AI Summary

• The paper discusses the importance of Theory of Change (ToC) in AI development and its application to various domains. [3]
• The authors emphasize the importance of considering the long-term consequences of AI development and deployment, using ToC as a framework for evaluating potential outcomes. [3]
• It highlights the importance of considering the long-term consequences of AI development and deployment, using ToC as a framework for evaluating potential outcomes. [3]
• It highlights the need for a more structured approach to AI development, incorporating ToC principles to ensure that AI systems are aligned with human values and goals. [2]
• Theory of Change (ToC): A structured approach to understanding how an intervention or program will lead to desired outcomes. [1]

Abstract
This paper introduces the Impact-Driven AI Framework (IDAIF), a novel architectural methodology that integrates Theory of Change (ToC) principles with modern artificial intelligence system design. As AI systems increasingly influence high-stakes domains including healthcare, finance, and public policy, the alignment problem--ensuring AI behavior corresponds with human values and intentions--has become critical. Current approaches predominantly optimize technical performance metrics while neglecting the sociotechnical dimensions of AI deployment. IDAIF addresses this gap by establishing a systematic mapping between ToC's five-stage model (Inputs-Activities-Outputs-Outcomes-Impact) and corresponding AI architectural layers (Data Layer-Pipeline Layer-Inference Layer-Agentic Layer-Normative Layer). Each layer incorporates rigorous theoretical foundations: multi-objective Pareto optimization for value alignment, hierarchical multi-agent orchestration for outcome achievement, causal directed acyclic graphs (DAGs) for hallucination mitigation, and adversarial debiasing with Reinforcement Learning from Human Feedback (RLHF) for fairness assurance. We provide formal mathematical formulations for each component and introduce an Assurance Layer that manages assumption failures through guardian architectures. Three case studies demonstrate IDAIF application across healthcare, cybersecurity, and software engineering domains. This framework represents a paradigm shift from model-centric to impact-centric AI development, providing engineers with concrete architectural patterns for building ethical, trustworthy, and socially beneficial AI systems.

Why we think this paper is great for you:
This framework offers a structured approach to aligning AI system design with broader goals, which aligns with managing complex engineering projects and teams. It provides a methodology for ensuring AI initiatives deliver tangible impact, a key concern for effective management.

Abstract
Enterprise data management is a monumental task. It spans data architecture and systems, integration, quality, governance, and continuous improvement. While AI assistants can help specific persona, such as data engineers and stewards, to navigate and configure the data stack, they fall far short of full automation. However, as AI becomes increasingly capable of tackling tasks that have previously resisted automation due to inherent complexities, we believe there is an imminent opportunity to target fully autonomous data estates. Currently, AI is used in different parts of the data stack, but in this paper, we argue for a paradigm shift from the use of AI in independent data component operations towards a more holistic and autonomous handling of the entire data lifecycle. Towards that end, we explore how each stage of the modern data stack can be autonomously managed by intelligent agents to build self-sufficient systems that can be used not only by human end-users, but also by AI itself. We begin by describing the mounting forces and opportunities that demand this paradigm shift, examine how agents can streamline the data lifecycle, and highlight open questions and areas where additional research is needed. We hope this work will inspire lively debate, stimulate further research, motivate collaborative approaches, and facilitate a more autonomous future for data systems.

Why we think this paper is great for you:
The paper explores the potential for AI to manage the entire data stack, addressing the challenges of enterprise data management. Understanding how AI can automate and optimize data infrastructure is crucial for data science engineering teams.

AI Summary

• The paper discusses the importance of Theory of Change (ToC) in AI development and its application to various domains. [3]
• The authors emphasize the importance of considering the long-term consequences of AI development and deployment, using ToC as a framework for evaluating potential outcomes. [3]
• It highlights the importance of considering the long-term consequences of AI development and deployment, using ToC as a framework for evaluating potential outcomes. [3]
• It highlights the need for a more structured approach to AI development, incorporating ToC principles to ensure that AI systems are aligned with human values and goals. [2]
• Theory of Change (ToC): A structured approach to understanding how an intervention or program will lead to desired outcomes. [1]

Abstract
This paper introduces the Impact-Driven AI Framework (IDAIF), a novel architectural methodology that integrates Theory of Change (ToC) principles with modern artificial intelligence system design. As AI systems increasingly influence high-stakes domains including healthcare, finance, and public policy, the alignment problem--ensuring AI behavior corresponds with human values and intentions--has become critical. Current approaches predominantly optimize technical performance metrics while neglecting the sociotechnical dimensions of AI deployment. IDAIF addresses this gap by establishing a systematic mapping between ToC's five-stage model (Inputs-Activities-Outputs-Outcomes-Impact) and corresponding AI architectural layers (Data Layer-Pipeline Layer-Inference Layer-Agentic Layer-Normative Layer). Each layer incorporates rigorous theoretical foundations: multi-objective Pareto optimization for value alignment, hierarchical multi-agent orchestration for outcome achievement, causal directed acyclic graphs (DAGs) for hallucination mitigation, and adversarial debiasing with Reinforcement Learning from Human Feedback (RLHF) for fairness assurance. We provide formal mathematical formulations for each component and introduce an Assurance Layer that manages assumption failures through guardian architectures. Three case studies demonstrate IDAIF application across healthcare, cybersecurity, and software engineering domains. This framework represents a paradigm shift from model-centric to impact-centric AI development, providing engineers with concrete architectural patterns for building ethical, trustworthy, and socially beneficial AI systems.

Why we think this paper is great for you:
The framework’s focus on aligning AI with broader goals and measurable impact directly addresses the need for strategic management of data science initiatives. This is particularly relevant when considering the operational aspects of managing teams.

AI Summary

• Baseline is an operation-based system for evolving and versioning data, allowing for bidirectional transfer of changes between different versions of a database. [3]
• Operation-based evolution: A method of evolving data by applying a sequence of operations to the original state, allowing for bidirectional transfer of changes. [3]
• The system uses a timeline of operations to represent queries, enabling query rewriting and transfer through schema changes. [2]
• Timeline of operations: A representation of a query as a sequence of operations that can be executed on the database, enabling query rewriting and transfer through schema changes. [1]

Abstract
Baseline is a platform for richly structured data supporting change in multiple dimensions: mutation over time, collaboration across space, and evolution through design changes. It is built upon Operational Differencing, a new technique for managing data in terms of high-level operations that include refactorings and schema changes. We use operational differencing to construct an operation-based form of version control on data structures used in programming languages and relational databases. This approach to data version control does fine-grained diffing and merging despite intervening structural transformations like schema changes. It offers users a simplified conceptual model of version control for ad hoc usage: There is no repo; Branching is just copying. The informaton maintained in a repo can be synthesized more precisely from the append-only histories of branches. Branches can be flexibly shared as is commonly done with document files, except with the added benefit of diffing and merging. We conjecture that queries can be operationalized into a sequence of schema and data operations. We develop that idea on a query language fragment containing selects and joins. Operationalized queries are represented as a future timeline that is speculatively executed as a branch off of the present state, returning a value from its hypothetical future. Operationalized queries get rewritten to accommodate schema change "for free" by the machinery of operational differencing. Altogether we develop solutions to four of the eight challenge problems of schema evolution identified in a recent paper.

Why we think this paper is great for you:
The paper’s emphasis on managing data through operations and evolution aligns with the need for robust and adaptable data management strategies. Understanding how to version and evolve data is essential for data science teams.

AI Summary

• The system aims to help players analyze and understand their gameplay data. [3]
• They developed a prototype that incorporates various visualization techniques, including network analysis, time-series plots, and scatterplots. [3]
• Visual analytics: The use of interactive visualizations to support analytical reasoning and decision-making. [3]
• Co-creation: A process where users collaborate with designers to create a product or service that meets their needs. [3]
• The system's effectiveness is attributed to its ability to provide actionable insights through interactive visualizations. [3]
• The paper discusses the design of a visual analytics system for Magic: The Gathering, a popular trading card game. [2]

Abstract
This paper presents the initial stages of a design study aimed at developing a dashboard to visualize gameplay data of the Commander format from Magic: The Gathering. We conducted a user-task analysis to identify requirements for a data visualization dashboard tailored to the Commander format. Afterwards, we proposed a design for the dashboard leveraging visualizations to address players' needs and pain points for typical data analysis tasks in the context domain. Then, we followed-up with a structured user test to evaluate players' comprehension and preferences of data visualizations. Results show that players prioritize contextually relevant, outcome-driven metrics over peripheral ones, and that canonical charts like heatmaps and line charts support higher comprehension than complex ones such as scatterplots or icicle plots. Our findings also highlight the importance of localized views, user customization, and progressive disclosure, emphasizing that adaptability and contextual relevance are as essential as accuracy in effective dashboard design. Our study contributes practical design guidelines for data visualization in gaming contexts and highlights broader implications for engagement-driven dashboards.

Why we think this paper is great for you:
This study explores the design of a data visualization dashboard, directly relating to the creation of tools for understanding and analyzing complex datasets. This is a key skill for data scientists.

AI Summary

• The Alexandridatabase has been expanded to include 5.8 million DFT-calculated structures with 175 thousand thermodynamically stable compounds on the convex hull. [3]
• The database exhibits predicted rates of structural disorder consistent with experimental inorganic crystal structure databases. [3]
• A multi-stage discovery workflow combining generative models, universal machine learning interatomic potentials, and specialized graph neural networks has achieved an unprecedented 99% success rate in identifying compounds within 100 meV/atom of thermodynamic stability. [3]
• The updated subsampled Alexandriadataset (sAlex25) provides 14 million out-of-equilibrium structures with forces and stresses along relaxation trajectories, specifically curated for training universal machine learning interatomic potentials. [3]
• The dataset has been used to fine-tune a GRACE model, achieving state-of-the-art performance on the WBM benchmark. [3]
• DFT: Density Functional Theory FAENet: A neural network architecture for encoding and predicting atomic environments ALIGNN: A graph neural network for predicting energies from approximate geometries uMLIPs: Universal Machine Learning Interatomic Potentials GRACE: Graph Atomic Cluster Expansion [3]

Abstract
We present a novel multi-stage workflow for computational materials discovery that achieves a 99% success rate in identifying compounds within 100 meV/atom of thermodynamic stability, with a threefold improvement over previous approaches. By combining the Matra-Genoa generative model, Orb-v2 universal machine learning interatomic potential, and ALIGNN graph neural network for energy prediction, we generated 119 million candidate structures and added 1.3 million DFT-validated compounds to the ALEXANDRIA database, including 74 thousand new stable materials. The expanded ALEXANDRIA database now contains 5.8 million structures with 175 thousand compounds on the convex hull. Predicted structural disorder rates (37-43%) match experimental databases, unlike other recent AI-generated datasets. Analysis reveals fundamental patterns in space group distributions, coordination environments, and phase stability networks, including sub-linear scaling of convex hull connectivity. We release the complete dataset, including sAlex25 with 14 million out-of-equilibrium structures containing forces and stresses for training universal force fields. We demonstrate that fine-tuning a GRACE model on this data improves benchmark accuracy. All data, models, and workflows are freely available under Creative Commons licenses.

Why we think this paper is great for you:
The paper's focus on computational materials discovery and a multi-stage workflow aligns with the need for efficient data management and analysis within a scientific context. This is relevant for data science engineering teams.

AI Summary

• Some sections of the paper seem disconnected from the main topic. [3]
• FAIR data management and stewardship Scientific workflow management Machine-readable expressions of research findings The paper could benefit from a clearer explanation of the technical aspects of machine-readable expressions of research findings. [2]
• Imagine you're working on a scientific project and you want to share your results with others. [1]

Abstract
For scientific knowledge to be findable, accessible, interoperable, and reusable, it needs to be machine-readable. Moving forward from post-publication extraction of knowledge, we adopted a pre-publication approach to write research findings in a machine-readable format at early stages of data analysis. For this purpose, we developed the package dtreg in Python and R. Registered and persistently identified data types, aka schemata, which dtreg applies to describe data analysis in a machine-readable format, cover the most widely used statistical tests and machine learning methods. The package supports (i) downloading a relevant schema as a mutable instance of a Python or R class, (ii) populating the instance object with metadata about data analysis, and (iii) converting the object into a lightweight Linked Data format. This paper outlines the background of our approach, explains the code architecture, and illustrates the functionality of dtreg with a machine-readable description of a t-test on Iris Data. We suggest that the dtreg package can enhance the methodological repertoire of researchers aiming to adhere to the FAIR principles.

Why we think this paper is great for you:
The paper's exploration of machine-readable data formats is crucial for knowledge sharing and interoperability, a key consideration for data science engineering and management.