Building an AI Agent to Detect and Handle Anomalies in Time-Series DataTowards Data Science Combining statistical detection with agentic decision-making
The post Building an AI Agent to Detect and Handle Anomalies in Time-Series Data appeared first on Towards Data Science.
Combining statistical detection with agentic decision-making
The post Building an AI Agent to Detect and Handle Anomalies in Time-Series Data appeared first on Towards Data Science. Read More
Not All RecSys Problems Are Created EqualTowards Data Science How baseline strength, churn, and subjectivity determine complexity
The post Not All RecSys Problems Are Created Equal appeared first on Towards Data Science.
How baseline strength, churn, and subjectivity determine complexity
The post Not All RecSys Problems Are Created Equal appeared first on Towards Data Science. Read More
Versioning and Testing Data Solutions: Applying CI and Unit Tests on Interview-style QueriesKDnuggets Learn how to apply unit testing, version control, and continuous integration to data analysis scripts using Python and GitHub Actions.
Learn how to apply unit testing, version control, and continuous integration to data analysis scripts using Python and GitHub Actions. Read More
Harness engineering: leveraging Codex in an agent-first worldOpenAI News By Ryan Lopopolo, Member of the Technical Staff
By Ryan Lopopolo, Member of the Technical Staff Read More
Barclays bets on AI to cut costs and boost returnsAI News Barclays recorded a 12 % jump in annual profit for 2025, reporting £9.1 billion in earnings before tax, up from £8.1 billion a year earlier. The bank also raised its performance targets out through 2028, aiming for a return on tangible equity (RoTE) of more than 14 %, up from a previous goal of above
The post Barclays bets on AI to cut costs and boost returns appeared first on AI News.
Barclays recorded a 12 % jump in annual profit for 2025, reporting £9.1 billion in earnings before tax, up from £8.1 billion a year earlier. The bank also raised its performance targets out through 2028, aiming for a return on tangible equity (RoTE) of more than 14 %, up from a previous goal of above
The post Barclays bets on AI to cut costs and boost returns appeared first on AI News. Read More
How insurance leaders use agentic AI to cut operational costsAI News Agentic AI offers insurance leaders a path to scalable efficiency as the sector confronts a tough digital transformation. Insurers hold deep data reserves and employ a workforce skilled in analytic decision-making. Despite these advantages, the industry has largely failed to advance beyond pilot programmes. Research suggests only seven percent of insurers have scaled these initiatives
The post How insurance leaders use agentic AI to cut operational costs appeared first on AI News.
Agentic AI offers insurance leaders a path to scalable efficiency as the sector confronts a tough digital transformation. Insurers hold deep data reserves and employ a workforce skilled in analytic decision-making. Despite these advantages, the industry has largely failed to advance beyond pilot programmes. Research suggests only seven percent of insurers have scaled these initiatives
The post How insurance leaders use agentic AI to cut operational costs appeared first on AI News. Read More
Debugging code world modelscs.AI updates on arXiv.org arXiv:2602.07672v1 Announce Type: cross
Abstract: Code World Models (CWMs) are language models trained to simulate program execution by predicting explicit runtime state after every executed command. This execution-based world modeling enables internal verification within the model, offering an alternative to natural language chain-of-thought reasoning. However, the sources of errors and the nature of CWMs’ limitations remain poorly understood. We study CWMs from two complementary perspectives: local semantic execution and long-horizon state tracking. On real-code benchmarks, we identify two dominant failure regimes. First, dense runtime state reveals produce token-intensive execution traces, leading to token-budget exhaustion on programs with long execution histories. Second, failures disproportionately concentrate in string-valued state, which we attribute to limitations of subword tokenization rather than program structure. To study long-horizon behavior, we use a controlled permutation-tracking benchmark that isolates state propagation under action execution. We show that long-horizon degradation is driven primarily by incorrect action generation: when actions are replaced with ground-truth commands, a Transformer-based CWM propagates state accurately over long horizons, despite known limitations of Transformers in long-horizon state tracking. These findings suggest directions for more efficient supervision and state representations in CWMs that are better aligned with program execution and data types.
arXiv:2602.07672v1 Announce Type: cross
Abstract: Code World Models (CWMs) are language models trained to simulate program execution by predicting explicit runtime state after every executed command. This execution-based world modeling enables internal verification within the model, offering an alternative to natural language chain-of-thought reasoning. However, the sources of errors and the nature of CWMs’ limitations remain poorly understood. We study CWMs from two complementary perspectives: local semantic execution and long-horizon state tracking. On real-code benchmarks, we identify two dominant failure regimes. First, dense runtime state reveals produce token-intensive execution traces, leading to token-budget exhaustion on programs with long execution histories. Second, failures disproportionately concentrate in string-valued state, which we attribute to limitations of subword tokenization rather than program structure. To study long-horizon behavior, we use a controlled permutation-tracking benchmark that isolates state propagation under action execution. We show that long-horizon degradation is driven primarily by incorrect action generation: when actions are replaced with ground-truth commands, a Transformer-based CWM propagates state accurately over long horizons, despite known limitations of Transformers in long-horizon state tracking. These findings suggest directions for more efficient supervision and state representations in CWMs that are better aligned with program execution and data types. Read More
5 Useful Python Scripts to Automate Boring File TasksKDnuggets Tired of sifting through bloated folders, waiting on manual conversions, or not quite knowing what is on your drive? These Python scripts handle the file grunt work so you don’t have to.
Tired of sifting through bloated folders, waiting on manual conversions, or not quite knowing what is on your drive? These Python scripts handle the file grunt work so you don’t have to. Read More
Alibaba Open-Sources Zvec: An Embedded Vector Database Bringing SQLite-like Simplicity and High-Performance On-Device RAG to Edge ApplicationsMarkTechPost Alibaba Tongyi Lab research team released ‘Zvec’, an open source, in-process vector database that targets edge and on-device retrieval workloads. It is positioned as ‘the SQLite of vector databases’ because it runs as a library inside your application and does not require any external service or daemon. It is designed for retrieval augmented generation (RAG),
The post Alibaba Open-Sources Zvec: An Embedded Vector Database Bringing SQLite-like Simplicity and High-Performance On-Device RAG to Edge Applications appeared first on MarkTechPost.
Alibaba Tongyi Lab research team released ‘Zvec’, an open source, in-process vector database that targets edge and on-device retrieval workloads. It is positioned as ‘the SQLite of vector databases’ because it runs as a library inside your application and does not require any external service or daemon. It is designed for retrieval augmented generation (RAG),
The post Alibaba Open-Sources Zvec: An Embedded Vector Database Bringing SQLite-like Simplicity and High-Performance On-Device RAG to Edge Applications appeared first on MarkTechPost. Read More
Building Your Modern Data Analytics Stack with Python, Parquet, and DuckDBKDnuggets Modern data analytics doesn’t have to be complex. Learn how Python, Parquet, and DuckDB work together in practice.
Modern data analytics doesn’t have to be complex. Learn how Python, Parquet, and DuckDB work together in practice. Read More