QMBench: A Research Level Benchmark for Quantum Materials Researchcs.AI updates on arXiv.org arXiv:2512.19753v1 Announce Type: cross
Abstract: We introduce QMBench, a comprehensive benchmark designed to evaluate the capability of large language model agents in quantum materials research. This specialized benchmark assesses the model’s ability to apply condensed matter physics knowledge and computational techniques such as density functional theory to solve research problems in quantum materials science. QMBench encompasses different domains of the quantum material research, including structural properties, electronic properties, thermodynamic and other properties, symmetry principle and computational methodologies. By providing a standardized evaluation framework, QMBench aims to accelerate the development of an AI scientist capable of making creative contributions to quantum materials research. We expect QMBench to be developed and constantly improved by the research community.
arXiv:2512.19753v1 Announce Type: cross
Abstract: We introduce QMBench, a comprehensive benchmark designed to evaluate the capability of large language model agents in quantum materials research. This specialized benchmark assesses the model’s ability to apply condensed matter physics knowledge and computational techniques such as density functional theory to solve research problems in quantum materials science. QMBench encompasses different domains of the quantum material research, including structural properties, electronic properties, thermodynamic and other properties, symmetry principle and computational methodologies. By providing a standardized evaluation framework, QMBench aims to accelerate the development of an AI scientist capable of making creative contributions to quantum materials research. We expect QMBench to be developed and constantly improved by the research community. Read More
4 Techniques to Optimize AI Coding EfficiencyTowards Data Science Learn how to code more effectively using AI
The post 4 Techniques to Optimize AI Coding Efficiency appeared first on Towards Data Science.
Learn how to code more effectively using AI
The post 4 Techniques to Optimize AI Coding Efficiency appeared first on Towards Data Science. Read More
The future of rail: Watching, predicting, and learningAI News A recent industry report [PDF] argues that Britain’s railway network could carry an extra billion journeys by the mid-2030s, building on the 1.6 billion passenger rail journeys recorded to year-end March 2024. The next decade will involve a combination of complexity and control, as more digital systems, data, and interconnected suppliers create the potential for
The post The future of rail: Watching, predicting, and learning appeared first on AI News.
A recent industry report [PDF] argues that Britain’s railway network could carry an extra billion journeys by the mid-2030s, building on the 1.6 billion passenger rail journeys recorded to year-end March 2024. The next decade will involve a combination of complexity and control, as more digital systems, data, and interconnected suppliers create the potential for
The post The future of rail: Watching, predicting, and learning appeared first on AI News. Read More
Why Disney is embedding generative AI into its operating modelAI News For a company built on intellectual property, scale creates a familiar tension. Disney needs to produce and distribute content across many formats and audiences, while keeping tight control over rights, safety, and brand consistency. Generative AI promises speed and flexibility, but unmanaged use risks creating legal, creative, and operational drag. Disney’s agreement with OpenAI shows
The post Why Disney is embedding generative AI into its operating model appeared first on AI News.
For a company built on intellectual property, scale creates a familiar tension. Disney needs to produce and distribute content across many formats and audiences, while keeping tight control over rights, safety, and brand consistency. Generative AI promises speed and flexibility, but unmanaged use risks creating legal, creative, and operational drag. Disney’s agreement with OpenAI shows
The post Why Disney is embedding generative AI into its operating model appeared first on AI News. Read More
InstaDeep Introduces Nucleotide Transformer v3 (NTv3): A New Multi-Species Genomics Foundation Model, Designed for 1 Mb Context Lengths at Single-Nucleotide ResolutionMarkTechPost Genomic prediction and design now require models that connect local motifs with megabase scale regulatory context and that operate across many organisms. Nucleotide Transformer v3, or NTv3, is InstaDeep’s new multi species genomics foundation model for this setting. It unifies representation learning, functional track and genome annotation prediction, and controllable sequence generation in a single
The post InstaDeep Introduces Nucleotide Transformer v3 (NTv3): A New Multi-Species Genomics Foundation Model, Designed for 1 Mb Context Lengths at Single-Nucleotide Resolution appeared first on MarkTechPost.
Genomic prediction and design now require models that connect local motifs with megabase scale regulatory context and that operate across many organisms. Nucleotide Transformer v3, or NTv3, is InstaDeep’s new multi species genomics foundation model for this setting. It unifies representation learning, functional track and genome annotation prediction, and controllable sequence generation in a single
The post InstaDeep Introduces Nucleotide Transformer v3 (NTv3): A New Multi-Species Genomics Foundation Model, Designed for 1 Mb Context Lengths at Single-Nucleotide Resolution appeared first on MarkTechPost. Read More
The U.S. Securities and Exchange Commission (SEC) has filed charges against multiple companies for their alleged involvement in an elaborate cryptocurrency scam that swindled more than $14 million from retail investors. The complaint charged crypto asset trading platforms Morocoin Tech Corp., Berge Blockchain Technology Co., Ltd., and Cirkor Inc., as well as investment clubs AI […]
A K-Means, Ward and DBSCAN repeatability studycs.AI updates on arXiv.org arXiv:2512.19772v1 Announce Type: cross
Abstract: Reproducibility is essential in machine learning because it ensures that a model or experiment yields the same scientific conclusion. For specific algorithms repeatability with bitwise identical results is also a key for scientific integrity because it allows debugging. We decomposed several very popular clustering algorithms: K-Means, DBSCAN and Ward into their fundamental steps, and we identify the conditions required to achieve repeatability at each stage. We use an implementation example with the Python library scikit-learn to examine the repeatable aspects of each method. Our results reveal inconsistent results with K-Means when the number of OpenMP threads exceeds two. This work aims to raise awareness of this issue among both users and developers, encouraging further investigation and potential fixes.
arXiv:2512.19772v1 Announce Type: cross
Abstract: Reproducibility is essential in machine learning because it ensures that a model or experiment yields the same scientific conclusion. For specific algorithms repeatability with bitwise identical results is also a key for scientific integrity because it allows debugging. We decomposed several very popular clustering algorithms: K-Means, DBSCAN and Ward into their fundamental steps, and we identify the conditions required to achieve repeatability at each stage. We use an implementation example with the Python library scikit-learn to examine the repeatable aspects of each method. Our results reveal inconsistent results with K-Means when the number of OpenMP threads exceeds two. This work aims to raise awareness of this issue among both users and developers, encouraging further investigation and potential fixes. Read More
Accelerate Enterprise AI Development using Weights & Biases and Amazon Bedrock AgentCoreArtificial Intelligence In this post, we demonstrate how to use Foundation Models (FMs) from Amazon Bedrock and the newly launched Amazon Bedrock AgentCore alongside W&B Weave to help build, evaluate, and monitor enterprise AI solutions. We cover the complete development lifecycle from tracking individual FM calls to monitoring complex agent workflows in production.
In this post, we demonstrate how to use Foundation Models (FMs) from Amazon Bedrock and the newly launched Amazon Bedrock AgentCore alongside W&B Weave to help build, evaluate, and monitor enterprise AI solutions. We cover the complete development lifecycle from tracking individual FM calls to monitoring complex agent workflows in production. Read More
How to Build a Proactive Pre-Emptive Churn Prevention Agent with Intelligent Observation and Strategy FormationMarkTechPost In this tutorial, we build a fully functional Pre-Emptive Churn Agent that proactively identifies at-risk users and drafts personalized re-engagement emails before they cancel. Rather than waiting for churn to occur, we design an agentic loop in which we observe user inactivity, analyze behavioral patterns, strategize incentives, and generate human-ready email drafts using Gemini. We
The post How to Build a Proactive Pre-Emptive Churn Prevention Agent with Intelligent Observation and Strategy Formation appeared first on MarkTechPost.
In this tutorial, we build a fully functional Pre-Emptive Churn Agent that proactively identifies at-risk users and drafts personalized re-engagement emails before they cancel. Rather than waiting for churn to occur, we design an agentic loop in which we observe user inactivity, analyze behavioral patterns, strategize incentives, and generate human-ready email drafts using Gemini. We
The post How to Build a Proactive Pre-Emptive Churn Prevention Agent with Intelligent Observation and Strategy Formation appeared first on MarkTechPost. Read More
How dLocal automated compliance reviews using Amazon Quick AutomateArtificial Intelligence In this post, we share how dLocal worked closely with the AWS team to help shape the product roadmap, reinforce its role as an industry innovator, and set new benchmarks for operational excellence in the global fintech landscape.
In this post, we share how dLocal worked closely with the AWS team to help shape the product roadmap, reinforce its role as an industry innovator, and set new benchmarks for operational excellence in the global fintech landscape. Read More
