Developers are leaning more heavily on AI for code generation, but in 2026, the development pipeline and security need to be prioritized. Read More
ChatGPT’s new formatting blocks make its UI look more like a task tool BleepingComputerMayank Parmar
OpenAI has quietly rolled out ‘formatting blocks,’ which tweak GPT’s layout to match the UI of the task it is supposed to execute. […] Read More
Microsoft is rolling out hardware-accelerated BitLocker in Windows 11 to address growing performance and security concerns by leveraging the capabilities of system-on-a-chip and CPU. […] Read More
MongoDB has warned IT admins to immediately patch a high-severity vulnerability that can be exploited in remote code execution (RCE) attacks targeting vulnerable servers. […] Read More
Cybersecurity researchers have discovered a new variant of a macOS information stealer called MacSync that’s delivered by means of a digitally signed, notarized Swift application masquerading as a messaging app installer to bypass Apple’s Gatekeeper checks. “Unlike earlier MacSync Stealer variants that primarily rely on drag-to-terminal or ClickFix-style techniques, this sample adopts a more Read More
The U.S. government has seized the ‘web3adspanels.org’ domain and the associated database used by cybercriminals to host bank login credentials stolen in account takeover attacks. […] Read More
Microsoft Teams to let admins block external users via Defender portal BleepingComputerSergiu Gatlan
Microsoft announced that security administrators will soon be able to block external users from sending messages, calls, or meeting invitations to members of their organization via Teams. […] Read More
Keeping Probabilities Honest: The Jacobian AdjustmentTowards Data Science An intuitive explanation of transforming random variables correctly.
The post Keeping Probabilities Honest: The Jacobian Adjustment appeared first on Towards Data Science.
An intuitive explanation of transforming random variables correctly.
The post Keeping Probabilities Honest: The Jacobian Adjustment appeared first on Towards Data Science. Read More
MiniMax Releases M2.1: An Enhanced M2 Version with Features like Multi-Coding Language Support, API Integration, and Improved Tools for Structured CodingMarkTechPost Just months after releasing M2—a fast, low-cost model designed for agents and code—MiniMax has introduced an enhanced version: MiniMax M2.1. M2 already stood out for its efficiency, running at roughly 8% of the cost of Claude Sonnet while delivering significantly higher speed. More importantly, it introduced a different computational and reasoning pattern, particularly in how
The post MiniMax Releases M2.1: An Enhanced M2 Version with Features like Multi-Coding Language Support, API Integration, and Improved Tools for Structured Coding appeared first on MarkTechPost.
Just months after releasing M2—a fast, low-cost model designed for agents and code—MiniMax has introduced an enhanced version: MiniMax M2.1. M2 already stood out for its efficiency, running at roughly 8% of the cost of Claude Sonnet while delivering significantly higher speed. More importantly, it introduced a different computational and reasoning pattern, particularly in how
The post MiniMax Releases M2.1: An Enhanced M2 Version with Features like Multi-Coding Language Support, API Integration, and Improved Tools for Structured Coding appeared first on MarkTechPost. Read More
Learning Treatment Policies From Multimodal Electronic Health Recordscs.AI updates on arXiv.org arXiv:2507.20993v2 Announce Type: replace-cross
Abstract: We study how to learn effective treatment policies from multimodal electronic health records (EHRs) that consist of tabular data and clinical text. These policies can help physicians make better treatment decisions and allocate healthcare resources more efficiently. Causal policy learning methods prioritize patients with the largest expected treatment benefit. Yet, existing estimators assume tabular covariates that satisfy strong causal assumptions, which are typically violated in the multimodal setting. As a result, predictive models of baseline risk are commonly used in practice to guide such decisions, as they extend naturally to multimodal data. However, such risk-based policies are not designed to identify which patients benefit most from treatment. We propose an extension of causal policy learning that uses expert-provided annotations during training to supervise treatment effect estimation, while using only multimodal representations as input during inference. We show that the proposed method achieves strong empirical performance across synthetic, semi-synthetic, and real-world EHR datasets, thereby offering practical insights into applying causal machine learning to realistic clinical data.
arXiv:2507.20993v2 Announce Type: replace-cross
Abstract: We study how to learn effective treatment policies from multimodal electronic health records (EHRs) that consist of tabular data and clinical text. These policies can help physicians make better treatment decisions and allocate healthcare resources more efficiently. Causal policy learning methods prioritize patients with the largest expected treatment benefit. Yet, existing estimators assume tabular covariates that satisfy strong causal assumptions, which are typically violated in the multimodal setting. As a result, predictive models of baseline risk are commonly used in practice to guide such decisions, as they extend naturally to multimodal data. However, such risk-based policies are not designed to identify which patients benefit most from treatment. We propose an extension of causal policy learning that uses expert-provided annotations during training to supervise treatment effect estimation, while using only multimodal representations as input during inference. We show that the proposed method achieves strong empirical performance across synthetic, semi-synthetic, and real-world EHR datasets, thereby offering practical insights into applying causal machine learning to realistic clinical data. Read More