A new tool is revealing the invisible networks inside cancerArtificial Intelligence News — ScienceDaily Spanish researchers have created a powerful new open-source tool that helps uncover the hidden genetic networks driving cancer. Called RNACOREX, the software can analyze thousands of molecular interactions at once, revealing how genes communicate inside tumors and how those signals relate to patient survival. Tested across 13 different cancer types using international data, the tool matches the predictive power of advanced AI systems—while offering something rare in modern analytics: clear, interpretable explanations that help scientists understand why tumors behave the way they do.
Spanish researchers have created a powerful new open-source tool that helps uncover the hidden genetic networks driving cancer. Called RNACOREX, the software can analyze thousands of molecular interactions at once, revealing how genes communicate inside tumors and how those signals relate to patient survival. Tested across 13 different cancer types using international data, the tool matches the predictive power of advanced AI systems—while offering something rare in modern analytics: clear, interpretable explanations that help scientists understand why tumors behave the way they do. Read More
How to Do Evals on a Bloated RAG PipelineTowards Data Science Comparing metrics across datasets and models
The post How to Do Evals on a Bloated RAG Pipeline appeared first on Towards Data Science.
Comparing metrics across datasets and models
The post How to Do Evals on a Bloated RAG Pipeline appeared first on Towards Data Science. Read More
What Happens When You Build an LLM Using Only 1s and 0sTowards Data Science An LLM that’s 41× more efficient and 9× faster than today’s standard models
The post What Happens When You Build an LLM Using Only 1s and 0s appeared first on Towards Data Science.
An LLM that’s 41× more efficient and 9× faster than today’s standard models
The post What Happens When You Build an LLM Using Only 1s and 0s appeared first on Towards Data Science. Read More
How to Build a Fully Autonomous Local Fleet-Maintenance Analysis Agent Using SmolAgents and Qwen ModelMarkTechPost In this tutorial, we walk through the process of creating a fully autonomous fleet-analysis agent using SmolAgents and a local Qwen model. We generate telemetry data, load it through a custom tool, and let our agent reason, analyze, and visualize maintenance risks without any external API calls. At each step of implementation, we see how
The post How to Build a Fully Autonomous Local Fleet-Maintenance Analysis Agent Using SmolAgents and Qwen Model appeared first on MarkTechPost.
In this tutorial, we walk through the process of creating a fully autonomous fleet-analysis agent using SmolAgents and a local Qwen model. We generate telemetry data, load it through a custom tool, and let our agent reason, analyze, and visualize maintenance risks without any external API calls. At each step of implementation, we see how
The post How to Build a Fully Autonomous Local Fleet-Maintenance Analysis Agent Using SmolAgents and Qwen Model appeared first on MarkTechPost. Read More
Google Introduces A2UI (Agent-to-User Interface): An Open Sourc Protocol for Agent Driven InterfacesMarkTechPost Google has open sourced A2UI, an Agent to User Interface specification and set of libraries that lets agents describe rich native interfaces in a declarative JSON format while client applications render them with their own components. The project targets a clear problem, how to let remote agents present secure, interactive interfaces across trust boundaries without
The post Google Introduces A2UI (Agent-to-User Interface): An Open Sourc Protocol for Agent Driven Interfaces appeared first on MarkTechPost.
Google has open sourced A2UI, an Agent to User Interface specification and set of libraries that lets agents describe rich native interfaces in a declarative JSON format while client applications render them with their own components. The project targets a clear problem, how to let remote agents present secure, interactive interfaces across trust boundaries without
The post Google Introduces A2UI (Agent-to-User Interface): An Open Sourc Protocol for Agent Driven Interfaces appeared first on MarkTechPost. Read More
Tesco signs three-year AI deal centred on customer experienceAI News For large retailers, the challenge with AI isn’t whether it can be useful, but how it fits into everyday work. A new three-year AI partnership by Tesco points to how one of the UK’s biggest supermarket groups is trying to achieve just that. Tesco plans to work with Mistral to develop AI tools that can
The post Tesco signs three-year AI deal centred on customer experience appeared first on AI News.
For large retailers, the challenge with AI isn’t whether it can be useful, but how it fits into everyday work. A new three-year AI partnership by Tesco points to how one of the UK’s biggest supermarket groups is trying to achieve just that. Tesco plans to work with Mistral to develop AI tools that can
The post Tesco signs three-year AI deal centred on customer experience appeared first on AI News. Read More
This AI finds simple rules where humans see only chaosArtificial Intelligence News — ScienceDaily A new AI developed at Duke University can uncover simple, readable rules behind extremely complex systems. It studies how systems evolve over time and reduces thousands of variables into compact equations that still capture real behavior. The method works across physics, engineering, climate science, and biology. Researchers say it could help scientists understand systems where traditional equations are missing or too complicated to write down.
A new AI developed at Duke University can uncover simple, readable rules behind extremely complex systems. It studies how systems evolve over time and reduces thousands of variables into compact equations that still capture real behavior. The method works across physics, engineering, climate science, and biology. Researchers say it could help scientists understand systems where traditional equations are missing or too complicated to write down. Read More
Adaptive Graph Pruning with Sudden-Events Evaluation for Traffic Prediction using Online Semi-Decentralized ST-GNNscs.AI updates on arXiv.org arXiv:2512.17352v1 Announce Type: cross
Abstract: Spatio-Temporal Graph Neural Networks (ST-GNNs) are well-suited for processing high-frequency data streams from geographically distributed sensors in smart mobility systems. However, their deployment at the edge across distributed compute nodes (cloudlets) createssubstantial communication overhead due to repeated transmission of overlapping node features between neighbouring cloudlets. To address this, we propose an adaptive pruning algorithm that dynamically filters redundant neighbour features while preserving the most informative spatial context for prediction. The algorithm adjusts pruning rates based on recent model performance, allowing each cloudlet to focus on regions experiencing traffic changes without compromising accuracy. Additionally, we introduce the Sudden Event Prediction Accuracy (SEPA), a novel event-focused metric designed to measure responsiveness to traffic slowdowns and recoveries, which are often missed by standard error metrics. We evaluate our approach in an online semi-decentralized setting with traditional FL, server-free FL, and Gossip Learning on two large-scale traffic datasets, PeMS-BAY and PeMSD7-M, across short-, mid-, and long-term prediction horizons. Experiments show that, in contrast to standard metrics, SEPA exposes the true value of spatial connectivity in predicting dynamic and irregular traffic. Our adaptive pruning algorithm maintains prediction accuracy while significantly lowering communication cost in all online semi-decentralized settings, demonstrating that communication can be reduced without compromising responsiveness to critical traffic events.
arXiv:2512.17352v1 Announce Type: cross
Abstract: Spatio-Temporal Graph Neural Networks (ST-GNNs) are well-suited for processing high-frequency data streams from geographically distributed sensors in smart mobility systems. However, their deployment at the edge across distributed compute nodes (cloudlets) createssubstantial communication overhead due to repeated transmission of overlapping node features between neighbouring cloudlets. To address this, we propose an adaptive pruning algorithm that dynamically filters redundant neighbour features while preserving the most informative spatial context for prediction. The algorithm adjusts pruning rates based on recent model performance, allowing each cloudlet to focus on regions experiencing traffic changes without compromising accuracy. Additionally, we introduce the Sudden Event Prediction Accuracy (SEPA), a novel event-focused metric designed to measure responsiveness to traffic slowdowns and recoveries, which are often missed by standard error metrics. We evaluate our approach in an online semi-decentralized setting with traditional FL, server-free FL, and Gossip Learning on two large-scale traffic datasets, PeMS-BAY and PeMSD7-M, across short-, mid-, and long-term prediction horizons. Experiments show that, in contrast to standard metrics, SEPA exposes the true value of spatial connectivity in predicting dynamic and irregular traffic. Our adaptive pruning algorithm maintains prediction accuracy while significantly lowering communication cost in all online semi-decentralized settings, demonstrating that communication can be reduced without compromising responsiveness to critical traffic events. Read More
Optimisation of Aircraft Maintenance Schedulescs.AI updates on arXiv.org arXiv:2512.17412v1 Announce Type: cross
Abstract: We present an aircraft maintenance scheduling problem, which requires suitably qualified staff to be assigned to maintenance tasks on each aircraft. The tasks on each aircraft must be completed within a given turn around window so that the aircraft may resume revenue earning service. This paper presents an initial study based on the application of an Evolutionary Algorithm to the problem. Evolutionary Algorithms evolve a solution to a problem by evaluating many possible solutions, focusing the search on those solutions that are of a higher quality, as defined by a fitness function. In this paper, we benchmark the algorithm on 60 generated problem instances to demonstrate the underlying representation and associated genetic operators.
arXiv:2512.17412v1 Announce Type: cross
Abstract: We present an aircraft maintenance scheduling problem, which requires suitably qualified staff to be assigned to maintenance tasks on each aircraft. The tasks on each aircraft must be completed within a given turn around window so that the aircraft may resume revenue earning service. This paper presents an initial study based on the application of an Evolutionary Algorithm to the problem. Evolutionary Algorithms evolve a solution to a problem by evaluating many possible solutions, focusing the search on those solutions that are of a higher quality, as defined by a fitness function. In this paper, we benchmark the algorithm on 60 generated problem instances to demonstrate the underlying representation and associated genetic operators. Read More
AutoMetrics: Approximate Human Judgements with Automatically Generated Evaluatorscs.AI updates on arXiv.org arXiv:2512.17267v1 Announce Type: cross
Abstract: Evaluating user-facing AI applications remains a central challenge, especially in open-ended domains such as travel planning, clinical note generation, or dialogue. The gold standard is user feedback (e.g., thumbs up/down) or behavioral signals (e.g., retention), but these are often scarce in prototypes and research projects, or too-slow to use for system optimization. We present AutoMetrics, a framework for synthesizing evaluation metrics under low-data constraints. AutoMetrics combines retrieval from MetricBank, a collection of 48 metrics we curate, with automatically generated LLM-as-a-Judge criteria informed by lightweight human feedback. These metrics are composed via regression to maximize correlation with human signal. AutoMetrics takes you from expensive measures to interpretable automatic metrics. Across 5 diverse tasks, AutoMetrics improves Kendall correlation with human ratings by up to 33.4% over LLM-as-a-Judge while requiring fewer than 100 feedback points. We show that AutoMetrics can be used as a proxy reward to equal effect as a verifiable reward. We release the full AutoMetrics toolkit and MetricBank to accelerate adaptive evaluation of LLM applications.
arXiv:2512.17267v1 Announce Type: cross
Abstract: Evaluating user-facing AI applications remains a central challenge, especially in open-ended domains such as travel planning, clinical note generation, or dialogue. The gold standard is user feedback (e.g., thumbs up/down) or behavioral signals (e.g., retention), but these are often scarce in prototypes and research projects, or too-slow to use for system optimization. We present AutoMetrics, a framework for synthesizing evaluation metrics under low-data constraints. AutoMetrics combines retrieval from MetricBank, a collection of 48 metrics we curate, with automatically generated LLM-as-a-Judge criteria informed by lightweight human feedback. These metrics are composed via regression to maximize correlation with human signal. AutoMetrics takes you from expensive measures to interpretable automatic metrics. Across 5 diverse tasks, AutoMetrics improves Kendall correlation with human ratings by up to 33.4% over LLM-as-a-Judge while requiring fewer than 100 feedback points. We show that AutoMetrics can be used as a proxy reward to equal effect as a verifiable reward. We release the full AutoMetrics toolkit and MetricBank to accelerate adaptive evaluation of LLM applications. Read More