by jsendak | Feb 27, 2025 | AI
arXiv:2502.18836v1 Announce Type: new Abstract: This benchmark suite provides a comprehensive evaluation framework for assessing both individual LLMs and multi-agent systems in real-world planning scenarios. The suite encompasses eleven designed problems that progress from basic to highly complex, incorporating key aspects such as multi-agent coordination, inter-agent dependencies, and dynamic environmental disruptions. Each problem can be scaled along three dimensions: the number of parallel planning threads, the complexity of inter-dependencies, and the frequency of unexpected disruptions requiring real-time adaptation. The benchmark includes detailed specifications, evaluation metrics, and baseline implementations using contemporary frameworks like LangGraph, enabling rigorous testing of both single-agent and multi-agent planning capabilities. Through standardized evaluation criteria and scalable complexity, this benchmark aims to drive progress in developing more robust and adaptable AI planning systems for real-world applications.
The article “arXiv:2502.18836v1” introduces a benchmark suite that aims to evaluate the performance of both individual LLMs (Language Model Models) and multi-agent systems in real-world planning scenarios. This comprehensive evaluation framework consists of eleven designed problems that range from basic to highly complex, incorporating important aspects such as multi-agent coordination, inter-agent dependencies, and dynamic environmental disruptions. The suite allows for scalability along three dimensions: the number of parallel planning threads, the complexity of inter-dependencies, and the frequency of unexpected disruptions requiring real-time adaptation. It provides detailed specifications, evaluation metrics, and baseline implementations using contemporary frameworks like LangGraph, enabling rigorous testing of single-agent and multi-agent planning capabilities. The ultimate goal of this benchmark is to drive progress in the development of more robust and adaptable AI planning systems for real-world applications, through standardized evaluation criteria and scalable complexity.
Driving Progress in AI Planning Systems: A New Benchmark Suite
Artificial Intelligence (AI) has come a long way, with significant advancements in various domains. However, there is still considerable room for improvement when it comes to real-world planning scenarios. To address this, a new benchmark suite has been developed, providing a comprehensive evaluation framework for assessing both individual LLMs (Language Model Models) and multi-agent systems.
The benchmark suite consists of eleven designed problems that progress from basic to highly complex. These problems incorporate key aspects such as multi-agent coordination, inter-agent dependencies, and dynamic environmental disruptions. Each problem within the suite can be scaled along three dimensions: the number of parallel planning threads, the complexity of inter-dependencies, and the frequency of unexpected disruptions requiring real-time adaptation.
One of the primary objectives of this benchmark suite is to establish standardized evaluation criteria. This will enable researchers and developers to test and compare the capabilities of both single-agent and multi-agent planning systems using a common framework. By defining specific evaluation metrics and providing baseline implementations using contemporary frameworks like LangGraph, this benchmark suite ensures rigorous testing of AI planning capabilities.
Traditionally, AI planning systems have relied on individual LLMs or single-agent approaches. While these can be effective in certain scenarios, they often struggle with complex real-world applications that involve multiple agents and dynamic environments. The benchmark suite aims to address this limitation by encouraging the development of more robust and adaptable AI planning systems.
Standardized Evaluation Criteria
Standardized evaluation criteria are crucial for fair and objective comparisons between different AI planning systems. The benchmark suite includes detailed specifications for each problem, defining the desired outcomes, constraints, and evaluation metrics. By using a common set of criteria, researchers can analyze the performance of their planning systems accurately.
The evaluation metrics consider factors such as the efficiency of planning, the ability to handle inter-agent dependencies, and the adaptability to unexpected disruptions. These metrics provide quantitative measures that can be used to assess and compare the performance of different planning systems.
Baseline Implementations
To facilitate the adoption and usage of the benchmark suite, baseline implementations using contemporary frameworks like LangGraph are provided. These implementations serve as a starting point for researchers and developers, allowing them to focus on improving and optimizing their algorithms rather than spending time on building the infrastructure from scratch.
The baseline implementations are designed to showcase the capabilities of the benchmark suite and demonstrate the potential of AI planning systems in real-world applications. They serve as a reference for developers to understand the expected performance and behavior of their systems.
Scalable Complexity
The benchmark suite’s problems are designed to be scalable along various dimensions, allowing researchers to test their planning systems under different levels of complexity. The number of parallel planning threads can be increased to evaluate the system’s performance under higher workload scenarios. Similarly, the complexity of inter-dependencies and the frequency of unexpected disruptions can be adjusted to assess adaptability and robustness.
This scalability offers a realistic simulation of real-world planning scenarios, where dynamic environments and interactions between agents constantly evolve. By benchmarking planning systems across a range of complexities, researchers can identify strengths and weaknesses in their algorithms and work towards improving them.
Driving Progress in AI Planning Systems
The new benchmark suite aims to drive progress in the development of more robust and adaptable AI planning systems for real-world applications. By providing standardized evaluation criteria, baseline implementations, and scalability, researchers and developers can improve their algorithms effectively.
Through rigorous testing and comparison, promising solutions can emerge, offering better planning capabilities for various industries. The benchmark suite encourages innovation, collaboration, and the exchange of ideas within the AI community, fostering the development of cutting-edge planning systems.
With the new benchmark suite, the possibilities for AI planning systems are expanding, opening doors to more advanced and efficient applications. As researchers continue to push the boundaries of AI, we can look forward to solutions that can address the complexities of real-world planning scenarios with unparalleled precision and adaptability.
The arXiv paper titled “Benchmark Suite for Real-World Planning Scenarios” introduces a comprehensive evaluation framework for assessing both individual LLMs (Logic Language Model) and multi-agent systems in real-world planning scenarios. This benchmark suite is designed to address the challenges faced by AI planning systems in real-world applications, such as multi-agent coordination, inter-agent dependencies, and dynamic environmental disruptions.
The suite consists of eleven carefully designed problems that range from basic to highly complex. These problems are meant to simulate real-world planning scenarios and provide a standardized evaluation platform for AI planning systems. One of the notable features of this benchmark suite is that it allows for scaling along three dimensions: the number of parallel planning threads, the complexity of inter-dependencies, and the frequency of unexpected disruptions requiring real-time adaptation.
To facilitate evaluation and comparison, the benchmark suite includes detailed specifications, evaluation metrics, and baseline implementations using contemporary frameworks like LangGraph. This enables researchers and developers to test and evaluate both single-agent and multi-agent planning capabilities using a common framework.
The ultimate goal of this benchmark suite is to drive progress in the development of more robust and adaptable AI planning systems for real-world applications. By providing standardized evaluation criteria and scalable complexity, researchers can assess the performance of their systems objectively and identify areas for improvement.
Looking ahead, this benchmark suite has the potential to significantly advance the field of AI planning by fostering competition and collaboration among researchers. As more researchers use this benchmark suite to evaluate their systems, it will likely lead to the development of more sophisticated planning algorithms and techniques. Additionally, the scalability of the benchmark suite allows for future expansion and inclusion of even more complex planning scenarios, further pushing the boundaries of AI planning capabilities.
Furthermore, this benchmark suite could also serve as a valuable tool for industry practitioners who are developing AI planning systems for real-world applications. By utilizing the evaluation framework and baseline implementations provided in the benchmark suite, practitioners can assess the performance of their systems against established standards and make informed decisions regarding system improvements.
In conclusion, the introduction of this benchmark suite for real-world planning scenarios is a significant contribution to the field of AI planning. It provides a comprehensive evaluation framework that addresses key challenges faced by planning systems in real-world applications. By driving progress in developing more robust and adaptable AI planning systems, this benchmark suite has the potential to greatly impact various industries and domains that rely on efficient planning and decision-making.
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by jsendak | Feb 27, 2025 | Computer Science
arXiv:2502.18495v1 Announce Type: new
Abstract: Composed Image Retrieval (CIR) is an emerging yet challenging task that allows users to search for target images using a multimodal query, comprising a reference image and a modification text specifying the user’s desired changes to the reference image. Given its significant academic and practical value, CIR has become a rapidly growing area of interest in the computer vision and machine learning communities, particularly with the advances in deep learning. To the best of our knowledge, there is currently no comprehensive review of CIR to provide a timely overview of this field. Therefore, we synthesize insights from over 120 publications in top conferences and journals, including ACM TOIS, SIGIR, and CVPR In particular, we systematically categorize existing supervised CIR and zero-shot CIR models using a fine-grained taxonomy. For a comprehensive review, we also briefly discuss approaches for tasks closely related to CIR, such as attribute-based CIR and dialog-based CIR. Additionally, we summarize benchmark datasets for evaluation and analyze existing supervised and zero-shot CIR methods by comparing experimental results across multiple datasets. Furthermore, we present promising future directions in this field, offering practical insights for researchers interested in further exploration.
Composed Image Retrieval: A Comprehensive Review
Introduction
Composed Image Retrieval (CIR) is a challenging task that allows users to search for target images using a multimodal query. This query consists of a reference image and a modification text that specifies the user’s desired changes to the reference image. CIR has gained significant academic and practical value, resulting in a rapidly growing interest in the fields of computer vision and machine learning.
Multi-disciplinary Nature of CIR
CIR is a multi-disciplinary field that requires expertise in various domains. It combines principles from computer vision, natural language processing, and information retrieval. The computer vision aspect involves understanding and analyzing the visual content of images, while natural language processing helps to interpret and analyze the modification text. Information retrieval techniques are utilized to match the query with relevant images in the database.
Advances in Deep Learning
The recent advances in deep learning have significantly impacted CIR research. Deep learning models, especially those based on convolutional neural networks (CNNs) and recurrent neural networks (RNNs), have shown remarkable performance in various computer vision tasks. These models have also been successfully applied to CIR, enabling more accurate image retrieval based on both visual and textual information.
Existing CIR Models
In this review, insights from over 120 publications in top conferences and journals are synthesized. The reviewed papers cover a range of CIR models. The authors systematically categorize existing models based on a fine-grained taxonomy, covering both supervised and zero-shot learning approaches. This categorization provides a comprehensive overview of the different methodologies employed in CIR.
Related Tasks
In addition to CIR, the review also briefly discusses related tasks such as attribute-based CIR and dialog-based CIR. Attribute-based CIR focuses on retrieving images based on specific attributes or characteristics specified in the modification text. Dialog-based CIR involves a conversational setting between the user and the system to search for images based on a series of queries and responses.
Evaluation and Analysis
The review summarizes benchmark datasets used for evaluating CIR models. It also compares the experimental results across multiple datasets for both supervised and zero-shot CIR methods. This analysis provides valuable insights into the strengths and weaknesses of different approaches and highlights areas for improvement in future research.
Promising Future Directions
The review concludes by discussing promising future directions for CIR research. It suggests potential areas of exploration such as incorporating user feedback to improve retrieval accuracy, exploring novel approaches for combining visual and textual modalities, and exploring the application of CIR in real-world scenarios such as e-commerce and content creation.
Conclusion
This comprehensive review of Composed Image Retrieval (CIR) provides a timely overview of this emerging field. It highlights the multi-disciplinary nature of CIR and its relation to computer vision, natural language processing, and information retrieval. The review categorizes and analyzes existing CIR models, discusses related tasks, and presents future research directions. The insights and findings presented in this review will be valuable to researchers interested in further exploration of CIR and its applications in multimedia information systems, animations, artificial reality, augmented reality, and virtual realities.
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by jsendak | Feb 27, 2025 | AI
arXiv:2502.18531v1 Announce Type: new
Abstract: Background: Recruitment for cohorts involving complex liver diseases, such as hepatocellular carcinoma and liver cirrhosis, often requires interpreting semantically complex criteria. Traditional manual screening methods are time-consuming and prone to errors. While AI-powered pre-screening offers potential solutions, challenges remain regarding accuracy, efficiency, and data privacy. Methods: We developed a novel patient pre-screening pipeline that leverages clinical expertise to guide the precise, safe, and efficient application of large language models. The pipeline breaks down complex criteria into a series of composite questions and then employs two strategies to perform semantic question-answering through electronic health records – (1) Pathway A, Anthropomorphized Experts’ Chain of Thought strategy, and (2) Pathway B, Preset Stances within an Agent Collaboration strategy, particularly in managing complex clinical reasoning scenarios. The pipeline is evaluated on three key metrics-precision, time consumption, and counterfactual inference – at both the question and criterion levels. Results: Our pipeline achieved high precision (0.921, in criteria level) and efficiency (0.44s per task). Pathway B excelled in complex reasoning, while Pathway A was effective in precise data extraction with faster processing times. Both pathways achieved comparable precision. The pipeline showed promising results in hepatocellular carcinoma (0.878) and cirrhosis trials (0.843). Conclusions: This data-secure and time-efficient pipeline shows high precision in hepatopathy trials, providing promising solutions for streamlining clinical trial workflows. Its efficiency and adaptability make it suitable for improving patient recruitment. And its capability to function in resource-constrained environments further enhances its utility in clinical settings.
Expert Commentary: Streamlining Clinical Trial Workflows with AI-Powered Patient Pre-Screening
In the field of clinical research, patient recruitment for complex liver diseases such as hepatocellular carcinoma and liver cirrhosis can be a challenging task. The traditional manual screening methods are not only time-consuming but also prone to human errors. However, the advent of AI-powered pre-screening offers potential solutions to these challenges.
This article introduces a novel patient pre-screening pipeline that leverages clinical expertise to guide the precise, safe, and efficient application of large language models. The pipeline breaks down complex criteria into a series of composite questions and then applies two strategies to perform semantic question-answering through electronic health records.
Multi-disciplinary Nature of the Concepts
This research effort combines expertise from multiple disciplines, including clinical medicine, artificial intelligence, and natural language processing. It demonstrates the integration of clinical knowledge and technological advancements to address the specific challenges associated with patient recruitment in complex liver disease trials.
The pipeline’s approach to breaking down complex criteria shows the influence of clinical expertise in designing effective questions that extract the relevant information from electronic health records. At the same time, the utilization of large language models powered by AI demonstrates the significance of cutting-edge technology in achieving precise and efficient results.
Pathway A: Anthropomorphized Experts’ Chain of Thought Strategy
This strategy employed in the pipeline focuses on mimicking the reasoning process of human experts. By breaking down complex clinical reasoning scenarios into a series of questions, it facilitates precise data extraction from electronic health records. Pathway A shows the potential to assist in automating the understanding and interpretation of complex medical information, reducing the burden on human experts and improving the efficiency of patient pre-screening.
Pathway B: Preset Stances within an Agent Collaboration Strategy
Pathway B, on the other hand, utilizes the collaboration between an agent and the clinical experts to tackle complex reasoning scenarios. This strategy acknowledges the limitations of fully automated approaches and emphasizes the importance of human input in handling intricate clinical situations. By combining the insights and expertise of both machine and human, Pathway B enhances the accuracy of semantic question-answering and provides a valuable approach for managing complex clinical reasoning.
Evaluation Metrics and Results
The pipeline’s evaluation metrics include precision, time consumption, and counterfactual inference at both the question and criterion levels. The results indicate high precision (0.921 at the criterion level) and efficiency (0.44 seconds per task) of the pipeline. This suggests that the pipeline is capable of accurately extracting relevant information from electronic health records and processing it in a timely manner.
Importantly, the pipeline’s promising results in the specific contexts of hepatocellular carcinoma and cirrhosis trials (achieving precision rates of 0.878 and 0.843, respectively) highlight its potential in advancing the recruitment process for these complex liver diseases. The ability of the pipeline to handle different diseases showcases its adaptability and generalizability, making it a suitable tool for improving patient recruitment in various clinical trial workflows.
Promising Solutions for Streamlining Clinical Trial Workflows
This data-secure and time-efficient patient pre-screening pipeline holds great promise for streamlining clinical trial workflows. By automating the screening process and reducing the manual effort required, the pipeline can expedite patient recruitment and enhance the efficiency of clinical trials. Its precision and adaptability further contribute to its utility in diverse clinical settings.
The multi-disciplinary nature of this research effort highlights the importance of collaboration between clinical experts and technology specialists. Moving forward, further research could focus on refining the pipeline’s accuracy, exploring its potential in other disease areas, and addressing any data privacy concerns. Overall, the integration of AI-powered patient pre-screening in clinical trials opens new avenues for improving healthcare outcomes and advancing medical research.
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by jsendak | Feb 27, 2025 | GR & QC Articles
arXiv:2502.18560v1 Announce Type: new
Abstract: The quantum nature of gravity remains an open question in fundamental physics, lacking experimental verification. Gravitational waves (GWs) provide a potential avenue for detecting gravitons, the hypothetical quantum carriers of gravity. However, by analogy with quantum optics, distinguishing gravitons from classical GWs requires the preservation of quantum coherence, which may be lost due to interactions with the cosmic environment causing decoherence. We investigate whether GWs retain their quantum state by deriving the reduced density matrix and evaluating decoherence, using an environmental model where a scalar field is conformally coupled to gravity. Our results show that quantum decoherence of GWs is stronger at lower frequencies and higher reheating temperatures. We identify a model-independent amplitude threshold below which decoherence is negligible, providing a fundamental limit for directly probing the quantum nature of gravity. In the standard cosmological scenario, the low energy density of the universe at the end of inflation leads to complete decoherence at the classical amplitude level of inflationary GWs. However, for higher energy densities, decoherence is negligible within a frequency window in the range $100 {rm Hz} text{-} 10^8 {rm Hz}$, which depends on the reheating temperature. In a kinetic-dominated scenario, the dependence on reheating temperature weakens, allowing GWs to maintain quantum coherence above $10^7 {rm Hz}$.
The Quantum Nature of Gravity and the Detectability of Gravitons
Gravity, one of the fundamental forces of nature, is still not fully understood in the framework of quantum physics. While we have theories like general relativity to describe gravity, there is no experimental evidence for the existence of gravitons, the hypothetical quantum particles carrying gravity. In this study, we explore the potential of gravitational waves (GWs) to provide a way to detect gravitons. However, distinguishing gravitons from classical GWs is a challenging task due to the loss of quantum coherence caused by interactions with the cosmic environment.
Investigating Quantum Decoherence in Gravitational Waves
To evaluate the preservation of quantum coherence in GWs, we analyze the decoherence effect using an environmental model where a scalar field is conformally coupled to gravity. By deriving the reduced density matrix, we are able to quantify the level of decoherence in GWs. Our findings reveal that the degree of quantum decoherence in GWs is dependent on both the frequency of the waves and the reheating temperature of the universe.
Frequency and Reheating Temperature Dependencies
We establish a key observation that quantum decoherence of GWs is stronger at lower frequencies and higher reheating temperatures. At these conditions, the interaction with the cosmic environment causes stronger decoherence effects and makes it more difficult to maintain the quantum state of the GWs.
Fundamental Limit for Probing the Quantum Nature of Gravity
Our research leads to an important conclusion: there exists an amplitude threshold below which the decoherence of GWs becomes negligible. This threshold serves as a fundamental limit for directly investigating the quantum nature of gravity.
Implications in Cosmological Scenarios
In the standard cosmological scenario, where the energy density of the universe is low at the end of inflation, the decoherence effects in GWs reach a complete level at the classical amplitude of inflationary GWs. However, for higher energy densities, the decoherence becomes negligible within a specific frequency range of 0 {rm Hz} text{-} 10^8 {rm Hz}$, which is dependent on the reheating temperature.
In the context of a kinetic-dominated scenario, the dependence on the reheating temperature weakens, allowing GWs to maintain quantum coherence even at frequencies above ^7 {rm Hz}$. This opens up opportunities to study the quantum nature of gravity in different cosmological scenarios.
Future Roadmap: Challenges and Opportunities
Challenges
- The preservation of quantum coherence in GWs is a challenging task due to the strong interactions with the cosmic environment.
- Differentiating gravitons from classical GWs requires addressing the issue of decoherence.
- Understanding the impact of lower frequencies and higher reheating temperatures on quantum decoherence in GWs.
Opportunities
- The identification of an amplitude threshold for negligible decoherence provides a fundamental limit for directly probing the quantum nature of gravity.
- Exploring the specific frequency range and reheating temperature dependencies allows for the investigation of quantum coherence in different cosmological scenarios.
- Advancing our understanding of the quantum nature of gravity through experimental verification of gravitons using GW detection methods.
Note: This study contributes to the ongoing quest to unify quantum mechanics and gravity, shedding light on the quantum nature of gravity and its potential experimental detection through GWs.
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by jsendak | Feb 27, 2025 | Computer Science
In this article, a novel framework for automated code generation and debugging is presented. The framework aims to improve accuracy, efficiency, and scalability in software development. The system consists of three core components: LangGraph, GLM4 Flash, and ChromaDB, which are integrated within a four-step iterative workflow.
LangGraph: Orchestrating Tasks
LangGraph serves as a graph-based library for orchestrating tasks in the code generation and debugging process. It provides precise control and execution while maintaining a unified state object for dynamic updates and consistency. This makes it highly adaptable to complex software engineering workflows, supporting multi-agent, hierarchical, and sequential processes. By having a flexible and adaptable task orchestration module, developers can effectively manage and streamline their software development process.
GLM4 Flash: Advanced Code Generation
GLM4 Flash is a large language model that leverages its advanced capabilities in natural language understanding, contextual reasoning, and multilingual support to generate accurate code snippets based on user prompts. By utilizing sophisticated language processing techniques, GLM4 Flash can generate code that is contextually relevant and accurate. This can greatly speed up the code generation process and reduce errors caused by manual coding efforts.
ChromaDB: Semantic Search and Contextual Memory Storage
ChromaDB acts as a vector database for semantic search and contextual memory storage. It enables the identification of patterns and the generation of context-aware bug fixes based on historical data. By leveraging the semantic search and memory capabilities of ChromaDB, the system can provide intelligent suggestions for bug fixes and improvements based on past code analysis and debugging experiences. This can assist developers in quickly identifying and resolving common coding issues.
Four-Step Iterative Workflow
The system operates through a structured four-step process to generate and debug code:
- Code Generation: Natural language descriptions are translated into executable code using GLM4 Flash. This step provides a bridge between human-readable descriptions and machine-executable code.
- Code Execution: The generated code is validated by identifying runtime errors and inconsistencies. This step ensures that the generated code functions correctly.
- Code Repair: Buggy code is iteratively refined using ChromaDB’s memory capabilities and LangGraph’s state tracking. The system utilizes historical data and semantic search to identify patterns and generate context-aware bug fixes.
- Code Update: The code is iteratively modified to meet functional and performance requirements. This step ensures that the generated code is optimized and meets the desired specifications.
This four-step iterative workflow allows the system to continuously generate, execute, refine, and update code, improving the overall software development process. By automating code generation and debugging tasks, developers can save time and effort, resulting in faster and more efficient software development cycles.
In conclusion, the proposed framework for automated code generation and debugging shows promise in improving accuracy, efficiency, and scalability in software development. Utilizing the capabilities of LangGraph, GLM4 Flash, and ChromaDB, the system provides a comprehensive solution for code generation and debugging. By integrating these core components within a structured four-step iterative workflow, the system aims to deliver robust performance and seamless functionality. This framework has the potential to greatly assist developers in their software development efforts, reducing time spent on coding and debugging, and improving the overall quality of software products.
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