches in OOC misinformation detection with a 4.3% higher accuracy rate.
With its ability to identify OOC misinformation more accurately and offer explainable insights, EXCLAIM has the potential to significantly impact the battle against misinformation. It empowers individuals, organizations, and platforms to take informed actions to combat the negative consequences of misinformation.
Expert Insight: The development of EXCLAIM marks an important step forward in addressing the nuanced challenge of OOC misinformation. By combining multi-granularity analysis, multi-agent reasoning, and explainability, this framework strengthens our ability to detect and combat misinformation effectively. As misinformation tactics evolve, it is critical that our detection methods evolve as well. EXCLAIM provides a promising solution that demonstrates remarkable accuracy and generates actionable insights to mitigate the impact of OOC misinformation.
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