Author: Tech Jacks Solutions

How to Build a Multi-Turn Crescendo Red-Teaming Pipeline to Evaluate and Stress-Test LLM Safety Using GarakMarkTechPost In this tutorial, we build an advanced, multi-turn crescendo-style red-teaming harness using Garak to evaluate how large language models behave under gradual conversational pressure. We implement a custom iterative probe and a lightweight detector to simulate realistic escalation patterns in which benign prompts slowly pivot toward sensitive requests, and we assess whether the model maintains
The post How to Build a Multi-Turn Crescendo Red-Teaming Pipeline to Evaluate and Stress-Test LLM Safety Using Garak appeared first on MarkTechPost.

 In this tutorial, we build an advanced, multi-turn crescendo-style red-teaming harness using Garak to evaluate how large language models behave under gradual conversational pressure. We implement a custom iterative probe and a lightweight detector to simulate realistic escalation patterns in which benign prompts slowly pivot toward sensitive requests, and we assess whether the model maintains
The post How to Build a Multi-Turn Crescendo Red-Teaming Pipeline to Evaluate and Stress-Test LLM Safety Using Garak appeared first on MarkTechPost. Read More  

This AI spots dangerous blood cells doctors often missArtificial Intelligence News — ScienceDaily A generative AI system can now analyze blood cells with greater accuracy and confidence than human experts, detecting subtle signs of diseases like leukemia. It not only spots rare abnormalities but also recognizes its own uncertainty, making it a powerful support tool for clinicians.

 A generative AI system can now analyze blood cells with greater accuracy and confidence than human experts, detecting subtle signs of diseases like leukemia. It not only spots rare abnormalities but also recognizes its own uncertainty, making it a powerful support tool for clinicians. Read More  

Allister Frost: Tackling workforce anxiety for AI integration successAI News Navigating workforce anxiety remains a primary challenge for leaders as AI integration defines modern enterprise success. For enterprise leaders, deploying AI is less a technical hurdle than a complex exercise in change management. The reality for many organisations is that, while algorithms offer efficiency, the human element dictates the speed of adoption. Data from the
The post Allister Frost: Tackling workforce anxiety for AI integration success appeared first on AI News.

 Navigating workforce anxiety remains a primary challenge for leaders as AI integration defines modern enterprise success. For enterprise leaders, deploying AI is less a technical hurdle than a complex exercise in change management. The reality for many organisations is that, while algorithms offer efficiency, the human element dictates the speed of adoption. Data from the
The post Allister Frost: Tackling workforce anxiety for AI integration success appeared first on AI News. Read More  

Why Your ML Model Works in Training But Fails in ProductionTowards Data Science Hard lessons from building production ML systems where data leaks, defaults lie, populations shift, and time does not behave the way we expect.
The post Why Your ML Model Works in Training But Fails in Production appeared first on Towards Data Science.

 Hard lessons from building production ML systems where data leaks, defaults lie, populations shift, and time does not behave the way we expect.
The post Why Your ML Model Works in Training But Fails in Production appeared first on Towards Data Science. Read More  

5 Useful Python Scripts for Effective Feature EngineeringKDnuggets Feature engineering doesn’t have to be complex. These 5 Python scripts help you create meaningful features that improve model performance.

 Feature engineering doesn’t have to be complex. These 5 Python scripts help you create meaningful features that improve model performance. Read More  

KALE-LM-Chem: Vision and Practice Toward an AI Brain for Chemistrycs.AI updates on arXiv.org arXiv:2409.18695v3 Announce Type: replace
Abstract: Recent advancements in large language models (LLMs) have demonstrated strong potential for enabling domain-specific intelligence. In this work, we present our vision for building an AI-powered chemical brain, which frames chemical intelligence around four core capabilities: information extraction, semantic parsing, knowledge-based QA, and reasoning & planning. We argue that domain knowledge and logic are essential pillars for enabling such a system to assist and accelerate scientific discovery. To initiate this effort, we introduce our first generation of large language models for chemistry: KALE-LM-Chem and KALE-LM-Chem-1.5, which have achieved outstanding performance in tasks related to the field of chemistry. We hope that our work serves as a strong starting point, helping to realize more intelligent AI and promoting the advancement of human science and technology, as well as societal development.

 arXiv:2409.18695v3 Announce Type: replace
Abstract: Recent advancements in large language models (LLMs) have demonstrated strong potential for enabling domain-specific intelligence. In this work, we present our vision for building an AI-powered chemical brain, which frames chemical intelligence around four core capabilities: information extraction, semantic parsing, knowledge-based QA, and reasoning & planning. We argue that domain knowledge and logic are essential pillars for enabling such a system to assist and accelerate scientific discovery. To initiate this effort, we introduce our first generation of large language models for chemistry: KALE-LM-Chem and KALE-LM-Chem-1.5, which have achieved outstanding performance in tasks related to the field of chemistry. We hope that our work serves as a strong starting point, helping to realize more intelligent AI and promoting the advancement of human science and technology, as well as societal development. Read More  

Generating Diverse TSP Tours via a Combination of Graph Pointer Network and Dispersioncs.AI updates on arXiv.org arXiv:2601.01132v2 Announce Type: replace-cross
Abstract: We address the Diverse Traveling Salesman Problem (D-TSP), a bi-criteria optimization challenge that seeks a set of $k$ distinct TSP tours. The objective requires every selected tour to have a length at most $c|T^*|$ (where $|T^*|$ is the optimal tour length) while minimizing the average Jaccard similarity across all tour pairs. This formulation is crucial for applications requiring both high solution quality and fault tolerance, such as logistics planning, robotics pathfinding or strategic patrolling. Current methods are limited: traditional heuristics, such as the Niching Memetic Algorithm (NMA) or bi-criteria optimization, incur high computational complexity $O(n^3)$, while modern neural approaches (e.g., RF-MA3S) achieve limited diversity quality and rely on complex, external mechanisms.
To overcome these limitations, we propose a novel hybrid framework that decomposes D-TSP into two efficient steps. First, we utilize a simple Graph Pointer Network (GPN), augmented with an approximated sequence entropy loss, to efficiently sample a large, diverse pool of high-quality tours. This simple modification effectively controls the quality-diversity trade-off without complex external mechanisms. Second, we apply a greedy algorithm that yields a 2-approximation for the dispersion problem to select the final $k$ maximally diverse tours from the generated pool. Our results demonstrate state-of-the-art performance. On the Berlin instance, our model achieves an average Jaccard index of $0.015$, significantly outperforming NMA ($0.081$) and RF-MA3S. By leveraging GPU acceleration, our GPN structure achieves a near-linear empirical runtime growth of $O(n)$. While maintaining solution diversity comparable to complex bi-criteria algorithms, our approach is over 360 times faster on large-scale instances (783 cities), delivering high-quality TSP solutions with unprecedented efficiency and simplicity.

 arXiv:2601.01132v2 Announce Type: replace-cross
Abstract: We address the Diverse Traveling Salesman Problem (D-TSP), a bi-criteria optimization challenge that seeks a set of $k$ distinct TSP tours. The objective requires every selected tour to have a length at most $c|T^*|$ (where $|T^*|$ is the optimal tour length) while minimizing the average Jaccard similarity across all tour pairs. This formulation is crucial for applications requiring both high solution quality and fault tolerance, such as logistics planning, robotics pathfinding or strategic patrolling. Current methods are limited: traditional heuristics, such as the Niching Memetic Algorithm (NMA) or bi-criteria optimization, incur high computational complexity $O(n^3)$, while modern neural approaches (e.g., RF-MA3S) achieve limited diversity quality and rely on complex, external mechanisms.
To overcome these limitations, we propose a novel hybrid framework that decomposes D-TSP into two efficient steps. First, we utilize a simple Graph Pointer Network (GPN), augmented with an approximated sequence entropy loss, to efficiently sample a large, diverse pool of high-quality tours. This simple modification effectively controls the quality-diversity trade-off without complex external mechanisms. Second, we apply a greedy algorithm that yields a 2-approximation for the dispersion problem to select the final $k$ maximally diverse tours from the generated pool. Our results demonstrate state-of-the-art performance. On the Berlin instance, our model achieves an average Jaccard index of $0.015$, significantly outperforming NMA ($0.081$) and RF-MA3S. By leveraging GPU acceleration, our GPN structure achieves a near-linear empirical runtime growth of $O(n)$. While maintaining solution diversity comparable to complex bi-criteria algorithms, our approach is over 360 times faster on large-scale instances (783 cities), delivering high-quality TSP solutions with unprecedented efficiency and simplicity. Read More  

Topological Signatures of ReLU Neural Network Activation Patternscs.AI updates on arXiv.org arXiv:2510.12700v2 Announce Type: replace-cross
Abstract: This paper explores the topological signatures of ReLU neural network activation patterns. We consider feedforward neural networks with ReLU activation functions and analyze the polytope decomposition of the feature space induced by the network. Mainly, we investigate how the Fiedler partition of the dual graph and show that it appears to correlate with the decision boundary — in the case of binary classification. Additionally, we compute the homology of the cellular decomposition — in a regression task — to draw similar patterns in behavior between the training loss and polyhedral cell-count, as the model is trained.

 arXiv:2510.12700v2 Announce Type: replace-cross
Abstract: This paper explores the topological signatures of ReLU neural network activation patterns. We consider feedforward neural networks with ReLU activation functions and analyze the polytope decomposition of the feature space induced by the network. Mainly, we investigate how the Fiedler partition of the dual graph and show that it appears to correlate with the decision boundary — in the case of binary classification. Additionally, we compute the homology of the cellular decomposition — in a regression task — to draw similar patterns in behavior between the training loss and polyhedral cell-count, as the model is trained. Read More  

CliCARE: Grounding Large Language Models in Clinical Guidelines for Decision Support over Longitudinal Cancer Electronic Health Recordscs.AI updates on arXiv.org arXiv:2507.22533v2 Announce Type: replace-cross
Abstract: Large Language Models (LLMs) hold significant promise for improving clinical decision support and reducing physician burnout by synthesizing complex, longitudinal cancer Electronic Health Records (EHRs). However, their implementation in this critical field faces three primary challenges: the inability to effectively process the extensive length and fragmented nature of patient records for accurate temporal analysis; a heightened risk of clinical hallucination, as conventional grounding techniques such as Retrieval-Augmented Generation (RAG) do not adequately incorporate process-oriented clinical guidelines; and unreliable evaluation metrics that hinder the validation of AI systems in oncology. To address these issues, we propose CliCARE, a framework for Grounding Large Language Models in Clinical Guidelines for Decision Support over Longitudinal Cancer Electronic Health Records. The framework operates by transforming unstructured, longitudinal EHRs into patient-specific Temporal Knowledge Graphs (TKGs) to capture long-range dependencies, and then grounding the decision support process by aligning these real-world patient trajectories with a normative guideline knowledge graph. This approach provides oncologists with evidence-grounded decision support by generating a high-fidelity clinical summary and an actionable recommendation. We validated our framework using large-scale, longitudinal data from a private Chinese cancer dataset and the public English MIMIC-IV dataset. In these settings, CliCARE significantly outperforms baselines, including leading long-context LLMs and Knowledge Graph-enhanced RAG methods. The clinical validity of our results is supported by a robust evaluation protocol, which demonstrates a high correlation with assessments made by oncologists.

 arXiv:2507.22533v2 Announce Type: replace-cross
Abstract: Large Language Models (LLMs) hold significant promise for improving clinical decision support and reducing physician burnout by synthesizing complex, longitudinal cancer Electronic Health Records (EHRs). However, their implementation in this critical field faces three primary challenges: the inability to effectively process the extensive length and fragmented nature of patient records for accurate temporal analysis; a heightened risk of clinical hallucination, as conventional grounding techniques such as Retrieval-Augmented Generation (RAG) do not adequately incorporate process-oriented clinical guidelines; and unreliable evaluation metrics that hinder the validation of AI systems in oncology. To address these issues, we propose CliCARE, a framework for Grounding Large Language Models in Clinical Guidelines for Decision Support over Longitudinal Cancer Electronic Health Records. The framework operates by transforming unstructured, longitudinal EHRs into patient-specific Temporal Knowledge Graphs (TKGs) to capture long-range dependencies, and then grounding the decision support process by aligning these real-world patient trajectories with a normative guideline knowledge graph. This approach provides oncologists with evidence-grounded decision support by generating a high-fidelity clinical summary and an actionable recommendation. We validated our framework using large-scale, longitudinal data from a private Chinese cancer dataset and the public English MIMIC-IV dataset. In these settings, CliCARE significantly outperforms baselines, including leading long-context LLMs and Knowledge Graph-enhanced RAG methods. The clinical validity of our results is supported by a robust evaluation protocol, which demonstrates a high correlation with assessments made by oncologists. Read More