A Framework for Objective-Driven Dynamical Stochastic Fieldscs.AI updates on arXiv.org arXiv:2504.16115v2 Announce Type: replace
Abstract: Fields offer a versatile approach for describing complex systems composed of interacting and dynamic components. In particular, some of these dynamical and stochastic systems may exhibit goal-directed behaviors aimed at achieving specific objectives, which we refer to as $textit{intelligent fields}$. However, due to their inherent complexity, it remains challenging to develop a formal theoretical description of such systems and to effectively translate these descriptions into practical applications. In this paper, we propose three fundamental principles to establish a theoretical framework for understanding intelligent fields: complete configuration, locality, and purposefulness. Moreover, we explore methodologies for designing such fields from the perspective of artificial intelligence applications. This initial investigation aims to lay the groundwork for future theoretical developments and practical advances in understanding and harnessing the potential of such objective-driven dynamical stochastic fields.
arXiv:2504.16115v2 Announce Type: replace
Abstract: Fields offer a versatile approach for describing complex systems composed of interacting and dynamic components. In particular, some of these dynamical and stochastic systems may exhibit goal-directed behaviors aimed at achieving specific objectives, which we refer to as $textit{intelligent fields}$. However, due to their inherent complexity, it remains challenging to develop a formal theoretical description of such systems and to effectively translate these descriptions into practical applications. In this paper, we propose three fundamental principles to establish a theoretical framework for understanding intelligent fields: complete configuration, locality, and purposefulness. Moreover, we explore methodologies for designing such fields from the perspective of artificial intelligence applications. This initial investigation aims to lay the groundwork for future theoretical developments and practical advances in understanding and harnessing the potential of such objective-driven dynamical stochastic fields. Read More
Aeolus: A Multi-structural Flight Delay Datasetcs.AI updates on arXiv.org arXiv:2510.26616v2 Announce Type: replace-cross
Abstract: We introduce Aeolus, a large-scale Multi-modal Flight Delay Dataset designed to advance research on flight delay prediction and support the development of foundation models for tabular data. Existing datasets in this domain are typically limited to flat tabular structures and fail to capture the spatiotemporal dynamics inherent in delay propagation. Aeolus addresses this limitation by providing three aligned modalities: (i) a tabular dataset with rich operational, meteorological, and airportlevel features for over 50 million flights; (ii) a flight chain module that models delay propagation along sequential flight legs, capturing upstream and downstream dependencies; and (iii) a flight network graph that encodes shared aircraft, crew, and airport resource connections, enabling cross-flight relational reasoning. The dataset is carefully constructed with temporal splits, comprehensive features, and strict leakage prevention to support realistic and reproducible machine learning evaluation. Aeolus supports a broad range of tasks, including regression, classification, temporal structure modeling, and graph learning, serving as a unified benchmark across tabular, sequential, and graph modalities. We release baseline experiments and preprocessing tools to facilitate adoption. Aeolus fills a key gap for both domain-specific modeling and general-purpose structured data research.Our source code and data can be accessed at https://github.com/Flnny/Delay-data
arXiv:2510.26616v2 Announce Type: replace-cross
Abstract: We introduce Aeolus, a large-scale Multi-modal Flight Delay Dataset designed to advance research on flight delay prediction and support the development of foundation models for tabular data. Existing datasets in this domain are typically limited to flat tabular structures and fail to capture the spatiotemporal dynamics inherent in delay propagation. Aeolus addresses this limitation by providing three aligned modalities: (i) a tabular dataset with rich operational, meteorological, and airportlevel features for over 50 million flights; (ii) a flight chain module that models delay propagation along sequential flight legs, capturing upstream and downstream dependencies; and (iii) a flight network graph that encodes shared aircraft, crew, and airport resource connections, enabling cross-flight relational reasoning. The dataset is carefully constructed with temporal splits, comprehensive features, and strict leakage prevention to support realistic and reproducible machine learning evaluation. Aeolus supports a broad range of tasks, including regression, classification, temporal structure modeling, and graph learning, serving as a unified benchmark across tabular, sequential, and graph modalities. We release baseline experiments and preprocessing tools to facilitate adoption. Aeolus fills a key gap for both domain-specific modeling and general-purpose structured data research.Our source code and data can be accessed at https://github.com/Flnny/Delay-data Read More
5 Fun Data Science Projects for Absolute BeginnersKDnuggets These beginner-friendly projects guide you through the full data science workflow so you can learn by building and experimenting.
These beginner-friendly projects guide you through the full data science workflow so you can learn by building and experimenting. Read More
From ambition to accountability: Quantifying AI ROI in strategyAI News For many UK executives, AI investment has become a necessity, not an experiment in innovation. Boards now demand evidence of measurable impact – whether through efficiency gains, revenue growth, or reduced operational risk. Yet, as Pete Smyth, CEO of Leading Resolutions notes, many SMEs treat AI as an exploratory exercise, not a structured business strategy.
The post From ambition to accountability: Quantifying AI ROI in strategy appeared first on AI News.
For many UK executives, AI investment has become a necessity, not an experiment in innovation. Boards now demand evidence of measurable impact – whether through efficiency gains, revenue growth, or reduced operational risk. Yet, as Pete Smyth, CEO of Leading Resolutions notes, many SMEs treat AI as an exploratory exercise, not a structured business strategy.
The post From ambition to accountability: Quantifying AI ROI in strategy appeared first on AI News. Read More
DevOps for AI: Continuous deployment pipelines for machine learning systemsAI News AI’s effects on continuous development and deployment pipelines are becoming difficult to ignore. However, decision-makers in software development functions need to consider a broad range of elements when considering the uses of the technology. The challenges of deploying AI at scale Deploying artificial intelligence isn’t the same as deploying, for example, a web app. Traditional
The post DevOps for AI: Continuous deployment pipelines for machine learning systems appeared first on AI News.
AI’s effects on continuous development and deployment pipelines are becoming difficult to ignore. However, decision-makers in software development functions need to consider a broad range of elements when considering the uses of the technology. The challenges of deploying AI at scale Deploying artificial intelligence isn’t the same as deploying, for example, a web app. Traditional
The post DevOps for AI: Continuous deployment pipelines for machine learning systems appeared first on AI News. Read More
NVIDIA and South Korea align on sovereign AI at APEC CEO SummitAI News At the APEC CEO Summit, NVIDIA said it is working with public agencies and private companies to build sovereign AI infrastructure across South Korea. The plan includes hundreds of thousands of NVIDIA GPUs across sovereign clouds and AI factories for areas like automotive, manufacturing and telecommunications. “Korea’s leadership in technology and manufacturing positions it at
The post NVIDIA and South Korea align on sovereign AI at APEC CEO Summit appeared first on AI News.
At the APEC CEO Summit, NVIDIA said it is working with public agencies and private companies to build sovereign AI infrastructure across South Korea. The plan includes hundreds of thousands of NVIDIA GPUs across sovereign clouds and AI factories for areas like automotive, manufacturing and telecommunications. “Korea’s leadership in technology and manufacturing positions it at
The post NVIDIA and South Korea align on sovereign AI at APEC CEO Summit appeared first on AI News. Read More
Qualcomm unveils AI data centre chips to crack the Inference marketAI News The AI chip wars just got a new heavyweight contender. Qualcomm, the company that powers billions of smartphones worldwide, has made an audacious leap into AI data centre chips – a market where Nvidia has been minting money at an almost unfathomable rate and where fortunes rise and fall on promises of computational supremacy. On
The post Qualcomm unveils AI data centre chips to crack the Inference market appeared first on AI News.
The AI chip wars just got a new heavyweight contender. Qualcomm, the company that powers billions of smartphones worldwide, has made an audacious leap into AI data centre chips – a market where Nvidia has been minting money at an almost unfathomable rate and where fortunes rise and fall on promises of computational supremacy. On
The post Qualcomm unveils AI data centre chips to crack the Inference market appeared first on AI News. Read More
How to Design a Persistent Memory and Personalized Agentic AI System with Decay and Self-Evaluation?MarkTechPost In this tutorial, we explore how to build an intelligent agent that remembers, learns, and adapts to us over time. We implement a Persistent Memory & Personalisation system using simple, rule-based logic to simulate how modern Agentic AI frameworks store and recall contextual information. As we progress, we see how the agent’s responses evolve with
The post How to Design a Persistent Memory and Personalized Agentic AI System with Decay and Self-Evaluation? appeared first on MarkTechPost.
In this tutorial, we explore how to build an intelligent agent that remembers, learns, and adapts to us over time. We implement a Persistent Memory & Personalisation system using simple, rule-based logic to simulate how modern Agentic AI frameworks store and recall contextual information. As we progress, we see how the agent’s responses evolve with
The post How to Design a Persistent Memory and Personalized Agentic AI System with Decay and Self-Evaluation? appeared first on MarkTechPost. Read More
LLMs are Overconfident: Evaluating Confidence Interval Calibration with FermiEvalcs.AI updates on arXiv.org arXiv:2510.26995v1 Announce Type: cross
Abstract: Large language models (LLMs) excel at numerical estimation but struggle to correctly quantify uncertainty. We study how well LLMs construct confidence intervals around their own answers and find that they are systematically overconfident. To evaluate this behavior, we introduce FermiEval, a benchmark of Fermi-style estimation questions with a rigorous scoring rule for confidence interval coverage and sharpness. Across several modern models, nominal 99% intervals cover the true answer only 65% of the time on average. With a conformal prediction based approach that adjusts the intervals, we obtain accurate 99% observed coverage, and the Winkler interval score decreases by 54%. We also propose direct log-probability elicitation and quantile adjustment methods, which further reduce overconfidence at high confidence levels. Finally, we develop a perception-tunnel theory explaining why LLMs exhibit overconfidence: when reasoning under uncertainty, they act as if sampling from a truncated region of their inferred distribution, neglecting its tails.
arXiv:2510.26995v1 Announce Type: cross
Abstract: Large language models (LLMs) excel at numerical estimation but struggle to correctly quantify uncertainty. We study how well LLMs construct confidence intervals around their own answers and find that they are systematically overconfident. To evaluate this behavior, we introduce FermiEval, a benchmark of Fermi-style estimation questions with a rigorous scoring rule for confidence interval coverage and sharpness. Across several modern models, nominal 99% intervals cover the true answer only 65% of the time on average. With a conformal prediction based approach that adjusts the intervals, we obtain accurate 99% observed coverage, and the Winkler interval score decreases by 54%. We also propose direct log-probability elicitation and quantile adjustment methods, which further reduce overconfidence at high confidence levels. Finally, we develop a perception-tunnel theory explaining why LLMs exhibit overconfidence: when reasoning under uncertainty, they act as if sampling from a truncated region of their inferred distribution, neglecting its tails. Read More
Generative Semantic Coding for Ultra-Low Bitrate Visual Communication and Analysiscs.AI updates on arXiv.org arXiv:2510.27324v1 Announce Type: cross
Abstract: We consider the problem of ultra-low bit rate visual communication for remote vision analysis, human interactions and control in challenging scenarios with very low communication bandwidth, such as deep space exploration, battlefield intelligence, and robot navigation in complex environments. In this paper, we ask the following important question: can we accurately reconstruct the visual scene using only a very small portion of the bit rate in existing coding methods while not sacrificing the accuracy of vision analysis and performance of human interactions? Existing text-to-image generation models offer a new approach for ultra-low bitrate image description. However, they can only achieve a semantic-level approximation of the visual scene, which is far insufficient for the purpose of visual communication and remote vision analysis and human interactions. To address this important issue, we propose to seamlessly integrate image generation with deep image compression, using joint text and coding latent to guide the rectified flow models for precise generation of the visual scene. The semantic text description and coding latent are both encoded and transmitted to the decoder at a very small bit rate. Experimental results demonstrate that our method can achieve the same image reconstruction quality and vision analysis accuracy as existing methods while using much less bandwidth. The code will be released upon paper acceptance.
arXiv:2510.27324v1 Announce Type: cross
Abstract: We consider the problem of ultra-low bit rate visual communication for remote vision analysis, human interactions and control in challenging scenarios with very low communication bandwidth, such as deep space exploration, battlefield intelligence, and robot navigation in complex environments. In this paper, we ask the following important question: can we accurately reconstruct the visual scene using only a very small portion of the bit rate in existing coding methods while not sacrificing the accuracy of vision analysis and performance of human interactions? Existing text-to-image generation models offer a new approach for ultra-low bitrate image description. However, they can only achieve a semantic-level approximation of the visual scene, which is far insufficient for the purpose of visual communication and remote vision analysis and human interactions. To address this important issue, we propose to seamlessly integrate image generation with deep image compression, using joint text and coding latent to guide the rectified flow models for precise generation of the visual scene. The semantic text description and coding latent are both encoded and transmitted to the decoder at a very small bit rate. Experimental results demonstrate that our method can achieve the same image reconstruction quality and vision analysis accuracy as existing methods while using much less bandwidth. The code will be released upon paper acceptance. Read More
