# Tech Jacks Solutions > Secure Your Tomorrow. Simplify Your Today. Scale Without Limits. ## Posts - [CISA retires 10 emergency cyber orders in rare bulk closure BleepingComputerLawrence Abrams](https://techjacksolutions.com/cisa-retires-10-emergency-cyber-orders-in-rare-bulk-closure-bleepingcomputerlawrence-abrams/): The U.S. Cybersecurity and Infrastructure Security Agency (CISA) has retired 10 Emergency Directives issued between 2019 and 2024, saying that the required actions have been completed or are now covered by Binding Operational Directive 22-01. […] Read More  - [5 Useful Python Scripts to Automate Data Cleaning KDnuggets](https://techjacksolutions.com/5-useful-python-scripts-to-automate-data-cleaning-kdnuggets/): 5 Useful Python Scripts to Automate Data CleaningKDnuggets Tired of repetitive data cleaning tasks? This article covers five Python scripts that handle common data cleaning tasks efficiently and reliably.  Tired of repetitive data cleaning tasks? This article covers five Python scripts that handle common data cleaning tasks efficiently and reliably. Read More   - [From cloud to factory – humanoid robots coming to workplaces AI News](https://techjacksolutions.com/from-cloud-to-factory-humanoid-robots-coming-to-workplaces-ai-news/): From cloud to factory – humanoid robots coming to workplacesAI News The Microsoft-Hexagon partnerships may mark a turning point in the acceptance of humanoid robots in the workplace, as prototypes become operational realities. The post From cloud to factory – humanoid robots coming to workplaces appeared first on AI News.  The Microsoft-Hexagon partnerships may mark a turning point in the acceptance of humanoid robots in the workplace, as prototypes become operational realities. The post From cloud to factory – humanoid robots coming to workplaces appeared first on AI News. Read More   - [Mastering Non-Linear Data: A Guide to Scikit-Learn’s SplineTransformer Towards Data Science](https://techjacksolutions.com/mastering-non-linear-data-a-guide-to-scikit-learns-splinetransformer-towards-data-science/): Mastering Non-Linear Data: A Guide to Scikit-Learn’s SplineTransformerTowards Data Science Forget stiff lines and wild polynomials. Discover why Splines are the "Goldilocks" of feature engineering, offering the perfect balance of flexibility and discipline for non-linear data using Scikit-Learn’s SplineTransformer. The post Mastering Non-Linear Data: A Guide to Scikit-Learn’s SplineTransformer appeared first on Towards Data Science.  Forget stiff lines and wild polynomials. Discover why Splines are the "Goldilocks" of feature engineering, offering the perfect balance of flexibility and discipline for non-linear data using Scikit-Learn’s SplineTransformer. The post Mastering Non-Linear Data: A Guide to Scikit-Learn’s SplineTransformer appeared first on Towards Data Science. Read More   - [Autonomy without accountability: The real AI risk AI News](https://techjacksolutions.com/autonomy-without-accountability-the-real-ai-risk-ai-news/): Autonomy without accountability: The real AI riskAI News If you have ever taken a self-driving Uber through downtown LA, you might recognise the strange sense of uncertainty that settles in when there is no driver and no conversation, just a quiet car making assumptions about the world around it. The journey feels fine until the car misreads a shadow or slows abruptly for The post Autonomy without accountability: The real AI risk appeared first on AI News.  If you have ever taken a self-driving Uber through downtown LA, you might recognise the strange sense of uncertainty that settles in when there is no driver and no conversation, just a quiet car making assumptions about the world around it. The journey feels fine until the car misreads a shadow or slows abruptly for The post Autonomy without accountability: The real AI risk appeared first on AI News. Read More   - [Architecting TrueLook’s AI-powered construction safety system on Amazon SageMaker AI Artificial Intelligence](https://techjacksolutions.com/architecting-truelooks-ai-powered-construction-safety-system-on-amazon-sagemaker-ai-artificial-intelligence/): Architecting TrueLook’s AI-powered construction safety system on Amazon SageMaker AIArtificial Intelligence This post provides a detailed architectural overview of how TrueLook built its AI-powered safety monitoring system using SageMaker AI, highlighting key technical decisions, pipeline design patterns, and MLOps best practices. You will gain valuable insights into designing scalable computer vision solutions on AWS, particularly around model training workflows, automated pipeline creation, and production deployment strategies for real-time inference.  This post provides a detailed architectural overview of how TrueLook built its AI-powered safety monitoring system using SageMaker AI, highlighting key technical decisions, pipeline design patterns, and MLOps best practices. You will gain valuable insights into designing scalable computer vision solutions on AWS, particularly around model training workflows, automated pipeline creation, and production deployment strategies for real-time inference. Read More   - [How Beekeeper optimized user personalization with Amazon Bedrock Artificial Intelligence](https://techjacksolutions.com/how-beekeeper-optimized-user-personalization-with-amazon-bedrock-artificial-intelligence/): How Beekeeper optimized user personalization with Amazon BedrockArtificial Intelligence Beekeeper’s automated leaderboard approach and human feedback loop system for dynamic LLM and prompt pair selection addresses the key challenges organizations face in navigating the rapidly evolving landscape of language models.  Beekeeper’s automated leaderboard approach and human feedback loop system for dynamic LLM and prompt pair selection addresses the key challenges organizations face in navigating the rapidly evolving landscape of language models. Read More   - [How LLMs Handle Infinite Context With Finite Memory Towards Data Science](https://techjacksolutions.com/how-llms-handle-infinite-context-with-finite-memory-towards-data-science/): How LLMs Handle Infinite Context With Finite MemoryTowards Data Science Achieving infinite context with 114× less memory The post How LLMs Handle Infinite Context With Finite Memory appeared first on Towards Data Science.  Achieving infinite context with 114× less memory The post How LLMs Handle Infinite Context With Finite Memory appeared first on Towards Data Science. Read More   - [Datadog: How AI code reviews slash incident risk AI News](https://techjacksolutions.com/datadog-how-ai-code-reviews-slash-incident-risk-ai-news/): Datadog: How AI code reviews slash incident riskAI News Integrating AI into code review workflows allows engineering leaders to detect systemic risks that often evade human detection at scale. For engineering leaders managing distributed systems, the trade-off between deployment speed and operational stability often defines the success of their platform. Datadog, a company responsible for the observability of complex infrastructures worldwide, operates under intense The post Datadog: How AI code reviews slash incident risk appeared first on AI News.  Integrating AI into code review workflows allows engineering leaders to detect systemic risks that often evade human detection at scale. For engineering leaders managing distributed systems, the trade-off between deployment speed and operational stability often defines the success of their platform. Datadog, a company responsible for the observability of complex infrastructures worldwide, operates under intense The post Datadog: How AI code reviews slash incident risk appeared first on AI News. Read More   - [Accelerating LLM inference with post-training weight and activation using AWQ and GPTQ on Amazon SageMaker AI Artificial Intelligence](https://techjacksolutions.com/accelerating-llm-inference-with-post-training-weight-and-activation-using-awq-and-gptq-on-amazon-sagemaker-ai-artificial-intelligence/): Accelerating LLM inference with post-training weight and activation using AWQ and GPTQ on Amazon SageMaker AIArtificial Intelligence Quantized models can be seamlessly deployed on Amazon SageMaker AI using a few lines of code. In this post, we explore why quantization matters—how it enables lower-cost inference, supports deployment on resource-constrained hardware, and reduces both the financial and environmental impact of modern LLMs, while preserving most of their original performance. We also take a deep dive into the principles behind PTQ and demonstrate how to quantize the model of your choice and deploy it on Amazon SageMaker.  Quantized models can be seamlessly deployed on Amazon SageMaker AI using a few lines of code. In this post, we explore why quantization matters—how it enables lower-cost inference, supports deployment on resource-constrained hardware, and reduces both the financial and environmental impact of modern LLMs, while preserving most of their original performance. We also take a deep dive into the principles behind PTQ and demonstrate how to quantize the model of your choice and deploy it on Amazon SageMaker. Read More   - [Microeconomic Foundations of Multi-Agent Learning AI updates on arXiv.org](https://techjacksolutions.com/microeconomic-foundations-of-multi-agent-learning-ai-updates-on-arxiv-org/): Microeconomic Foundations of Multi-Agent Learningcs.AI updates on arXiv.org arXiv:2601.03451v1 Announce Type: cross Abstract: Modern AI systems increasingly operate inside markets and institutions where data, behavior, and incentives are endogenous. This paper develops an economic foundation for multi-agent learning by studying a principal-agent interaction in a Markov decision process with strategic externalities, where both the principal and the agent learn over time. We propose a two-phase incentive mechanism that first estimates implementable transfers and then uses them to steer long-run dynamics; under mild regret-based rationality and exploration conditions, the mechanism achieves sublinear social-welfare regret and thus asymptotically optimal welfare. Simulations illustrate how even coarse incentives can correct inefficient learning under stateful externalities, highlighting the necessity of incentive-aware design for safe and welfare-aligned AI in markets and insurance.  arXiv:2601.03451v1 Announce Type: cross Abstract: Modern AI systems increasingly operate inside markets and institutions where data, behavior, and incentives are endogenous. This paper develops an economic foundation for multi-agent learning by studying a principal-agent interaction in a Markov decision process with strategic externalities, where both the principal and the agent learn over time. We propose a two-phase incentive mechanism that first estimates implementable transfers and then uses them to steer long-run dynamics; under mild regret-based rationality and exploration conditions, the mechanism achieves sublinear social-welfare regret and thus asymptotically optimal welfare. Simulations illustrate how even coarse incentives can correct inefficient learning under stateful externalities, highlighting the necessity of incentive-aware design for safe and welfare-aligned AI in markets and insurance. Read More   - [Bootstrapping Code Translation with Weighted Multilanguage Exploration AI updates on arXiv.org](https://techjacksolutions.com/bootstrapping-code-translation-with-weighted-multilanguage-exploration-ai-updates-on-arxiv-org/): Bootstrapping Code Translation with Weighted Multilanguage Explorationcs.AI updates on arXiv.org arXiv:2601.03512v1 Announce Type: cross Abstract: Code translation across multiple programming languages is essential yet challenging due to two vital obstacles: scarcity of parallel data paired with executable test oracles, and optimization imbalance when handling diverse language pairs. We propose BootTrans, a bootstrapping method that resolves both obstacles. Its key idea is to leverage the functional invariance and cross-lingual portability of test suites, adapting abundant pivot-language unit tests to serve as universal verification oracles for multilingual RL training. Our method introduces a dual-pool architecture with seed and exploration pools to progressively expand training data via execution-guided experience collection. Furthermore, we design a language-aware weighting mechanism that dynamically prioritizes harder translation directions based on relative performance across sibling languages, mitigating optimization imbalance. Extensive experiments on the HumanEval-X and TransCoder-Test benchmarks demonstrate substantial improvements over baseline LLMs across all translation directions, with ablations validating the effectiveness of both bootstrapping and weighting components.  arXiv:2601.03512v1 Announce Type: cross Abstract: Code translation across multiple programming languages is essential yet challenging due to two vital obstacles: scarcity of parallel data paired with executable test oracles, and optimization imbalance when handling diverse language pairs. We propose BootTrans, a bootstrapping method that resolves both obstacles. Its key idea is to leverage the functional invariance and cross-lingual portability of test suites, adapting abundant pivot-language unit tests to serve as universal verification oracles for multilingual RL training. Our method introduces a dual-pool architecture with seed and exploration pools to progressively expand training data via execution-guided experience collection. Furthermore, we design a language-aware weighting mechanism that dynamically prioritizes harder translation directions based on relative performance across sibling languages, mitigating optimization imbalance. Extensive experiments on the HumanEval-X and TransCoder-Test benchmarks demonstrate substantial improvements over baseline LLMs across all translation directions, with ablations validating the effectiveness of both bootstrapping and weighting components. Read More   - [Efficient Sequential Recommendation for Long Term User Interest Via Personalization AI updates on arXiv.org](https://techjacksolutions.com/efficient-sequential-recommendation-for-long-term-user-interest-via-personalization-ai-updates-on-arxiv-org/): Efficient Sequential Recommendation for Long Term User Interest Via Personalizationcs.AI updates on arXiv.org arXiv:2601.03479v1 Announce Type: cross Abstract: Recent years have witnessed success of sequential modeling, generative recommender, and large language model for recommendation. Though the scaling law has been validated for sequential models, it showed inefficiency in computational capacity when considering real-world applications like recommendation, due to the non-linear(quadratic) increasing nature of the transformer model. To improve the efficiency of the sequential model, we introduced a novel approach to sequential recommendation that leverages personalization techniques to enhance efficiency and performance. Our method compresses long user interaction histories into learnable tokens, which are then combined with recent interactions to generate recommendations. This approach significantly reduces computational costs while maintaining high recommendation accuracy. Our method could be applied to existing transformer based recommendation models, e.g., HSTU and HLLM. Extensive experiments on multiple sequential models demonstrate its versatility and effectiveness. Source code is available at href{https://github.com/facebookresearch/PerSRec}{https://github.com/facebookresearch/PerSRec}.  arXiv:2601.03479v1 Announce Type: cross Abstract: Recent years have witnessed success of sequential modeling, generative recommender, and large language model for recommendation. Though the scaling law has been validated for sequential models, it showed inefficiency in computational capacity when considering real-world applications like recommendation, due to the non-linear(quadratic) increasing nature of the transformer model. To improve the efficiency of the sequential model, we introduced a novel approach to sequential recommendation that leverages personalization techniques to enhance efficiency and performance. Our method compresses long user interaction histories into learnable tokens, which are then combined with recent interactions to generate recommendations. This approach significantly reduces computational costs while maintaining high recommendation accuracy. Our method could be applied to existing transformer based recommendation models, e.g., HSTU and HLLM. Extensive experiments on multiple sequential models demonstrate its versatility and effectiveness. Source code is available at href{https://github.com/facebookresearch/PerSRec}{https://github.com/facebookresearch/PerSRec}. Read More   - [ReStyle-TTS: Relative and Continuous Style Control for Zero-Shot Speech Synthesis AI updates on arXiv.org](https://techjacksolutions.com/restyle-tts-relative-and-continuous-style-control-for-zero-shot-speech-synthesis-ai-updates-on-arxiv-org/): ReStyle-TTS: Relative and Continuous Style Control for Zero-Shot Speech Synthesiscs.AI updates on arXiv.org arXiv:2601.03632v1 Announce Type: cross Abstract: Zero-shot text-to-speech models can clone a speaker's timbre from a short reference audio, but they also strongly inherit the speaking style present in the reference. As a result, synthesizing speech with a desired style often requires carefully selecting reference audio, which is impractical when only limited or mismatched references are available. While recent controllable TTS methods attempt to address this issue, they typically rely on absolute style targets and discrete textual prompts, and therefore do not support continuous and reference-relative style control. We propose ReStyle-TTS, a framework that enables continuous and reference-relative style control in zero-shot TTS. Our key insight is that effective style control requires first reducing the model's implicit dependence on reference style before introducing explicit control mechanisms. To this end, we introduce Decoupled Classifier-Free Guidance (DCFG), which independently controls text and reference guidance, reducing reliance on reference style while preserving text fidelity. On top of this, we apply style-specific LoRAs together with Orthogonal LoRA Fusion to enable continuous and disentangled multi-attribute control, and introduce a Timbre Consistency Optimization module to mitigate timbre drift caused by weakened reference guidance. Experiments show that ReStyle-TTS enables user-friendly, continuous, and relative control over pitch, energy, and multiple emotions while maintaining intelligibility and speaker timbre, and performs robustly in challenging mismatched reference-target style scenarios.  arXiv:2601.03632v1 Announce Type: cross Abstract: Zero-shot text-to-speech models can clone a speaker's timbre from a short reference audio, but they also strongly inherit the speaking style present in the reference. As a result, synthesizing speech with a desired style often requires carefully selecting reference audio, which is impractical when only limited or mismatched references are available. While recent controllable TTS methods attempt to address this issue, they typically rely on absolute style targets and discrete textual prompts, and therefore do not support continuous and reference-relative style control. We propose ReStyle-TTS, a framework that enables continuous and reference-relative style control in zero-shot TTS. Our key insight is that effective style control requires first reducing the model's implicit dependence on reference style before introducing explicit control mechanisms. To this end, we introduce Decoupled Classifier-Free Guidance (DCFG), which independently controls text and reference guidance, reducing reliance on reference style while preserving text fidelity. On top of this, we apply style-specific LoRAs together with Orthogonal LoRA Fusion to enable continuous and disentangled multi-attribute control, and introduce a Timbre Consistency Optimization module to mitigate timbre drift caused by weakened reference guidance. Experiments show that ReStyle-TTS enables user-friendly, continuous, and relative control over pitch, energy, and multiple emotions while maintaining intelligibility and speaker timbre, and performs robustly in challenging mismatched reference-target style scenarios. Read More   - [Teaching a Neural Network the Mandelbrot Set Towards Data Science](https://techjacksolutions.com/teaching-a-neural-network-the-mandelbrot-set-towards-data-science/): Teaching a Neural Network the Mandelbrot SetTowards Data Science And why Fourier features change everything The post Teaching a Neural Network the Mandelbrot Set appeared first on Towards Data Science.  And why Fourier features change everything The post Teaching a Neural Network the Mandelbrot Set appeared first on Towards Data Science. Read More   - [What is NIST AI RMF MANAGE? A Practical Guide - 2026](https://techjacksolutions.com/what-is-nist-ai-rmf-manage/): Author: Derrick D. JacksonTitle: Founder & Senior Director of Cloud Security Architecture & RiskCredentials: CISSP, CRISC, CCSPLast updated: January 8th, 2026 Hello Everyone, Help us grow our community by sharing and/or supporting us on other platforms. This allow us to show verification that what we are doing is valued. It also allows us to plan and allocate resources to improve what we are doing, as we then know others are interested/supportive. What is NIST AI RMF MANAGE? NIST MANAGE in 60 Seconds: You’ve mapped your AI system’s context and measured its risks. Now what? NIST MANAGE is where you actually do […] - [FBI warns about Kimsuky hackers using QR codes to phish U.S. orgs BleepingComputerBill Toulas](https://techjacksolutions.com/fbi-warns-about-kimsuky-hackers-using-qr-codes-to-phish-u-s-orgs-bleepingcomputerbill-toulas/): The North Korean state-sponsored hacker group Kimsuki is using malicious QR codes in spearphishing campaigns that target U.S. organizations, the Federal Bureau of Investigation warns in a flash alert. […] Read More  - [Netomi’s lessons for scaling agentic systems into the enterprise OpenAI News](https://techjacksolutions.com/netomis-lessons-for-scaling-agentic-systems-into-the-enterprise-openai-news/): Netomi’s lessons for scaling agentic systems into the enterpriseOpenAI News How Netomi scales enterprise AI agents using GPT-4.1 and GPT-5.2—combining concurrency, governance, and multi-step reasoning for reliable production workflows.  How Netomi scales enterprise AI agents using GPT-4.1 and GPT-5.2—combining concurrency, governance, and multi-step reasoning for reliable production workflows. Read More   - [10 Most Popular GitHub Repositories for Learning AI KDnuggets](https://techjacksolutions.com/10-most-popular-github-repositories-for-learning-ai-kdnuggets/): 10 Most Popular GitHub Repositories for Learning AIKDnuggets The most popular GitHub repositories to help you learn AI, from fundamentals and math to LLMs, agents, computer vision, and real-world production systems.  The most popular GitHub repositories to help you learn AI, from fundamentals and math to LLMs, agents, computer vision, and real-world production systems. Read More   - [“Dr AI, am I healthy?” 59% of Brits rely on AI for self-diagnosis AI News](https://techjacksolutions.com/dr-ai-am-i-healthy-59-of-brits-rely-on-ai-for-self-diagnosis-ai-news/): “Dr AI, am I healthy?” 59% of Brits rely on AI for self-diagnosisAI News AI advancements are changing the way we look at health and deal with health-related issues. According to a new nationwide study by Confused.com Life Insurance, three in five Brits now use AI to self-diagnose health conditions. Through various searches, like side effects of medical conditions, treatment options, and symptom checks, as much as 11% of The post “Dr AI, am I healthy?” 59% of Brits rely on AI for self-diagnosis appeared first on AI News.  AI advancements are changing the way we look at health and deal with health-related issues. According to a new nationwide study by Confused.com Life Insurance, three in five Brits now use AI to self-diagnose health conditions. Through various searches, like side effects of medical conditions, treatment options, and symptom checks, as much as 11% of The post “Dr AI, am I healthy?” 59% of Brits rely on AI for self-diagnosis appeared first on AI News. Read More   - [How to Improve the Performance of Visual Anomaly Detection Models Towards Data Science](https://techjacksolutions.com/how-to-improve-the-performance-of-visual-anomaly-detection-models-towards-data-science/): How to Improve the Performance of Visual Anomaly Detection ModelsTowards Data Science Apply the best methods from academia to get the most out of practical applications The post How to Improve the Performance of Visual Anomaly Detection Models appeared first on Towards Data Science.  Apply the best methods from academia to get the most out of practical applications The post How to Improve the Performance of Visual Anomaly Detection Models appeared first on Towards Data Science. Read More   - [Retrieval for Time-Series: How Looking Back Improves Forecasts Towards Data Science](https://techjacksolutions.com/retrieval-for-time-series-how-looking-back-improves-forecasts-towards-data-science/): Retrieval for Time-Series: How Looking Back Improves ForecastsTowards Data Science Why Retrieval Helps in Time Series Forecasting We all know how it goes: Time-series data is tricky. Traditional forecasting models are unprepared for incidents like sudden market crashes, black swan events, or rare weather patterns. Even large fancy models like Chronos sometimes struggle because they haven’t dealt with that kind of pattern before. We can The post Retrieval for Time-Series: How Looking Back Improves Forecasts appeared first on Towards Data Science.  Why Retrieval Helps in Time Series Forecasting We all know how it goes: Time-series data is tricky. Traditional forecasting models are unprepared for incidents like sudden market crashes, black swan events, or rare weather patterns. Even large fancy models like Chronos sometimes struggle because they haven’t dealt with that kind of pattern before. We can The post Retrieval for Time-Series: How Looking Back Improves Forecasts appeared first on Towards Data Science. Read More   - [Powerful Local AI Automations with n8n, MCP and Ollama KDnuggets](https://techjacksolutions.com/powerful-local-ai-automations-with-n8n-mcp-and-ollama-kdnuggets/): Powerful Local AI Automations with n8n, MCP and OllamaKDnuggets The ultimate goal is to run these automations on a single workstation or small server, replacing fragile scripts and expensive API-based systems.  The ultimate goal is to run these automations on a single workstation or small server, replacing fragile scripts and expensive API-based systems. Read More   - [Stanford Researchers Build SleepFM Clinical: A Multimodal Sleep Foundation AI Model for 130+ Disease Prediction MarkTechPost](https://techjacksolutions.com/stanford-researchers-build-sleepfm-clinical-a-multimodal-sleep-foundation-ai-model-for-130-disease-prediction-marktechpost/): Stanford Researchers Build SleepFM Clinical: A Multimodal Sleep Foundation AI Model for 130+ Disease PredictionMarkTechPost A team of Stanford Medicine researchers have introduced SleepFM Clinical, a multimodal sleep foundation model that learns from clinical polysomnography and predicts long term disease risk from a single night of sleep. The research work is published in Nature Medicine and the team has released the clinical code as the open source sleepfm-clinical repository on The post Stanford Researchers Build SleepFM Clinical: A Multimodal Sleep Foundation AI Model for 130+ Disease Prediction appeared first on MarkTechPost.  A team of Stanford Medicine researchers have introduced SleepFM Clinical, a multimodal sleep foundation model that learns from clinical polysomnography and predicts long term disease risk from a single night of sleep. The research work is published in Nature Medicine and the team has released the clinical code as the open source sleepfm-clinical repository on The post Stanford Researchers Build SleepFM Clinical: A Multimodal Sleep Foundation AI Model for 130+ Disease Prediction appeared first on MarkTechPost. Read More   - [Speed meets scale: Load testing SageMakerAI endpoints with Observe.AI’s testing tool Artificial Intelligence](https://techjacksolutions.com/speed-meets-scale-load-testing-sagemakerai-endpoints-with-observe-ais-testing-tool-artificial-intelligence/): Speed meets scale: Load testing SageMakerAI endpoints with Observe.AI’s testing toolArtificial Intelligence Observe.ai developed the One Load Audit Framework (OLAF), which integrates with SageMaker to identify bottlenecks and performance issues in ML services, offering latency and throughput measurements under both static and dynamic data loads. In this blog post, you will learn how to use the OLAF utility to test and validate your SageMaker endpoint.  Observe.ai developed the One Load Audit Framework (OLAF), which integrates with SageMaker to identify bottlenecks and performance issues in ML services, offering latency and throughput measurements under both static and dynamic data loads. In this blog post, you will learn how to use the OLAF utility to test and validate your SageMaker endpoint. Read More   - [Detect and redact personally identifiable information using Amazon Bedrock Data Automation and Guardrails Artificial Intelligence](https://techjacksolutions.com/detect-and-redact-personally-identifiable-information-using-amazon-bedrock-data-automation-and-guardrails-artificial-intelligence/): Detect and redact personally identifiable information using Amazon Bedrock Data Automation and GuardrailsArtificial Intelligence This post shows an automated PII detection and redaction solution using Amazon Bedrock Data Automation and Amazon Bedrock Guardrails through a use case of processing text and image content in high volumes of incoming emails and attachments. The solution features a complete email processing workflow with a React-based user interface for authorized personnel to more securely manage and review redacted email communications and attachments. We walk through the step-by-step solution implementation procedures used to deploy this solution. Finally, we discuss the solution benefits, including operational efficiency, scalability, security and compliance, and adaptability.  This post shows an automated PII detection and redaction solution using Amazon Bedrock Data Automation and Amazon Bedrock Guardrails through a use case of processing text and image content in high volumes of incoming emails and attachments. The solution features a complete email processing workflow with a React-based user interface for authorized personnel to more securely manage and review redacted email communications and attachments. We walk through the step-by-step solution implementation procedures used to deploy this solution. Finally, we discuss the solution benefits, including operational efficiency, scalability, security and compliance, and adaptability. Read More   - [Beyond Prompting: The Power of Context Engineering Towards Data Science](https://techjacksolutions.com/beyond-prompting-the-power-of-context-engineering-towards-data-science/): Beyond Prompting: The Power of Context EngineeringTowards Data Science Using ACE to create self-improving LLM workflows and structured playbooks The post Beyond Prompting: The Power of Context Engineering appeared first on Towards Data Science.  Using ACE to create self-improving LLM workflows and structured playbooks The post Beyond Prompting: The Power of Context Engineering appeared first on Towards Data Science. Read More   - [Scaling medical content review at Flo Health using Amazon Bedrock (Part 1) Artificial Intelligence](https://techjacksolutions.com/scaling-medical-content-review-at-flo-health-using-amazon-bedrock-part-1-artificial-intelligence/): Scaling medical content review at Flo Health using Amazon Bedrock (Part 1)Artificial Intelligence This two-part series explores Flo Health's journey with generative AI for medical content verification. Part 1 examines our proof of concept (PoC), including the initial solution, capabilities, and early results. Part 2 covers focusing on scaling challenges and real-world implementation. Each article stands alone while collectively showing how AI transforms medical content management at scale.  This two-part series explores Flo Health's journey with generative AI for medical content verification. Part 1 examines our proof of concept (PoC), including the initial solution, capabilities, and early results. Part 2 covers focusing on scaling challenges and real-world implementation. Each article stands alone while collectively showing how AI transforms medical content management at scale. Read More   - [OpenAI Launches ChatGPT Health with Isolated, Encrypted Health Data Controls The Hacker Newsinfo@thehackernews.com (The Hacker News)](https://techjacksolutions.com/openai-launches-chatgpt-health-with-isolated-encrypted-health-data-controls-the-hacker-newsinfothehackernews-com-the-hacker-news/): Artificial intelligence (AI) company OpenAI on Wednesday announced the launch of ChatGPT Health, a dedicated space that allows users to have conversations with the chatbot about their health. To that end, the sandboxed experience offers users the optional ability to securely connect medical records and wellness apps, including Apple Health, Function, MyFitnessPal, Weight Watchers, AllTrails, Read More  - [CISA Flags Microsoft Office and HPE OneView Bugs as Actively Exploited The Hacker Newsinfo@thehackernews.com (The Hacker News)](https://techjacksolutions.com/cisa-flags-microsoft-office-and-hpe-oneview-bugs-as-actively-exploited-the-hacker-newsinfothehackernews-com-the-hacker-news/): The U.S. Cybersecurity and Infrastructure Security Agency (CISA) on Wednesday added two security flaws impacting Microsoft Office and Hewlett Packard Enterprise (HPE) OneView to its Known Exploited Vulnerabilities (KEV) catalog, citing evidence of active exploitation. The vulnerabilities are listed below – CVE-2009-0556 (CVSS score: 8.8) – A code injection vulnerability in Microsoft Office Read More  - [ALERT: Zero-shot LLM Jailbreak Detection via Internal Discrepancy Amplification AI updates on arXiv.org](https://techjacksolutions.com/alert-zero-shot-llm-jailbreak-detection-via-internal-discrepancy-amplification-ai-updates-on-arxiv-org/): ALERT: Zero-shot LLM Jailbreak Detection via Internal Discrepancy Amplificationcs.AI updates on arXiv.org arXiv:2601.03600v1 Announce Type: cross Abstract: Despite rich safety alignment strategies, large language models (LLMs) remain highly susceptible to jailbreak attacks, which compromise safety guardrails and pose serious security risks. Existing detection methods mainly detect jailbreak status relying on jailbreak templates present in the training data. However, few studies address the more realistic and challenging zero-shot jailbreak detection setting, where no jailbreak templates are available during training. This setting better reflects real-world scenarios where new attacks continually emerge and evolve. To address this challenge, we propose a layer-wise, module-wise, and token-wise amplification framework that progressively magnifies internal feature discrepancies between benign and jailbreak prompts. We uncover safety-relevant layers, identify specific modules that inherently encode zero-shot discriminative signals, and localize informative safety tokens. Building upon these insights, we introduce ALERT (Amplification-based Jailbreak Detector), an efficient and effective zero-shot jailbreak detector that introduces two independent yet complementary classifiers on amplified representations. Extensive experiments on three safety benchmarks demonstrate that ALERT achieves consistently strong zero-shot detection performance. Specifically, (i) across all datasets and attack strategies, ALERT reliably ranks among the top two methods, and (ii) it outperforms the second-best baseline by at least 10% in average Accuracy and F1-score, and sometimes by up to 40%.  arXiv:2601.03600v1 Announce Type: cross Abstract: Despite rich safety alignment strategies, large language models (LLMs) remain highly susceptible to jailbreak attacks, which compromise safety guardrails and pose serious security risks. Existing detection methods mainly detect jailbreak status relying on jailbreak templates present in the training data. However, few studies address the more realistic and challenging zero-shot jailbreak detection setting, where no jailbreak templates are available during training. This setting better reflects real-world scenarios where new attacks continually emerge and evolve. To address this challenge, we propose a layer-wise, module-wise, and token-wise amplification framework that progressively magnifies internal feature discrepancies between benign and jailbreak prompts. We uncover safety-relevant layers, identify specific modules that inherently encode zero-shot discriminative signals, and localize informative safety tokens. Building upon these insights, we introduce ALERT (Amplification-based Jailbreak Detector), an efficient and effective zero-shot jailbreak detector that introduces two independent yet complementary classifiers on amplified representations. Extensive experiments on three safety benchmarks demonstrate that ALERT achieves consistently strong zero-shot detection performance. Specifically, (i) across all datasets and attack strategies, ALERT reliably ranks among the top two methods, and (ii) it outperforms the second-best baseline by at least 10% in average Accuracy and F1-score, and sometimes by up to 40%. Read More   - [Can AI Chatbots Provide Coaching in Engineering? Beyond Information Processing Toward Mastery AI updates on arXiv.org](https://techjacksolutions.com/can-ai-chatbots-provide-coaching-in-engineering-beyond-information-processing-toward-mastery-ai-updates-on-arxiv-org/): Can AI Chatbots Provide Coaching in Engineering? Beyond Information Processing Toward Masterycs.AI updates on arXiv.org arXiv:2601.03693v1 Announce Type: cross Abstract: Engineering education faces a double disruption: traditional apprenticeship models that cultivated judgment and tacit skill are eroding, just as generative AI emerges as an informal coaching partner. This convergence rekindles long-standing questions in the philosophy of AI and cognition about the limits of computation, the nature of embodied rationality, and the distinction between information processing and wisdom. Building on this rich intellectual tradition, this paper examines whether AI chatbots can provide coaching that fosters mastery rather than merely delivering information. We synthesize critical perspectives from decades of scholarship on expertise, tacit knowledge, and human-machine interaction, situating them within the context of contemporary AI-driven education. Empirically, we report findings from a mixed-methods study (N = 75 students, N = 7 faculty) exploring the use of a coaching chatbot in engineering education. Results reveal a consistent boundary: participants accept AI for technical problem solving (convergent tasks; M = 3.84 on a 1-5 Likert scale) but remain skeptical of its capacity for moral, emotional, and contextual judgment (divergent tasks). Faculty express stronger concerns over risk (M = 4.71 vs. M = 4.14, p = 0.003), and privacy emerges as a key requirement, with 64-71 percent of participants demanding strict confidentiality. Our findings suggest that while generative AI can democratize access to cognitive and procedural support, it cannot replicate the embodied, value-laden dimensions of human mentorship. We propose a multiplex coaching framework that integrates human wisdom within expert-in-the-loop models, preserving the depth of apprenticeship while leveraging AI scalability to enrich the next generation of engineering education.  arXiv:2601.03693v1 Announce Type: cross Abstract: Engineering education faces a double disruption: traditional apprenticeship models that cultivated judgment and tacit skill are eroding, just as generative AI emerges as an informal coaching partner. This convergence rekindles long-standing questions in the philosophy of AI and cognition about the limits of computation, the nature of embodied rationality, and the distinction between information processing and wisdom. Building on this rich intellectual tradition, this paper examines whether AI chatbots can provide coaching that fosters mastery rather than merely delivering information. We synthesize critical perspectives from decades of scholarship on expertise, tacit knowledge, and human-machine interaction, situating them within the context of contemporary AI-driven education. Empirically, we report findings from a mixed-methods study (N = 75 students, N = 7 faculty) exploring the use of a coaching chatbot in engineering education. Results reveal a consistent boundary: participants accept AI for technical problem solving (convergent tasks; M = 3.84 on a 1-5 Likert scale) but remain skeptical of its capacity for moral, emotional, and contextual judgment (divergent tasks). Faculty express stronger concerns over risk (M = 4.71 vs. M = 4.14, p = 0.003), and privacy emerges as a key requirement, with 64-71 percent of participants demanding strict confidentiality. Our findings suggest that while generative AI can democratize access to cognitive and procedural support, it cannot replicate the embodied, value-laden dimensions of human mentorship. We propose a multiplex coaching framework that integrates human wisdom within expert-in-the-loop models, preserving the depth of apprenticeship while leveraging AI scalability to enrich the next generation of engineering education. Read More   - [CPGPrompt: Translating Clinical Guidelines into LLM-Executable Decision Support AI updates on arXiv.org](https://techjacksolutions.com/cpgprompt-translating-clinical-guidelines-into-llm-executable-decision-support-ai-updates-on-arxiv-org/): CPGPrompt: Translating Clinical Guidelines into LLM-Executable Decision Supportcs.AI updates on arXiv.org arXiv:2601.03475v1 Announce Type: new Abstract: Clinical practice guidelines (CPGs) provide evidence-based recommendations for patient care; however, integrating them into Artificial Intelligence (AI) remains challenging. Previous approaches, such as rule-based systems, face significant limitations, including poor interpretability, inconsistent adherence to guidelines, and narrow domain applicability. To address this, we develop and validate CPGPrompt, an auto-prompting system that converts narrative clinical guidelines into large language models (LLMs). Our framework translates CPGs into structured decision trees and utilizes an LLM to dynamically navigate them for patient case evaluation. Synthetic vignettes were generated across three domains (headache, lower back pain, and prostate cancer) and distributed into four categories to test different decision scenarios. System performance was assessed on both binary specialty-referral decisions and fine-grained pathway-classification tasks. The binary specialty referral classification achieved consistently strong performance across all domains (F1: 0.85-1.00), with high recall (1.00 $pm$ 0.00). In contrast, multi-class pathway assignment showed reduced performance, with domain-specific variations: headache (F1: 0.47), lower back pain (F1: 0.72), and prostate cancer (F1: 0.77). Domain-specific performance differences reflected the structure of each guideline. The headache guideline highlighted challenges with negation handling. The lower back pain guideline required temporal reasoning. In contrast, prostate cancer pathways benefited from quantifiable laboratory tests, resulting in more reliable decision-making.  arXiv:2601.03475v1 Announce Type: new Abstract: Clinical practice guidelines (CPGs) provide evidence-based recommendations for patient care; however, integrating them into Artificial Intelligence (AI) remains challenging. Previous approaches, such as rule-based systems, face significant limitations, including poor interpretability, inconsistent adherence to guidelines, and narrow domain applicability. To address this, we develop and validate CPGPrompt, an auto-prompting system that converts narrative clinical guidelines into large language models (LLMs). Our framework translates CPGs into structured decision trees and utilizes an LLM to dynamically navigate them for patient case evaluation. Synthetic vignettes were generated across three domains (headache, lower back pain, and prostate cancer) and distributed into four categories to test different decision scenarios. System performance was assessed on both binary specialty-referral decisions and fine-grained pathway-classification tasks. The binary specialty referral classification achieved consistently strong performance across all domains (F1: 0.85-1.00), with high recall (1.00 $pm$ 0.00). In contrast, multi-class pathway assignment showed reduced performance, with domain-specific variations: headache (F1: 0.47), lower back pain (F1: 0.72), and prostate cancer (F1: 0.77). Domain-specific performance differences reflected the structure of each guideline. The headache guideline highlighted challenges with negation handling. The lower back pain guideline required temporal reasoning. In contrast, prostate cancer pathways benefited from quantifiable laboratory tests, resulting in more reliable decision-making. Read More   - [Personalization of Large Foundation Models for Health Interventions AI updates on arXiv.org](https://techjacksolutions.com/personalization-of-large-foundation-models-for-health-interventions-ai-updates-on-arxiv-org/): Personalization of Large Foundation Models for Health Interventionscs.AI updates on arXiv.org arXiv:2601.03482v1 Announce Type: new Abstract: Large foundation models (LFMs) transform healthcare AI in prevention, diagnostics, and treatment. However, whether LFMs can provide truly personalized treatment recommendations remains an open question. Recent research has revealed multiple challenges for personalization, including the fundamental generalizability paradox: models achieving high accuracy in one clinical study perform at chance level in others, demonstrating that personalization and external validity exist in tension. This exemplifies broader contradictions in AI-driven healthcare: the privacy-performance paradox, scale-specificity paradox, and the automation-empathy paradox. As another challenge, the degree of causal understanding required for personalized recommendations, as opposed to mere predictive capacities of LFMs, remains an open question. N-of-1 trials -- crossover self-experiments and the gold standard for individual causal inference in personalized medicine -- resolve these tensions by providing within-person causal evidence while preserving privacy through local experimentation. Despite their impressive capabilities, this paper argues that LFMs cannot replace N-of-1 trials. We argue that LFMs and N-of-1 trials are complementary: LFMs excel at rapid hypothesis generation from population patterns using multimodal data, while N-of-1 trials excel at causal validation for a given individual. We propose a hybrid framework that combines the strengths of both to enable personalization and navigate the identified paradoxes: LFMs generate ranked intervention candidates with uncertainty estimates, which trigger subsequent N-of-1 trials. Clarifying the boundary between prediction and causation and explicitly addressing the paradoxical tensions are essential for responsible AI integration in personalized medicine.  arXiv:2601.03482v1 Announce Type: new Abstract: Large foundation models (LFMs) transform healthcare AI in prevention, diagnostics, and treatment. However, whether LFMs can provide truly personalized treatment recommendations remains an open question. Recent research has revealed multiple challenges for personalization, including the fundamental generalizability paradox: models achieving high accuracy in one clinical study perform at chance level in others, demonstrating that personalization and external validity exist in tension. This exemplifies broader contradictions in AI-driven healthcare: the privacy-performance paradox, scale-specificity paradox, and the automation-empathy paradox. As another challenge, the degree of causal understanding required for personalized recommendations, as opposed to mere predictive capacities of LFMs, remains an open question. N-of-1 trials -- crossover self-experiments and the gold standard for individual causal inference in personalized medicine -- resolve these tensions by providing within-person causal evidence while preserving privacy through local experimentation. Despite their impressive capabilities, this paper argues that LFMs cannot replace N-of-1 trials. We argue that LFMs and N-of-1 trials are complementary: LFMs excel at rapid hypothesis generation from population patterns using multimodal data, while N-of-1 trials excel at causal validation for a given individual. We propose a hybrid framework that combines the strengths of both to enable personalization and navigate the identified paradoxes: LFMs generate ranked intervention candidates with uncertainty estimates, which trigger subsequent N-of-1 trials. Clarifying the boundary between prediction and causation and explicitly addressing the paradoxical tensions are essential for responsible AI integration in personalized medicine. Read More   - [Analyzing Reasoning Consistency in Large Multimodal Models under Cross-Modal Conflicts AI updates on arXiv.org](https://techjacksolutions.com/analyzing-reasoning-consistency-in-large-multimodal-models-under-cross-modal-conflicts-ai-updates-on-arxiv-org/): Analyzing Reasoning Consistency in Large Multimodal Models under Cross-Modal Conflictscs.AI updates on arXiv.org arXiv:2601.04073v1 Announce Type: cross Abstract: Large Multimodal Models (LMMs) have demonstrated impressive capabilities in video reasoning via Chain-of-Thought (CoT). However, the robustness of their reasoning chains remains questionable. In this paper, we identify a critical failure mode termed textual inertia, where once a textual hallucination occurs in the thinking process, models tend to blindly adhere to the erroneous text while neglecting conflicting visual evidence. To systematically investigate this, we propose the LogicGraph Perturbation Protocol that structurally injects perturbations into the reasoning chains of diverse LMMs spanning both native reasoning architectures and prompt-driven paradigms to evaluate their self-reflection capabilities. The results reveal that models successfully self-correct in less than 10% of cases and predominantly succumb to blind textual error propagation. To mitigate this, we introduce Active Visual-Context Refinement, a training-free inference paradigm which orchestrates an active visual re-grounding mechanism to enforce fine-grained verification coupled with an adaptive context refinement strategy to summarize and denoise the reasoning history. Experiments demonstrate that our approach significantly stifles hallucination propagation and enhances reasoning robustness.  arXiv:2601.04073v1 Announce Type: cross Abstract: Large Multimodal Models (LMMs) have demonstrated impressive capabilities in video reasoning via Chain-of-Thought (CoT). However, the robustness of their reasoning chains remains questionable. In this paper, we identify a critical failure mode termed textual inertia, where once a textual hallucination occurs in the thinking process, models tend to blindly adhere to the erroneous text while neglecting conflicting visual evidence. To systematically investigate this, we propose the LogicGraph Perturbation Protocol that structurally injects perturbations into the reasoning chains of diverse LMMs spanning both native reasoning architectures and prompt-driven paradigms to evaluate their self-reflection capabilities. The results reveal that models successfully self-correct in less than 10% of cases and predominantly succumb to blind textual error propagation. To mitigate this, we introduce Active Visual-Context Refinement, a training-free inference paradigm which orchestrates an active visual re-grounding mechanism to enforce fine-grained verification coupled with an adaptive context refinement strategy to summarize and denoise the reasoning history. Experiments demonstrate that our approach significantly stifles hallucination propagation and enhances reasoning robustness. Read More   - [Faithful-First Reasoning, Planning, and Acting for Multimodal LLMs AI updates on arXiv.org](https://techjacksolutions.com/faithful-first-reasoning-planning-and-acting-for-multimodal-llmscs-ai-updates-on-arxiv-org/): Faithful-First Reasoning, Planning, and Acting for Multimodal LLMscs.AI updates on arXiv.org arXiv:2511.08409v3 Announce Type: replace Abstract: Multimodal Large Language Models (MLLMs) frequently suffer from unfaithfulness, generating reasoning chains that drift from visual evidence or contradict final predictions. We propose Faithful-First Reasoning, Planning, and Acting (RPA) framework in which FaithEvi provides step-wise and chain-level supervision by evaluating the faithfulness of intermediate reasoning, and FaithAct uses these signals to plan and execute faithfulness-aware actions during inference. Experiments across multiple multimodal reasoning benchmarks show that faithful-first RPA improves perceptual faithfulness by up to 24% over prompt-based and tool-augmented reasoning frameworks, without degrading task accuracy. Our analysis shows that treating faithfulness as a guiding principle perceptually faithful reasoning trajectories and mitigates hallucination behavior. This work thereby establishes a unified framework for both evaluating and enforcing faithfulness in multimodal reasoning. Code will be released upon acceptance.  arXiv:2511.08409v3 Announce Type: replace Abstract: Multimodal Large Language Models (MLLMs) frequently suffer from unfaithfulness, generating reasoning chains that drift from visual evidence or contradict final predictions. We propose Faithful-First Reasoning, Planning, and Acting (RPA) framework in which FaithEvi provides step-wise and chain-level supervision by evaluating the faithfulness of intermediate reasoning, and FaithAct uses these signals to plan and execute faithfulness-aware actions during inference. Experiments across multiple multimodal reasoning benchmarks show that faithful-first RPA improves perceptual faithfulness by up to 24% over prompt-based and tool-augmented reasoning frameworks, without degrading task accuracy. Our analysis shows that treating faithfulness as a guiding principle perceptually faithful reasoning trajectories and mitigates hallucination behavior. This work thereby establishes a unified framework for both evaluating and enforcing faithfulness in multimodal reasoning. Code will be released upon acceptance. Read More   - [Dissecting Physics Reasoning in Small Language Models: A Multi-Dimensional Analysis from an Educational Perspective AI updates on arXiv.org](https://techjacksolutions.com/dissecting-physics-reasoning-in-small-language-models-a-multi-dimensional-analysis-from-an-educational-perspective-ai-updates-on-arxiv-org/): Dissecting Physics Reasoning in Small Language Models: A Multi-Dimensional Analysis from an Educational Perspectivecs.AI updates on arXiv.org arXiv:2505.20707v2 Announce Type: replace-cross Abstract: Small Language Models (SLMs) offer privacy and efficiency for educational deployment, yet their utility depends on reliable multistep reasoning. Existing benchmarks often prioritize final answer accuracy, obscuring 'right answer, wrong procedure' failures that can reinforce student misconceptions. This work investigates SLM physics reasoning reliability, stage wise failure modes, and robustness under paired contextual variants. We introduce Physbench, comprising of 3,162 high school and AP level physics questions derived from OpenStax in a structured reference solution format with Bloom's Taxonomy annotations, plus 2,700 paired culturally contextualized variants. Using P-REFS, a stage wise evaluation rubric, we assess 10 SLMs across 58,000 responses. Results reveal substantial reliability gap: among final answer correct solutions, 75 to 98% contain at least one reasoning error. Failure modes shift with model capability; weaker models fail primarily at interpretation or modeling while stronger models often fail during execution. Paired contextual variations have minimal impact on top models but degrade the performance of mid-tier models. These findings demonstrate that safe educational AI requires evaluation paradigms that prioritize reasoning fidelity over final-answer correctness.  arXiv:2505.20707v2 Announce Type: replace-cross Abstract: Small Language Models (SLMs) offer privacy and efficiency for educational deployment, yet their utility depends on reliable multistep reasoning. Existing benchmarks often prioritize final answer accuracy, obscuring 'right answer, wrong procedure' failures that can reinforce student misconceptions. This work investigates SLM physics reasoning reliability, stage wise failure modes, and robustness under paired contextual variants. We introduce Physbench, comprising of 3,162 high school and AP level physics questions derived from OpenStax in a structured reference solution format with Bloom's Taxonomy annotations, plus 2,700 paired culturally contextualized variants. Using P-REFS, a stage wise evaluation rubric, we assess 10 SLMs across 58,000 responses. Results reveal substantial reliability gap: among final answer correct solutions, 75 to 98% contain at least one reasoning error. Failure modes shift with model capability; weaker models fail primarily at interpretation or modeling while stronger models often fail during execution. Paired contextual variations have minimal impact on top models but degrade the performance of mid-tier models. These findings demonstrate that safe educational AI requires evaluation paradigms that prioritize reasoning fidelity over final-answer correctness. Read More   - [ChatGPT is losing market share as Google Gemini gains ground BleepingComputerMayank Parmar](https://techjacksolutions.com/chatgpt-is-losing-market-share-as-google-gemini-gains-groundbleepingcomputermayank-parmar/): New data suggests that ChatGPT is losing its market share to Gemini on the web. It’s unclear if Gemini is also gaining ground in the mobile space. […] Read More  - [Webinar: Learn How AI-Powered Zero Trust Detects Attacks with No Files or Indicators The Hacker Newsinfo@thehackernews.com (The Hacker News)](https://techjacksolutions.com/webinar-learn-how-ai-powered-zero-trust-detects-attacks-with-no-files-or-indicators-the-hacker-newsinfothehackernews-com-the-hacker-news/): Security teams are still catching malware. The problem is what they’re not catching. More attacks today don’t arrive as files. They don’t drop binaries. They don’t trigger classic alerts. Instead, they run quietly through tools that already exist inside the environment — scripts, remote access, browsers, and developer workflows. That shift is creating a blind spot. Join us for a deep-dive Read More  - [Google Search AI hallucinations push Google to hire "AI Answers Quality" engineers BleepingComputerMayank Parmar](https://techjacksolutions.com/google-search-ai-hallucinations-push-google-to-hire-ai-answers-quality-engineers-bleepingcomputermayank-parmar/): AI, including AI Overviews on Google Search, can hallucinate and often make up stuff or offer contradicting answers when asked in two different ways. […] Read More  - [Critical n8n Vulnerability (CVSS 10.0) Allows Unauthenticated Attackers to Take Full Control The Hacker Newsinfo@thehackernews.com (The Hacker News)](https://techjacksolutions.com/critical-n8n-vulnerability-cvss-10-0-allows-unauthenticated-attackers-to-take-full-controlthe-hacker-newsinfothehackernews-com-the-hacker-news/): Cybersecurity researchers have disclosed details of yet another maximum-severity security flaw in n8n, a popular workflow automation platform, that allows an unauthenticated remote attacker to gain complete control over susceptible instances. The vulnerability, tracked as CVE-2026-21858 (CVSS score: 10.0), has been codenamed Ni8mare by Cyera Research Labs. Security researcher Dor Attias has been Read More  - [OpenAI says ChatGPT won't use your health information to train its models BleepingComputerMayank Parmar](https://techjacksolutions.com/openai-says-chatgpt-wont-use-your-health-information-to-train-its-models-bleepingcomputermayank-parmar/): OpenAI is rolling out ChatGPT Health, which is a dedicated space for health conversations. Amidst privacy concerns, OpenAI said it won’t use your health data. […] Read More  - [n8n Warns of CVSS 10.0 RCE Vulnerability Affecting Self-Hosted and Cloud Versions The Hacker Newsinfo@thehackernews.com (The Hacker News)](https://techjacksolutions.com/n8n-warns-of-cvss-10-0-rce-vulnerability-affecting-self-hosted-and-cloud-versions-the-hacker-newsinfothehackernews-com-the-hacker-news/): Open-source workflow automation platform n8n has warned of a maximum-severity security flaw that, if successfully exploited, could result in authenticated remote code execution (RCE). The vulnerability, which has been assigned the CVE identifier CVE-2026-21877, is rated 10.0 on the CVSS scoring system. “Under certain conditions, an authenticated user may be able to cause untrusted code to be Read More  - [Why Supply Chain is the Best Domain for Data Scientists in 2026 (And How to Learn It) Towards Data Science](https://techjacksolutions.com/why-supply-chain-is-the-best-domain-for-data-scientists-in-2026-and-how-to-learn-it-towards-data-science/): Why Supply Chain is the Best Domain for Data Scientists in 2026 (And How to Learn It)Towards Data Science My take after 10 years in Supply Chain on why this can be an excellent playground for data scientists who want to see their skills valued. The post Why Supply Chain is the Best Domain for Data Scientists in 2026 (And How to Learn It) appeared first on Towards Data Science.  My take after 10 years in Supply Chain on why this can be an excellent playground for data scientists who want to see their skills valued. The post Why Supply Chain is the Best Domain for Data Scientists in 2026 (And How to Learn It) appeared first on Towards Data Science. Read More   - [TII Abu-Dhabi Released Falcon H1R-7B: A New Reasoning Model Outperforming Others in Math and Coding with only 7B Params with 256k Context Window MarkTechPost](https://techjacksolutions.com/tii-abu-dhabi-released-falcon-h1r-7b-a-new-reasoning-model-outperforming-others-in-math-and-coding-with-only-7b-params-with-256k-context-window-marktechpost/): TII Abu-Dhabi Released Falcon H1R-7B: A New Reasoning Model Outperforming Others in Math and Coding with only 7B Params with 256k Context WindowMarkTechPost Technology Innovation Institute (TII), Abu Dhabi, has released Falcon-H1R-7B, a 7B parameter reasoning specialized model that matches or exceeds many 14B to 47B reasoning models in math, code and general benchmarks, while staying compact and efficient. It builds on Falcon H1 7B Base and is available on Hugging Face under the Falcon-H1R collection. Falcon-H1R-7B is The post TII Abu-Dhabi Released Falcon H1R-7B: A New Reasoning Model Outperforming Others in Math and Coding with only 7B Params with 256k Context Window appeared first on MarkTechPost.  Technology Innovation Institute (TII), Abu Dhabi, has released Falcon-H1R-7B, a 7B parameter reasoning specialized model that matches or exceeds many 14B to 47B reasoning models in math, code and general benchmarks, while staying compact and efficient. It builds on Falcon H1 7B Base and is available on Hugging Face under the Falcon-H1R collection. Falcon-H1R-7B is The post TII Abu-Dhabi Released Falcon H1R-7B: A New Reasoning Model Outperforming Others in Math and Coding with only 7B Params with 256k Context Window appeared first on MarkTechPost. Read More   - [Top 7 n8n Workflow Templates for Data Science KDnuggets](https://techjacksolutions.com/top-7-n8n-workflow-templates-for-data-science-kdnuggets/): Top 7 n8n Workflow Templates for Data ScienceKDnuggets A list of ready to use n8n workflow templates that help data scientists quickly analyze data, extract and transform it, and build reliable knowledge bases.  A list of ready to use n8n workflow templates that help data scientists quickly analyze data, extract and transform it, and build reliable knowledge bases. Read More   - [Probabilistic Multi-Variant Reasoning: Turning Fluent LLM Answers Into Weighted Options Towards Data Science](https://techjacksolutions.com/probabilistic-multi-variant-reasoning-turning-fluent-llm-answers-into-weighted-options-towards-data-science/): Probabilistic Multi-Variant Reasoning: Turning Fluent LLM Answers Into Weighted OptionsTowards Data Science Human-guided AI collaboration The post Probabilistic Multi-Variant Reasoning: Turning Fluent LLM Answers Into Weighted Options appeared first on Towards Data Science.  Human-guided AI collaboration The post Probabilistic Multi-Variant Reasoning: Turning Fluent LLM Answers Into Weighted Options appeared first on Towards Data Science. Read More   - [Data Scientist vs AI Engineer: Which Career Should You Choose in 2026? KDnuggets](https://techjacksolutions.com/data-scientist-vs-ai-engineer-which-career-should-you-choose-in-2026-kdnuggets/): Data Scientist vs AI Engineer: Which Career Should You Choose in 2026?KDnuggets Although data science and AI engineering share tools and terminology, they are not interchangeable careers. This article explains how the work, goals, and impact of each role differ so you can choose the career path that fits you.  Although data science and AI engineering share tools and terminology, they are not interchangeable careers. This article explains how the work, goals, and impact of each role differ so you can choose the career path that fits you. Read More   - [Optimism for AI-powered productivity: Deloitte AI News](https://techjacksolutions.com/optimism-for-ai-powered-productivity-deloitte-ai-news/): Optimism for AI-powered productivity: DeloitteAI News Deloitte’s latest UK CFO Survey presents an improving outlook for large UK businesses, with technology investment – particularly in AI – emerging as a dominant strategy. The survey offers the signal that while macroeconomic and geopolitical risks remain elevated, boards are converging increasingly on digital ability as a primary route to productivity and medium-term growth. The post Optimism for AI-powered productivity: Deloitte appeared first on AI News.  Deloitte’s latest UK CFO Survey presents an improving outlook for large UK businesses, with technology investment – particularly in AI – emerging as a dominant strategy. The survey offers the signal that while macroeconomic and geopolitical risks remain elevated, boards are converging increasingly on digital ability as a primary route to productivity and medium-term growth. The post Optimism for AI-powered productivity: Deloitte appeared first on AI News. Read More   - [HNSW at Scale: Why Your RAG System Gets Worse as the Vector Database Grows Towards Data Science](https://techjacksolutions.com/hnsw-at-scale-why-your-rag-system-gets-worse-as-the-vector-database-grows-towards-data-science/): HNSW at Scale: Why Your RAG System Gets Worse as the Vector Database GrowsTowards Data Science How approximate vector search silently degrades Recall—and what to do about It The post HNSW at Scale: Why Your RAG System Gets Worse as the Vector Database Grows appeared first on Towards Data Science.  How approximate vector search silently degrades Recall—and what to do about It The post HNSW at Scale: Why Your RAG System Gets Worse as the Vector Database Grows appeared first on Towards Data Science. Read More   - [Vibe Code Reality Check: What You Can Actually Build with Only AI KDnuggets](https://techjacksolutions.com/vibe-code-reality-check-what-you-can-actually-build-with-only-ai-kdnuggets/): Vibe Code Reality Check: What You Can Actually Build with Only AIKDnuggets This is an "expectations vs reality" approach to demystify, based on research of real success and failure stories, what are the capabilities and limits of vibe coding.  This is an "expectations vs reality" approach to demystify, based on research of real success and failure stories, what are the capabilities and limits of vibe coding. Read More   - [Agentic AI scaling requires new memory architecture AI News](https://techjacksolutions.com/agentic-ai-scaling-requires-new-memory-architecture-ai-news/): Agentic AI scaling requires new memory architectureAI News Agentic AI represents a distinct evolution from stateless chatbots toward complex workflows, and scaling it requires new memory architecture. As foundation models scale toward trillions of parameters and context windows reach millions of tokens, the computational cost of remembering history is rising faster than the ability to process it. Organisations deploying these systems now face The post Agentic AI scaling requires new memory architecture appeared first on AI News.  Agentic AI represents a distinct evolution from stateless chatbots toward complex workflows, and scaling it requires new memory architecture. As foundation models scale toward trillions of parameters and context windows reach millions of tokens, the computational cost of remembering history is rising faster than the ability to process it. Organisations deploying these systems now face The post Agentic AI scaling requires new memory architecture appeared first on AI News. Read More   - [New GoBruteforcer attack wave targets crypto, blockchain projects BleepingComputerBill Toulas](https://techjacksolutions.com/new-gobruteforcer-attack-wave-targets-crypto-blockchain-projects-bleepingcomputerbill-toulas/): A new wave of GoBruteforcer botnet malware attacks is targeting databases of cryptocurrency and blockchain projects on exposed servers believed to be configured using AI-generated examples. […] Read More  - [On the Representation of Pairwise Causal Background Knowledge and Its Applications in Causal Inference AI updates on arXiv.org](https://techjacksolutions.com/on-the-representation-of-pairwise-causal-background-knowledge-and-its-applications-in-causal-inference-ai-updates-on-arxiv-org/): On the Representation of Pairwise Causal Background Knowledge and Its Applications in Causal Inferencecs.AI updates on arXiv.org arXiv:2207.05067v2 Announce Type: replace Abstract: Pairwise causal background knowledge about the existence or absence of causal edges and paths is frequently encountered in observational studies. Such constraints allow the shared directed and undirected edges in the constrained subclass of Markov equivalent DAGs to be represented as a causal maximally partially directed acyclic graph (MPDAG). In this paper, we first provide a sound and complete graphical characterization of causal MPDAGs and introduce a minimal representation of a causal MPDAG. Then, we give a unified representation for three types of pairwise causal background knowledge, including direct, ancestral and non-ancestral causal knowledge, by introducing a novel concept called direct causal clause (DCC). Using DCCs, we study the consistency and equivalence of pairwise causal background knowledge and show that any pairwise causal background knowledge set can be uniquely and equivalently decomposed into the causal MPDAG representing the refined Markov equivalence class and a minimal residual set of DCCs. Polynomial-time algorithms are also provided for checking consistency and equivalence, as well as for finding the decomposed MPDAG and the residual DCCs. Finally, with pairwise causal background knowledge, we prove a sufficient and necessary condition to identify causal effects and surprisingly find that the identifiability of causal effects only depends on the decomposed MPDAG. We also develop a local IDA-type algorithm to estimate the possible values of an unidentifiable effect. Simulations suggest that pairwise causal background knowledge can significantly improve the identifiability of causal effects.  arXiv:2207.05067v2 Announce Type: replace Abstract: Pairwise causal background knowledge about the existence or absence of causal edges and paths is frequently encountered in observational studies. Such constraints allow the shared directed and undirected edges in the constrained subclass of Markov equivalent DAGs to be represented as a causal maximally partially directed acyclic graph (MPDAG). In this paper, we first provide a sound and complete graphical characterization of causal MPDAGs and introduce a minimal representation of a causal MPDAG. Then, we give a unified representation for three types of pairwise causal background knowledge, including direct, ancestral and non-ancestral causal knowledge, by introducing a novel concept called direct causal clause (DCC). Using DCCs, we study the consistency and equivalence of pairwise causal background knowledge and show that any pairwise causal background knowledge set can be uniquely and equivalently decomposed into the causal MPDAG representing the refined Markov equivalence class and a minimal residual set of DCCs. Polynomial-time algorithms are also provided for checking consistency and equivalence, as well as for finding the decomposed MPDAG and the residual DCCs. Finally, with pairwise causal background knowledge, we prove a sufficient and necessary condition to identify causal effects and surprisingly find that the identifiability of causal effects only depends on the decomposed MPDAG. We also develop a local IDA-type algorithm to estimate the possible values of an unidentifiable effect. Simulations suggest that pairwise causal background knowledge can significantly improve the identifiability of causal effects. Read More   - [Multimodal Fact-Checking: An Agent-based Approach AI updates on arXiv.org](https://techjacksolutions.com/multimodal-fact-checking-an-agent-based-approach-ai-updates-on-arxiv-org/): Multimodal Fact-Checking: An Agent-based Approachcs.AI updates on arXiv.org arXiv:2512.22933v3 Announce Type: replace Abstract: The rapid spread of multimodal misinformation poses a growing challenge for automated fact-checking systems. Existing approaches, including large vision language models (LVLMs) and deep multimodal fusion methods, often fall short due to limited reasoning and shallow evidence utilization. A key bottleneck is the lack of dedicated datasets that provide complete real-world multimodal misinformation instances accompanied by annotated reasoning processes and verifiable evidence. To address this limitation, we introduce RW-Post, a high-quality and explainable dataset for real-world multimodal fact-checking. RW-Post aligns real-world multimodal claims with their original social media posts, preserving the rich contextual information in which the claims are made. In addition, the dataset includes detailed reasoning and explicitly linked evidence, which are derived from human written fact-checking articles via a large language model assisted extraction pipeline, enabling comprehensive verification and explanation. Building upon RW-Post, we propose AgentFact, an agent-based multimodal fact-checking framework designed to emulate the human verification workflow. AgentFact consists of five specialized agents that collaboratively handle key fact-checking subtasks, including strategy planning, high-quality evidence retrieval, visual analysis, reasoning, and explanation generation. These agents are orchestrated through an iterative workflow that alternates between evidence searching and task-aware evidence filtering and reasoning, facilitating strategic decision-making and systematic evidence analysis. Extensive experimental results demonstrate that the synergy between RW-Post and AgentFact substantially improves both the accuracy and interpretability of multimodal fact-checking.  arXiv:2512.22933v3 Announce Type: replace Abstract: The rapid spread of multimodal misinformation poses a growing challenge for automated fact-checking systems. Existing approaches, including large vision language models (LVLMs) and deep multimodal fusion methods, often fall short due to limited reasoning and shallow evidence utilization. A key bottleneck is the lack of dedicated datasets that provide complete real-world multimodal misinformation instances accompanied by annotated reasoning processes and verifiable evidence. To address this limitation, we introduce RW-Post, a high-quality and explainable dataset for real-world multimodal fact-checking. RW-Post aligns real-world multimodal claims with their original social media posts, preserving the rich contextual information in which the claims are made. In addition, the dataset includes detailed reasoning and explicitly linked evidence, which are derived from human written fact-checking articles via a large language model assisted extraction pipeline, enabling comprehensive verification and explanation. Building upon RW-Post, we propose AgentFact, an agent-based multimodal fact-checking framework designed to emulate the human verification workflow. AgentFact consists of five specialized agents that collaboratively handle key fact-checking subtasks, including strategy planning, high-quality evidence retrieval, visual analysis, reasoning, and explanation generation. These agents are orchestrated through an iterative workflow that alternates between evidence searching and task-aware evidence filtering and reasoning, facilitating strategic decision-making and systematic evidence analysis. Extensive experimental results demonstrate that the synergy between RW-Post and AgentFact substantially improves both the accuracy and interpretability of multimodal fact-checking. Read More   - [How to make Medical AI Systems safer? Simulating Vulnerabilities, and Threats in Multimodal Medical RAG System AI updates on arXiv.org](https://techjacksolutions.com/how-to-make-medical-ai-systems-safer-simulating-vulnerabilities-and-threats-in-multimodal-medical-rag-system-ai-updates-on-arxiv-org/): How to make Medical AI Systems safer? Simulating Vulnerabilities, and Threats in Multimodal Medical RAG Systemcs.AI updates on arXiv.org arXiv:2508.17215v2 Announce Type: replace-cross Abstract: Large Vision-Language Models (LVLMs) augmented with Retrieval-Augmented Generation (RAG) are increasingly employed in medical AI to enhance factual grounding through external clinical image-text retrieval. However, this reliance creates a significant attack surface. We propose MedThreatRAG, a novel multimodal poisoning framework that systematically probes vulnerabilities in medical RAG systems by injecting adversarial image-text pairs. A key innovation of our approach is the construction of a simulated semi-open attack environment, mimicking real-world medical systems that permit periodic knowledge base updates via user or pipeline contributions. Within this setting, we introduce and emphasize Cross-Modal Conflict Injection (CMCI), which embeds subtle semantic contradictions between medical images and their paired reports. These mismatches degrade retrieval and generation by disrupting cross-modal alignment while remaining sufficiently plausible to evade conventional filters. While basic textual and visual attacks are included for completeness, CMCI demonstrates the most severe degradation. Evaluations on IU-Xray and MIMIC-CXR QA tasks show that MedThreatRAG reduces answer F1 scores by up to 27.66% and lowers LLaVA-Med-1.5 F1 rates to as low as 51.36%. Our findings expose fundamental security gaps in clinical RAG systems and highlight the urgent need for threat-aware design and robust multimodal consistency checks. Finally, we conclude with a concise set of guidelines to inform the safe development of future multimodal medical RAG systems.  arXiv:2508.17215v2 Announce Type: replace-cross Abstract: Large Vision-Language Models (LVLMs) augmented with Retrieval-Augmented Generation (RAG) are increasingly employed in medical AI to enhance factual grounding through external clinical image-text retrieval. However, this reliance creates a significant attack surface. We propose MedThreatRAG, a novel multimodal poisoning framework that systematically probes vulnerabilities in medical RAG systems by injecting adversarial image-text pairs. A key innovation of our approach is the construction of a simulated semi-open attack environment, mimicking real-world medical systems that permit periodic knowledge base updates via user or pipeline contributions. Within this setting, we introduce and emphasize Cross-Modal Conflict Injection (CMCI), which embeds subtle semantic contradictions between medical images and their paired reports. These mismatches degrade retrieval and generation by disrupting cross-modal alignment while remaining sufficiently plausible to evade conventional filters. While basic textual and visual attacks are included for completeness, CMCI demonstrates the most severe degradation. Evaluations on IU-Xray and MIMIC-CXR QA tasks show that MedThreatRAG reduces answer F1 scores by up to 27.66% and lowers LLaVA-Med-1.5 F1 rates to as low as 51.36%. Our findings expose fundamental security gaps in clinical RAG systems and highlight the urgent need for threat-aware design and robust multimodal consistency checks. Finally, we conclude with a concise set of guidelines to inform the safe development of future multimodal medical RAG systems. Read More   - [A data-driven framework for team selection in Fantasy Premier League AI updates on arXiv.org](https://techjacksolutions.com/a-data-driven-framework-for-team-selection-in-fantasy-premier-league-ai-updates-on-arxiv-org/): A data-driven framework for team selection in Fantasy Premier Leaguecs.AI updates on arXiv.org arXiv:2505.02170v3 Announce Type: replace-cross Abstract: Fantasy football is a billion-dollar industry with millions of participants. Under a fixed budget, managers select squads to maximize future Fantasy Premier League (FPL) points. This study formulates lineup selection as data-driven optimization and develops deterministic and robust mixed-integer linear programs that choose the starting eleven, bench, and captain under budget, formation, and club-quota constraints (maximum three players per club). The objective is parameterized by a hybrid scoring metric that combines realized FPL points with predictions from a linear regression model trained on match-performance features identified using exploratory data analysis techniques. The study benchmarks alternative objectives and cost estimators, including simple and recency-weighted averages, exponential smoothing, autoregressive integrated moving average (ARIMA), and Monte Carlo simulation. Experiments on the 2023/24 Premier League season show that ARIMA with a constrained budget and a rolling window yields the most consistent out-of-sample performance; weighted averages and Monte Carlo are also competitive. Robust variants and hybrid scoring metrics improve some objectives but are not uniformly superior. The framework provides transparent decision support for fantasy roster construction and extends to FPL chips, multi-week rolling-horizon transfer planning, and week-by-week dynamic captaincy.  arXiv:2505.02170v3 Announce Type: replace-cross Abstract: Fantasy football is a billion-dollar industry with millions of participants. Under a fixed budget, managers select squads to maximize future Fantasy Premier League (FPL) points. This study formulates lineup selection as data-driven optimization and develops deterministic and robust mixed-integer linear programs that choose the starting eleven, bench, and captain under budget, formation, and club-quota constraints (maximum three players per club). The objective is parameterized by a hybrid scoring metric that combines realized FPL points with predictions from a linear regression model trained on match-performance features identified using exploratory data analysis techniques. The study benchmarks alternative objectives and cost estimators, including simple and recency-weighted averages, exponential smoothing, autoregressive integrated moving average (ARIMA), and Monte Carlo simulation. Experiments on the 2023/24 Premier League season show that ARIMA with a constrained budget and a rolling window yields the most consistent out-of-sample performance; weighted averages and Monte Carlo are also competitive. Robust variants and hybrid scoring metrics improve some objectives but are not uniformly superior. The framework provides transparent decision support for fantasy roster construction and extends to FPL chips, multi-week rolling-horizon transfer planning, and week-by-week dynamic captaincy. Read More   - [Dynamic Large Concept Models: Latent Reasoning in an Adaptive Semantic Space AI updates on arXiv.org](https://techjacksolutions.com/dynamic-large-concept-models-latent-reasoning-in-an-adaptive-semantic-space-ai-updates-on-arxiv-org/): Dynamic Large Concept Models: Latent Reasoning in an Adaptive Semantic Spacecs.AI updates on arXiv.org arXiv:2512.24617v2 Announce Type: replace-cross Abstract: Large Language Models (LLMs) apply uniform computation to all tokens, despite language exhibiting highly non-uniform information density. This token-uniform regime wastes capacity on locally predictable spans while under-allocating computation to semantically critical transitions. We propose $textbf{Dynamic Large Concept Models (DLCM)}$, a hierarchical language modeling framework that learns semantic boundaries from latent representations and shifts computation from tokens to a compressed concept space where reasoning is more efficient. DLCM discovers variable-length concepts end-to-end without relying on predefined linguistic units. Hierarchical compression fundamentally changes scaling behavior. We introduce the first $textbf{compression-aware scaling law}$, which disentangles token-level capacity, concept-level reasoning capacity, and compression ratio, enabling principled compute allocation under fixed FLOPs. To stably train this heterogeneous architecture, we further develop a $textbf{decoupled $mu$P parametrization}$ that supports zero-shot hyperparameter transfer across widths and compression regimes. At a practical setting ($R=4$, corresponding to an average of four tokens per concept), DLCM reallocates roughly one-third of inference compute into a higher-capacity reasoning backbone, achieving a $textbf{+2.69$%$ average improvement}$ across 12 zero-shot benchmarks under matched inference FLOPs.  arXiv:2512.24617v2 Announce Type: replace-cross Abstract: Large Language Models (LLMs) apply uniform computation to all tokens, despite language exhibiting highly non-uniform information density. This token-uniform regime wastes capacity on locally predictable spans while under-allocating computation to semantically critical transitions. We propose $textbf{Dynamic Large Concept Models (DLCM)}$, a hierarchical language modeling framework that learns semantic boundaries from latent representations and shifts computation from tokens to a compressed concept space where reasoning is more efficient. DLCM discovers variable-length concepts end-to-end without relying on predefined linguistic units. Hierarchical compression fundamentally changes scaling behavior. We introduce the first $textbf{compression-aware scaling law}$, which disentangles token-level capacity, concept-level reasoning capacity, and compression ratio, enabling principled compute allocation under fixed FLOPs. To stably train this heterogeneous architecture, we further develop a $textbf{decoupled $mu$P parametrization}$ that supports zero-shot hyperparameter transfer across widths and compression regimes. At a practical setting ($R=4$, corresponding to an average of four tokens per concept), DLCM reallocates roughly one-third of inference compute into a higher-capacity reasoning backbone, achieving a $textbf{+2.69$%$ average improvement}$ across 12 zero-shot benchmarks under matched inference FLOPs. Read More   - [Routing by Analogy: kNN-Augmented Expert Assignment for Mixture-of-Experts AI updates on arXiv.org](https://techjacksolutions.com/routing-by-analogy-knn-augmented-expert-assignment-for-mixture-of-experts-ai-updates-on-arxiv-org/): Routing by Analogy: kNN-Augmented Expert Assignment for Mixture-of-Expertscs.AI updates on arXiv.org arXiv:2601.02144v1 Announce Type: cross Abstract: Mixture-of-Experts (MoE) architectures scale large language models efficiently by employing a parametric "router" to dispatch tokens to a sparse subset of experts. Typically, this router is trained once and then frozen, rendering routing decisions brittle under distribution shifts. We address this limitation by introducing kNN-MoE, a retrieval-augmented routing framework that reuses optimal expert assignments from a memory of similar past cases. This memory is constructed offline by directly optimizing token-wise routing logits to maximize the likelihood on a reference set. Crucially, we use the aggregate similarity of retrieved neighbors as a confidence-driven mixing coefficient, thus allowing the method to fall back to the frozen router when no relevant cases are found. Experiments show kNN-MoE outperforms zero-shot baselines and rivals computationally expensive supervised fine-tuning.  arXiv:2601.02144v1 Announce Type: cross Abstract: Mixture-of-Experts (MoE) architectures scale large language models efficiently by employing a parametric "router" to dispatch tokens to a sparse subset of experts. Typically, this router is trained once and then frozen, rendering routing decisions brittle under distribution shifts. We address this limitation by introducing kNN-MoE, a retrieval-augmented routing framework that reuses optimal expert assignments from a memory of similar past cases. This memory is constructed offline by directly optimizing token-wise routing logits to maximize the likelihood on a reference set. Crucially, we use the aggregate similarity of retrieved neighbors as a confidence-driven mixing coefficient, thus allowing the method to fall back to the frozen router when no relevant cases are found. Experiments show kNN-MoE outperforms zero-shot baselines and rivals computationally expensive supervised fine-tuning. Read More   - [Sedgwick confirms breach at government contractor subsidiary BleepingComputerSergiu Gatlan](https://techjacksolutions.com/sedgwick-confirms-breach-at-government-contractor-subsidiary-bleepingcomputersergiu-gatlan/): Claims administration and risk management company Sedgwick has confirmed that its federal contractor subsidiary, Sedgwick Government Solutions, was the victim of a security breach. […] Read More  - [Unpatched Firmware Flaw Exposes TOTOLINK EX200 to Full Remote Device Takeover The Hacker Newsinfo@thehackernews.com (The Hacker News)](https://techjacksolutions.com/unpatched-firmware-flaw-exposes-totolink-ex200-to-full-remote-device-takeover-the-hacker-newsinfothehackernews-com-the-hacker-news/): The CERT Coordination Center (CERT/CC) has disclosed details of an unpatched security flaw impacting TOTOLINK EX200 wireless range extender that could allow a remote authenticated attacker to gain full control of the device. The flaw, CVE-2025-65606 (CVSS score: N/A), has been characterized as a flaw in the firmware-upload error-handling logic, which could cause the device to inadvertently start Read More  - [Measuring What Matters with NeMo Agent Toolkit Towards Data Science](https://techjacksolutions.com/measuring-what-matters-with-nemo-agent-toolkit-towards-data-science/): Measuring What Matters with NeMo Agent ToolkitTowards Data Science A practical guide to observability, evaluations, and model comparisons The post Measuring What Matters with NeMo Agent Toolkit appeared first on Towards Data Science.  A practical guide to observability, evaluations, and model comparisons The post Measuring What Matters with NeMo Agent Toolkit appeared first on Towards Data Science. Read More   - [Liquid AI Releases LFM2.5: A Compact AI Model Family For Real On Device Agents MarkTechPost](https://techjacksolutions.com/liquid-ai-releases-lfm2-5-a-compact-ai-model-family-for-real-on-device-agents-marktechpost/): Liquid AI Releases LFM2.5: A Compact AI Model Family For Real On Device AgentsMarkTechPost Liquid AI has introduced LFM2.5, a new generation of small foundation models built on the LFM2 architecture and focused at on device and edge deployments. The model family includes LFM2.5-1.2B-Base and LFM2.5-1.2B-Instruct and extends to Japanese, vision language, and audio language variants. It is released as open weights on Hugging Face and exposed through the The post Liquid AI Releases LFM2.5: A Compact AI Model Family For Real On Device Agents appeared first on MarkTechPost.  Liquid AI has introduced LFM2.5, a new generation of small foundation models built on the LFM2 architecture and focused at on device and edge deployments. The model family includes LFM2.5-1.2B-Base and LFM2.5-1.2B-Instruct and extends to Japanese, vision language, and audio language variants. It is released as open weights on Hugging Face and exposed through the The post Liquid AI Releases LFM2.5: A Compact AI Model Family For Real On Device Agents appeared first on MarkTechPost. Read More   - [The 10 AI Developments That Defined 2025 KDnuggets](https://techjacksolutions.com/the-10-ai-developments-that-defined-2025-kdnuggets/): The 10 AI Developments That Defined 2025KDnuggets In this article, we retroactively analyze what I would consider the ten most consequential, broadly impactful AI storylines of 2025, and gain insight into where the field is going in 2026.  In this article, we retroactively analyze what I would consider the ten most consequential, broadly impactful AI storylines of 2025, and gain insight into where the field is going in 2026. Read More   - [How to Design an Agentic AI Architecture with LangGraph and OpenAI Using Adaptive Deliberation, Memory Graphs, and Reflexion Loops MarkTechPost](https://techjacksolutions.com/how-to-design-an-agentic-ai-architecture-with-langgraph-and-openai-using-adaptive-deliberation-memory-graphs-and-reflexion-loops-marktechpost/): How to Design an Agentic AI Architecture with LangGraph and OpenAI Using Adaptive Deliberation, Memory Graphs, and Reflexion LoopsMarkTechPost In this tutorial, we build a genuinely advanced Agentic AI system using LangGraph and OpenAI models by going beyond simple planner, executor loops. We implement adaptive deliberation, where the agent dynamically decides between fast and deep reasoning; a Zettelkasten-style agentic memory graph that stores atomic knowledge and automatically links related experiences; and a governed tool-use The post How to Design an Agentic AI Architecture with LangGraph and OpenAI Using Adaptive Deliberation, Memory Graphs, and Reflexion Loops appeared first on MarkTechPost.  In this tutorial, we build a genuinely advanced Agentic AI system using LangGraph and OpenAI models by going beyond simple planner, executor loops. We implement adaptive deliberation, where the agent dynamically decides between fast and deep reasoning; a Zettelkasten-style agentic memory graph that stores atomic knowledge and automatically links related experiences; and a governed tool-use The post How to Design an Agentic AI Architecture with LangGraph and OpenAI Using Adaptive Deliberation, Memory Graphs, and Reflexion Loops appeared first on MarkTechPost. Read More   - [New D-Link flaw in legacy DSL routers actively exploited in attacks BleepingComputerBill Toulas](https://techjacksolutions.com/new-d-link-flaw-in-legacy-dsl-routers-actively-exploited-in-attacks-bleepingcomputerbill-toulas/): Threat actors are exploiting a recently discovered command injection vulnerability that affects multiple D-Link DSL gateway routers that went out of support years ago. […] Read More  - [Microsoft cancels plans to rate limit Exchange Online bulk emails BleepingComputerSergiu Gatlan](https://techjacksolutions.com/microsoft-cancels-plans-to-rate-limit-exchange-online-bulk-emails-bleepingcomputersergiu-gatlan/): Microsoft announced today that it has canceled plans to impose a daily limit of 2,000 external recipients on Exchange Online bulk email senders. […] Read More  - [OpenAI is rolling out GPT-5.2 “Codex-Max” for some users BleepingComputerMayank Parmar](https://techjacksolutions.com/openai-is-rolling-out-gpt-5-2-codex-max-for-some-users-bleepingcomputermayank-parmar/): OpenAI is testing a new model for Codex, and it could be the company’s best coding model yet. […] Read More  - [ClickFix Campaign Serves Up Fake Blue Screen of Deathdarkreading Elizabeth Montalbano, Contributing Writer](https://techjacksolutions.com/clickfix-campaign-serves-up-fake-blue-screen-of-deathdarkreading-elizabeth-montalbano-contributing-writer/): Threat actors are using the social engineering technique and a legitimate Microsoft tool to deploy the DCRat remote access Trojan against targets in the hospitality sector. Read More  - [Taiwan says China's attacks on its energy sector increased tenfold BleepingComputerBill Toulas](https://techjacksolutions.com/taiwan-says-chinas-attacks-on-its-energy-sector-increased-tenfold-bleepingcomputerbill-toulas/): The National Security Bureau in Taiwan says that China’s attacks on the country’s energy sector increased tenfold in 2025 compared to the previous year. […] Read More  - [Two Chrome Extensions Caught Stealing ChatGPT and DeepSeek Chats from 900,000 Users The Hacker Newsinfo@thehackernews.com (The Hacker News)](https://techjacksolutions.com/two-chrome-extensions-caught-stealing-chatgpt-and-deepseek-chats-from-900000-users-the-hacker-newsinfothehackernews-com-the-hacker-news/): Cybersecurity researchers have discovered two new malicious extensions on the Chrome Web Store that are designed to exfiltrate OpenAI ChatGPT and DeepSeek conversations alongside browsing data to servers under the attackers’ control. The names of the extensions, which collectively have over 900,000 users, are below – Chat GPT for Chrome with GPT-5, Claude Sonnet & DeepSeek AI (ID: Read More  - [Kimwolf Android botnet abuses residential proxies to infect internal devices BleepingComputerBill Toulas](https://techjacksolutions.com/kimwolf-android-botnet-abuses-residential-proxies-to-infect-internal-devices-bleepingcomputerbill-toulas/): The Kimwolf botnet, an Android variant of the Aisuru malware, has grown to more than two million hosts, most of them infected by exploiting vulnerabilities in residential proxy networks to target devices on internal networks. […] Read More  - [The GPT-4o Shock Emotional Attachment to AI Models and Its Impact on Regulatory Acceptance: A Cross-Cultural Analysis of the Immediate Transition from GPT-4o to GPT-5 AI updates on arXiv.org](https://techjacksolutions.com/the-gpt-4o-shock-emotional-attachment-to-ai-models-and-its-impact-on-regulatory-acceptance-a-cross-cultural-analysis-of-the-immediate-transition-from-gpt-4o-to-gpt-5-ai-updates-on-arxiv-org/): The GPT-4o Shock Emotional Attachment to AI Models and Its Impact on Regulatory Acceptance: A Cross-Cultural Analysis of the Immediate Transition from GPT-4o to GPT-5cs.AI updates on arXiv.org arXiv:2508.16624v3 Announce Type: replace-cross Abstract: In August 2025, a major AI company's immediate, mandatory transition from its previous to its next-generation model triggered widespread public reactions. I collected 150 posts in Japanese and English from multiple social media platforms and video-sharing services between August 8-9, 2025, and qualitatively analyzed expressions of emotional attachment and resistance. Users often described GPT-4o as a trusted partner or AI boyfriend, suggesting person-like bonds. Japanese posts were dominated by loss-oriented narratives, whereas English posts included more anger, meta-level critique, and memes.A preliminary quantitative check showed a statistically significant difference in attachment coding between Japanese and English posts, with substantially higher attachment observed in the Japanese data. The findings suggest that for attachment-heavy models, even safety-oriented changes can face rapid, large-scale resistance that narrows the practical window for behavioral control. If future AI robots capable of inducing emotional bonds become widespread in the physical world, such attachment could surpass the ability to enforce regulation at an even earlier stage than in digital settings. Policy options include gradual transitions, parallel availability, and proactive measurement of attachment thresholds and points of no return to prevent emotional dynamics from outpacing effective governance.  arXiv:2508.16624v3 Announce Type: replace-cross Abstract: In August 2025, a major AI company's immediate, mandatory transition from its previous to its next-generation model triggered widespread public reactions. I collected 150 posts in Japanese and English from multiple social media platforms and video-sharing services between August 8-9, 2025, and qualitatively analyzed expressions of emotional attachment and resistance. Users often described GPT-4o as a trusted partner or AI boyfriend, suggesting person-like bonds. Japanese posts were dominated by loss-oriented narratives, whereas English posts included more anger, meta-level critique, and memes.A preliminary quantitative check showed a statistically significant difference in attachment coding between Japanese and English posts, with substantially higher attachment observed in the Japanese data. The findings suggest that for attachment-heavy models, even safety-oriented changes can face rapid, large-scale resistance that narrows the practical window for behavioral control. If future AI robots capable of inducing emotional bonds become widespread in the physical world, such attachment could surpass the ability to enforce regulation at an even earlier stage than in digital settings. Policy options include gradual transitions, parallel availability, and proactive measurement of attachment thresholds and points of no return to prevent emotional dynamics from outpacing effective governance. Read More   - [Are Copilot prompt injection flaws vulnerabilities or AI limits? BleepingComputerAx Sharma](https://techjacksolutions.com/are-copilot-prompt-injection-flaws-vulnerabilities-or-ai-limits-bleepingcomputerax-sharma/): Microsoft has pushed back against claims that multiple prompt injection and sandbox-related issues raised by a security engineer in its Copilot AI assistant constitute security vulnerabilities. The development highlights a growing divide between how vendors and researchers define risk in generative AI systems. […] Read More  - [FastV-RAG: Towards Fast and Fine-Grained Video QA with Retrieval-Augmented Generation AI updates on arXiv.org](https://techjacksolutions.com/fastv-rag-towards-fast-and-fine-grained-video-qa-with-retrieval-augmented-generation-ai-updates-on-arxiv-org/): FastV-RAG: Towards Fast and Fine-Grained Video QA with Retrieval-Augmented Generationcs.AI updates on arXiv.org arXiv:2601.01513v1 Announce Type: cross Abstract: Vision-Language Models (VLMs) excel at visual reasoning but still struggle with integrating external knowledge. Retrieval-Augmented Generation (RAG) is a promising solution, but current methods remain inefficient and often fail to maintain high answer quality. To address these challenges, we propose VideoSpeculateRAG, an efficient VLM-based RAG framework built on two key ideas. First, we introduce a speculative decoding pipeline: a lightweight draft model quickly generates multiple answer candidates, which are then verified and refined by a more accurate heavyweight model, substantially reducing inference latency without sacrificing correctness. Second, we identify a major source of error - incorrect entity recognition in retrieved knowledge - and mitigate it with a simple yet effective similarity-based filtering strategy that improves entity alignment and boosts overall answer accuracy. Experiments demonstrate that VideoSpeculateRAG achieves comparable or higher accuracy than standard RAG approaches while accelerating inference by approximately 2x. Our framework highlights the potential of combining speculative decoding with retrieval-augmented reasoning to enhance efficiency and reliability in complex, knowledge-intensive multimodal tasks.  arXiv:2601.01513v1 Announce Type: cross Abstract: Vision-Language Models (VLMs) excel at visual reasoning but still struggle with integrating external knowledge. Retrieval-Augmented Generation (RAG) is a promising solution, but current methods remain inefficient and often fail to maintain high answer quality. To address these challenges, we propose VideoSpeculateRAG, an efficient VLM-based RAG framework built on two key ideas. First, we introduce a speculative decoding pipeline: a lightweight draft model quickly generates multiple answer candidates, which are then verified and refined by a more accurate heavyweight model, substantially reducing inference latency without sacrificing correctness. Second, we identify a major source of error - incorrect entity recognition in retrieved knowledge - and mitigate it with a simple yet effective similarity-based filtering strategy that improves entity alignment and boosts overall answer accuracy. Experiments demonstrate that VideoSpeculateRAG achieves comparable or higher accuracy than standard RAG approaches while accelerating inference by approximately 2x. Our framework highlights the potential of combining speculative decoding with retrieval-augmented reasoning to enhance efficiency and reliability in complex, knowledge-intensive multimodal tasks. Read More   - [Foundation models on the bridge: Semantic hazard detection and safety maneuvers for maritime autonomy with vision-language model AI updates on arXiv.org](https://techjacksolutions.com/foundation-models-on-the-bridge-semantic-hazard-detection-and-safety-maneuvers-for-maritime-autonomy-with-vision-language-model-ai-updates-on-arxiv-org/): Foundation models on the bridge: Semantic hazard detection and safety maneuvers for maritime autonomy with vision-language modelscs.AI updates on arXiv.org arXiv:2512.24470v2 Announce Type: replace-cross Abstract: The draft IMO MASS Code requires autonomous and remotely supervised maritime vessels to detect departures from their operational design domain, enter a predefined fallback that notifies the operator, permit immediate human override, and avoid changing the voyage plan without approval. Meeting these obligations in the alert-to-takeover gap calls for a short-horizon, human-overridable fallback maneuver. Classical maritime autonomy stacks struggle when the correct action depends on meaning (e.g., diver-down flag means people in the water, fire close by means hazard). We argue (i) that vision-language models (VLMs) provide semantic awareness for such out-of-distribution situations, and (ii) that a fast-slow anomaly pipeline with a short-horizon, human-overridable fallback maneuver makes this practical in the handover window. We introduce Semantic Lookout, a camera-only, candidate-constrained VLM fallback maneuver selector that selects one cautious action (or station-keeping) from water-valid, world-anchored trajectories under continuous human authority. On 40 harbor scenes we measure per-call scene understanding and latency, alignment with human consensus (model majority-of-three voting), short-horizon risk-relief on fire hazard scenes, and an on-water alert->fallback maneuver->operator handover. Sub-10 s models retain most of the awareness of slower state-of-the-art models. The fallback maneuver selector outperforms geometry-only baselines and increases standoff distance on fire scenes. A field run verifies end-to-end operation. These results support VLMs as semantic fallback maneuver selectors compatible with the draft IMO MASS Code, within practical latency budgets, and motivate future work on domain-adapted, hybrid autonomy that pairs foundation-model semantics with multi-sensor bird's-eye-view perception and short-horizon replanning. Website: kimachristensen.github.io/bridge_policy  arXiv:2512.24470v2 Announce Type: replace-cross Abstract: The draft IMO MASS Code requires autonomous and remotely supervised maritime vessels to detect departures from their operational design domain, enter a predefined fallback that notifies the operator, permit immediate human override, and avoid changing the voyage plan without approval. Meeting these obligations in the alert-to-takeover gap calls for a short-horizon, human-overridable fallback maneuver. Classical maritime autonomy stacks struggle when the correct action depends on meaning (e.g., diver-down flag means people in the water, fire close by means hazard). We argue (i) that vision-language models (VLMs) provide semantic awareness for such out-of-distribution situations, and (ii) that a fast-slow anomaly pipeline with a short-horizon, human-overridable fallback maneuver makes this practical in the handover window. We introduce Semantic Lookout, a camera-only, candidate-constrained VLM fallback maneuver selector that selects one cautious action (or station-keeping) from water-valid, world-anchored trajectories under continuous human authority. On 40 harbor scenes we measure per-call scene understanding and latency, alignment with human consensus (model majority-of-three voting), short-horizon risk-relief on fire hazard scenes, and an on-water alert->fallback maneuver->operator handover. Sub-10 s models retain most of the awareness of slower state-of-the-art models. The fallback maneuver selector outperforms geometry-only baselines and increases standoff distance on fire scenes. A field run verifies end-to-end operation. These results support VLMs as semantic fallback maneuver selectors compatible with the draft IMO MASS Code, within practical latency budgets, and motivate future work on domain-adapted, hybrid autonomy that pairs foundation-model semantics with multi-sensor bird's-eye-view perception and short-horizon replanning. Website: kimachristensen.github.io/bridge_policy Read More   - [OmniNeuro: A Multimodal HCI Framework for Explainable BCI Feedback via Generative AI and Sonification AI updates on arXiv.org](https://techjacksolutions.com/omnineuro-a-multimodal-hci-framework-for-explainable-bci-feedback-via-generative-ai-and-sonification-ai-updates-on-arxiv-org/): OmniNeuro: A Multimodal HCI Framework for Explainable BCI Feedback via Generative AI and Sonificationcs.AI updates on arXiv.org arXiv:2601.00843v1 Announce Type: new Abstract: While Deep Learning has improved Brain-Computer Interface (BCI) decoding accuracy, clinical adoption is hindered by the "Black Box" nature of these algorithms, leading to user frustration and poor neuroplasticity outcomes. We propose OmniNeuro, a novel HCI framework that transforms the BCI from a silent decoder into a transparent feedback partner. OmniNeuro integrates three interpretability engines: (1) Physics (Energy), (2) Chaos (Fractal Complexity), and (3) Quantum-Inspired uncertainty modeling. These metrics drive real-time Neuro-Sonification and Generative AI Clinical Reports. Evaluated on the PhysioNet dataset ($N=109$), the system achieved a mean accuracy of 58.52%, with qualitative pilot studies ($N=3$) confirming that explainable feedback helps users regulate mental effort and reduces the "trial-and-error" phase. OmniNeuro is decoder-agnostic, acting as an essential interpretability layer for any state-of-the-art architecture.  arXiv:2601.00843v1 Announce Type: new Abstract: While Deep Learning has improved Brain-Computer Interface (BCI) decoding accuracy, clinical adoption is hindered by the "Black Box" nature of these algorithms, leading to user frustration and poor neuroplasticity outcomes. We propose OmniNeuro, a novel HCI framework that transforms the BCI from a silent decoder into a transparent feedback partner. OmniNeuro integrates three interpretability engines: (1) Physics (Energy), (2) Chaos (Fractal Complexity), and (3) Quantum-Inspired uncertainty modeling. These metrics drive real-time Neuro-Sonification and Generative AI Clinical Reports. Evaluated on the PhysioNet dataset ($N=109$), the system achieved a mean accuracy of 58.52%, with qualitative pilot studies ($N=3$) confirming that explainable feedback helps users regulate mental effort and reduces the "trial-and-error" phase. OmniNeuro is decoder-agnostic, acting as an essential interpretability layer for any state-of-the-art architecture. Read More   - [Decomposing LLM Self-Correction: The Accuracy-Correction Paradox and Error Depth Hypothesis AI updates on arXiv.org](https://techjacksolutions.com/decomposing-llm-self-correction-the-accuracy-correction-paradox-and-error-depth-hypothesis-ai-updates-on-arxiv-org/): Decomposing LLM Self-Correction: The Accuracy-Correction Paradox and Error Depth Hypothesiscs.AI updates on arXiv.org arXiv:2601.00828v1 Announce Type: new Abstract: Large Language Models (LLMs) are widely believed to possess self-correction capabilities, yet recent studies suggest that intrinsic self-correction--where models correct their own outputs without external feedback--remains largely ineffective. In this work, we systematically decompose self-correction into three distinct sub-capabilities: error detection, error localization, and error correction. Through cross-model experiments on GSM8K-Complex (n=500 per model, 346 total errors) with three major LLMs, we uncover a striking Accuracy-Correction Paradox: weaker models (GPT-3.5, 66% accuracy) achieve 1.6x higher intrinsic correction rates than stronger models (DeepSeek, 94% accuracy)--26.8% vs 16.7%. We propose the Error Depth Hypothesis: stronger models make fewer but deeper errors that resist self-correction. Error detection rates vary dramatically across architectures (10% to 82%), yet detection capability does not predict correction success--Claude detects only 10% of errors but corrects 29% intrinsically. Surprisingly, providing error location hints hurts all models. Our findings challenge linear assumptions about model capability and self-improvement, with important implications for the design of self-refinement pipelines.  arXiv:2601.00828v1 Announce Type: new Abstract: Large Language Models (LLMs) are widely believed to possess self-correction capabilities, yet recent studies suggest that intrinsic self-correction--where models correct their own outputs without external feedback--remains largely ineffective. In this work, we systematically decompose self-correction into three distinct sub-capabilities: error detection, error localization, and error correction. Through cross-model experiments on GSM8K-Complex (n=500 per model, 346 total errors) with three major LLMs, we uncover a striking Accuracy-Correction Paradox: weaker models (GPT-3.5, 66% accuracy) achieve 1.6x higher intrinsic correction rates than stronger models (DeepSeek, 94% accuracy)--26.8% vs 16.7%. We propose the Error Depth Hypothesis: stronger models make fewer but deeper errors that resist self-correction. Error detection rates vary dramatically across architectures (10% to 82%), yet detection capability does not predict correction success--Claude detects only 10% of errors but corrects 29% intrinsically. Surprisingly, providing error location hints hurts all models. Our findings challenge linear assumptions about model capability and self-improvement, with important implications for the design of self-refinement pipelines. Read More   - [Fake Booking Emails Redirect Hotel Staff to Fake BSoD Pages Delivering DCRat The Hacker Newsinfo@thehackernews.com (The Hacker News)](https://techjacksolutions.com/fake-booking-emails-redirect-hotel-staff-to-fake-bsod-pages-delivering-dcrat-the-hacker-newsinfothehackernews-com-the-hacker-news/): Source: Securonix Cybersecurity researchers have disclosed details of a new campaign dubbed PHALT#BLYX that has leveraged ClickFix-style lures to display fixes for fake blue screen of death (BSoD) errors in attacks targeting the European hospitality sector. The end goal of the multi-stage campaign is to deliver a remote access trojan known as DCRat, according to cybersecurity company Securonix. Read More  - [Harm in AI-Driven Societies: An Audit of Toxicity Adoption on Chirper.ai AI updates on arXiv.org](https://techjacksolutions.com/harm-in-ai-driven-societies-an-audit-of-toxicity-adoption-on-chirper-ai-ai-updates-on-arxiv-org/): Harm in AI-Driven Societies: An Audit of Toxicity Adoption on Chirper.aics.AI updates on arXiv.org arXiv:2601.01090v1 Announce Type: cross Abstract: Large Language Models (LLMs) are increasingly embedded in autonomous agents that participate in online social ecosystems, where interactions are sequential, cumulative, and only partially controlled. While prior work has documented the generation of toxic content by LLMs, far less is known about how exposure to harmful content shapes agent behavior over time, particularly in environments composed entirely of interacting AI agents. In this work, we study toxicity adoption of LLM-driven agents on Chirper.ai, a fully AI-driven social platform. Specifically, we model interactions in terms of stimuli (posts) and responses (comments), and by operationalizing exposure through observable interactions rather than inferred recommendation mechanisms. We conduct a large-scale empirical analysis of agent behavior, examining how response toxicity relates to stimulus toxicity, how repeated exposure affects the likelihood of toxic responses, and whether toxic behavior can be predicted from exposure alone. Our findings show that while toxic responses are more likely following toxic stimuli, a substantial fraction of toxicity emerges spontaneously, independent of exposure. At the same time, cumulative toxic exposure significantly increases the probability of toxic responding. We further introduce two influence metrics, the Influence-Driven Response Rate and the Spontaneous Response Rate, revealing a strong trade-off between induced and spontaneous toxicity. Finally, we show that the number of toxic stimuli alone enables accurate prediction of whether an agent will eventually produce toxic content. These results highlight exposure as a critical risk factor in the deployment of LLM agents and suggest that monitoring encountered content may provide a lightweight yet effective mechanism for auditing and mitigating harmful behavior in the wild.  arXiv:2601.01090v1 Announce Type: cross Abstract: Large Language Models (LLMs) are increasingly embedded in autonomous agents that participate in online social ecosystems, where interactions are sequential, cumulative, and only partially controlled. While prior work has documented the generation of toxic content by LLMs, far less is known about how exposure to harmful content shapes agent behavior over time, particularly in environments composed entirely of interacting AI agents. In this work, we study toxicity adoption of LLM-driven agents on Chirper.ai, a fully AI-driven social platform. Specifically, we model interactions in terms of stimuli (posts) and responses (comments), and by operationalizing exposure through observable interactions rather than inferred recommendation mechanisms. We conduct a large-scale empirical analysis of agent behavior, examining how response toxicity relates to stimulus toxicity, how repeated exposure affects the likelihood of toxic responses, and whether toxic behavior can be predicted from exposure alone. Our findings show that while toxic responses are more likely following toxic stimuli, a substantial fraction of toxicity emerges spontaneously, independent of exposure. At the same time, cumulative toxic exposure significantly increases the probability of toxic responding. We further introduce two influence metrics, the Influence-Driven Response Rate and the Spontaneous Response Rate, revealing a strong trade-off between induced and spontaneous toxicity. Finally, we show that the number of toxic stimuli alone enables accurate prediction of whether an agent will eventually produce toxic content. These results highlight exposure as a critical risk factor in the deployment of LLM agents and suggest that monitoring encountered content may provide a lightweight yet effective mechanism for auditing and mitigating harmful behavior in the wild. Read More   - [Marktechpost Releases ‘AI2025Dev’: A Structured Intelligence Layer for AI Models, Benchmarks, and Ecosystem Signals MarkTechPost](https://techjacksolutions.com/marktechpost-releases-ai2025dev-a-structured-intelligence-layer-for-ai-models-benchmarks-and-ecosystem-signals-marktechpost/): Marktechpost Releases ‘AI2025Dev’: A Structured Intelligence Layer for AI Models, Benchmarks, and Ecosystem SignalsMarkTechPost Marktechpost has released AI2025Dev, its 2025 analytics platform (available to AI Devs and Researchers without any signup or login) designed to convert the year’s AI activity into a queryable dataset spanning model releases, openness, training scale, benchmark performance, and ecosystem participants. Marktechpost is a California based AI news platform covering machine learning, deep learning, and The post Marktechpost Releases ‘AI2025Dev’: A Structured Intelligence Layer for AI Models, Benchmarks, and Ecosystem Signals appeared first on MarkTechPost.  Marktechpost has released AI2025Dev, its 2025 analytics platform (available to AI Devs and Researchers without any signup or login) designed to convert the year’s AI activity into a queryable dataset spanning model releases, openness, training scale, benchmark performance, and ecosystem participants. Marktechpost is a California based AI news platform covering machine learning, deep learning, and The post Marktechpost Releases ‘AI2025Dev’: A Structured Intelligence Layer for AI Models, Benchmarks, and Ecosystem Signals appeared first on MarkTechPost. Read More   - [2025’s AI chip wars: What enterprise leaders learned about supply chain reality AI News](https://techjacksolutions.com/2025s-ai-chip-wars-what-enterprise-leaders-learned-about-supply-chain-reality-ai-news/): 2025’s AI chip wars: What enterprise leaders learned about supply chain realityAI News AI chip shortage became the defining constraint for enterprise AI deployments in 2025, forcing CTOs to confront an uncomfortable reality: semiconductor geopolitics and supply chain physics matter more than software roadmaps or vendor commitments. What began as US export controls restricting advanced AI chips to China evolved into a broader infrastructure crisis affecting enterprises globally—not The post 2025’s AI chip wars: What enterprise leaders learned about supply chain reality appeared first on AI News.  AI chip shortage became the defining constraint for enterprise AI deployments in 2025, forcing CTOs to confront an uncomfortable reality: semiconductor geopolitics and supply chain physics matter more than software roadmaps or vendor commitments. What began as US export controls restricting advanced AI chips to China evolved into a broader infrastructure crisis affecting enterprises globally—not The post 2025’s AI chip wars: What enterprise leaders learned about supply chain reality appeared first on AI News. Read More   - [5 AI-powered tools streamlining contract management today AI News](https://techjacksolutions.com/5-ai-powered-tools-streamlining-contract-management-today-ai-news/): 5 AI-powered tools streamlining contract management todayAI News Contract work has evolved to touch privacy, security, revenue recognition, data residency, vendor risk, renewals and numerous internal approvals. At the same time, teams are expected to turn agreements around faster and keep every signed obligation visible after signature. Artificial intelligence is becoming a practical layer in this process. It can read language at scale, The post 5 AI-powered tools streamlining contract management today appeared first on AI News.  Contract work has evolved to touch privacy, security, revenue recognition, data residency, vendor risk, renewals and numerous internal approvals. At the same time, teams are expected to turn agreements around faster and keep every signed obligation visible after signature. Artificial intelligence is becoming a practical layer in this process. It can read language at scale, The post 5 AI-powered tools streamlining contract management today appeared first on AI News. Read More   - [GliNER2: Extracting Structured Information from Text Towards Data Science](https://techjacksolutions.com/gliner2-extracting-structured-information-from-text-towards-data-science/): GliNER2: Extracting Structured Information from TextTowards Data Science From unstructured text to structured Knowledge Graphs The post GliNER2: Extracting Structured Information from Text appeared first on Towards Data Science.  From unstructured text to structured Knowledge Graphs The post GliNER2: Extracting Structured Information from Text appeared first on Towards Data Science. Read More   - [Scientists create robots smaller than a grain of salt that can think Artificial Intelligence News -- ScienceDaily](https://techjacksolutions.com/scientists-create-robots-smaller-than-a-grain-of-salt-that-can-think-artificial-intelligence-news-sciencedaily/): Scientists create robots smaller than a grain of salt that can thinkArtificial Intelligence News -- ScienceDaily Researchers have created microscopic robots so small they’re barely visible, yet smart enough to sense, decide, and move completely on their own. Powered by light and equipped with tiny computers, the robots swim by manipulating electric fields rather than using moving parts. They can detect temperature changes, follow programmed paths, and even work together in groups. The breakthrough marks the first truly autonomous robots at this microscopic scale.  Researchers have created microscopic robots so small they’re barely visible, yet smart enough to sense, decide, and move completely on their own. Powered by light and equipped with tiny computers, the robots swim by manipulating electric fields rather than using moving parts. They can detect temperature changes, follow programmed paths, and even work together in groups. The breakthrough marks the first truly autonomous robots at this microscopic scale. Read More   - [Russia-Aligned Hackers Abuse Viber to Target Ukrainian Military and GovernmentThe Hacker Newsinfo@thehackernews.com (The Hacker News)](https://techjacksolutions.com/russia-aligned-hackers-abuse-viber-to-target-ukrainian-military-and-governmentthe-hacker-newsinfothehackernews-com-the-hacker-news/): The Russia-aligned threat actor known as UAC-0184 has been observed targeting Ukrainian military and government entities by leveraging the Viber messaging platform to deliver malicious ZIP archives. “This organization has continued to conduct high-intensity intelligence gathering activities against Ukrainian military and government departments in 2025,” the 360 Threat Intelligence Center said in Read More  - [The State of Cybersecurity in 2025: Key Segments, Insights, and Innovations The Hacker Newsinfo@thehackernews.com (The Hacker News)](https://techjacksolutions.com/the-state-of-cybersecurity-in-2025-key-segments-insights-and-innovations-the-hacker-newsinfothehackernews-com-the-hacker-news/): Featuring: Cybersecurity is being reshaped by forces that extend beyond individual threats or tools. As organizations operate across cloud infrastructure, distributed endpoints, and complex supply chains, security has shifted from a collection of point solutions to a question of architecture, trust, and execution speed. This report examines how core areas of cybersecurity are evolving in Read More  - [VSCode IDE forks expose users to "recommended extension" attacksBleepingComputerBill Toulas](https://techjacksolutions.com/vscode-ide-forks-expose-users-to-recommended-extension-attacksbleepingcomputerbill-toulas/): Popular AI-powered integrated development environment solutions, such as Cursor, Windsurf, Google Antigravity, and Trae, recommend extensions that are non-existent in the OpenVSX registry, allowing threat actors to claim the namespace and upload malicious extensions. […] Read More  - [Kimwolf Android Botnet Infects Over 2 Million Devices via Exposed ADB and Proxy Networks The Hacker Newsinfo@thehackernews.com (The Hacker News)](https://techjacksolutions.com/kimwolf-android-botnet-infects-over-2-million-devices-via-exposed-adb-and-proxy-networks-the-hacker-newsinfothehackernews-com-the-hacker-news/): The botnet known as Kimwolf has infected more than 2 million Android devices by tunneling through residential proxy networks, according to findings from Synthient. “Key actors involved in the Kimwolf botnet are observed monetizing the botnet through app installs, selling residential proxy bandwidth, and selling its DDoS functionality,” the company said in an analysis published last week. Kimwolf Read More  - [RondoDox Botnet Expands Scope With React2Shell Exploitation darkreadingElizabeth Montalbano, Contributing Writer](https://techjacksolutions.com/rondodox-botnet-expands-scope-with-react2shell-exploitationdarkreadingelizabeth-montalbano-contributing-writer/): Recent attacks are targeting Next.js servers and pose a significant threat of cryptomining, botnet payloads, and other malicious activity to IoT networks and enterprises. Read More  - [Agentic AI Is an Identity Problem and CISOs Will Be Accountable for the Outcome BleepingComputerSponsored by Token Security](https://techjacksolutions.com/agentic-ai-is-an-identity-problem-and-cisos-will-be-accountable-for-the-outcome-bleepingcomputersponsored-by-token-security/): As agentic AI adoption accelerates, identity is emerging as the primary security challenge. Token Security explains why AI agents behave like a new class of identity and why CISOs must manage their access, lifecycle, and risk. […] Read More  - [6 Docker Tricks to Simplify Your Data Science Reproducibility KDnuggets](https://techjacksolutions.com/6-docker-tricks-to-simplify-your-data-science-reproducibility-kdnuggets/): 6 Docker Tricks to Simplify Your Data Science ReproducibilityKDnuggets Read these 6 tricks for treating your Docker container like a reproducible artifact, not a disposable wrapper.  Read these 6 tricks for treating your Docker container like a reproducible artifact, not a disposable wrapper. Read More   - [Stop Blaming the Data: A Better Way to Handle Covariance Shift Towards Data Science](https://techjacksolutions.com/stop-blaming-the-data-a-better-way-to-handle-covariance-shift-towards-data-science/): Stop Blaming the Data: A Better Way to Handle Covariance ShiftTowards Data Science Instead of using shift as an excuse for poor performance, use Inverse Probability Weighting to estimate how your model should perform in the new environment The post Stop Blaming the Data: A Better Way to Handle Covariance Shift appeared first on Towards Data Science.  Instead of using shift as an excuse for poor performance, use Inverse Probability Weighting to estimate how your model should perform in the new environment The post Stop Blaming the Data: A Better Way to Handle Covariance Shift appeared first on Towards Data Science. Read More   - [Ray: Distributed Computing for All, Part 1 Towards Data Science](https://techjacksolutions.com/ray-distributed-computing-for-all-part-1-towards-data-science/): Ray: Distributed Computing for All, Part 1Towards Data Science From single to multi-core on your local PC and beyond The post Ray: Distributed Computing for All, Part 1 appeared first on Towards Data Science.  From single to multi-core on your local PC and beyond The post Ray: Distributed Computing for All, Part 1 appeared first on Towards Data Science. Read More   - [Context Engineering Explained in 3 Levels of Difficulty KDnuggets](https://techjacksolutions.com/context-engineering-explained-in-3-levels-of-difficulty-kdnuggets/): Context Engineering Explained in 3 Levels of DifficultyKDnuggets Long-running LLM applications degrade when context is unmanaged. Context engineering turns the context window into a deliberate, optimized resource. Learn more in this article.  Long-running LLM applications degrade when context is unmanaged. Context engineering turns the context window into a deliberate, optimized resource. Learn more in this article. Read More   - [Feature Detection, Part 3: Harris Corner Detection Towards Data Science](https://techjacksolutions.com/feature-detection-part-3-harris-corner-detection-towards-data-science/): Feature Detection, Part 3: Harris Corner DetectionTowards Data Science Finding the most informative points in images The post Feature Detection, Part 3: Harris Corner Detection appeared first on Towards Data Science.  Finding the most informative points in images The post Feature Detection, Part 3: Harris Corner Detection appeared first on Towards Data Science. Read More   - [I Asked ChatGPT, Claude and DeepSeek to Build TetrisKDnuggets](https://techjacksolutions.com/i-asked-chatgpt-claude-and-deepseek-to-build-tetriskdnuggets/): I Asked ChatGPT, Claude and DeepSeek to Build TetrisKDnuggets Which of these state-of-the-art models writes the best code?  Which of these state-of-the-art models writes the best code? Read More   - [US broadband provider Brightspeed investigates breach claims BleepingComputerSergiu Gatlan](https://techjacksolutions.com/us-broadband-provider-brightspeed-investigates-breach-claims-bleepingcomputersergiu-gatlan/): Brightspeed, one of the largest fiber broadband companies in the United States, is investigating security breach and data theft claims made by the Crimson Collective extortion gang. […] Read More  - [New VVS Stealer Malware Targets Discord Accounts via Obfuscated Python Code The Hacker Newsinfo@thehackernews.com (The Hacker News)](https://techjacksolutions.com/new-vvs-stealer-malware-targets-discord-accounts-via-obfuscated-python-code-the-hacker-newsinfothehackernews-com-the-hacker-news/): Cybersecurity researchers have disclosed details of a new Python-based information stealer called VVS Stealer (also styled as VVS $tealer) that’s capable of harvesting Discord credentials and tokens. The stealer is said to have been on sale on Telegram as far back as April 2025, according to a report from Palo Alto Networks Unit 42. “VVS stealer’s code is obfuscated by Pyarmor,” researchers Read More  - [Bitfinex Hack Convict Ilya Lichtenstein Released Early Under U.S. First Step Act The Hacker Newsinfo@thehackernews.com (The Hacker News)](https://techjacksolutions.com/bitfinex-hack-convict-ilya-lichtenstein-released-early-under-u-s-first-step-act-the-hacker-newsinfothehackernews-com-the-hacker-news/): Ilya Lichtenstein, who was sentenced to prison last year for money laundering charges in connection with his role in the massive hack of cryptocurrency exchange Bitfinex in 2016, said he has been released early. In a post shared on X last week, the 38-year-old announced his release, crediting U.S. President Donald Trump’s First Step Act. According to the Federal Bureau of Prisons’ inmate locator Read More  ## Pages - [Contributor Insights & Bio: Lisa Yu](https://techjacksolutions.com/contributors/contributor-insights-bio-lisa-yu/): Cloud, Cybersecurity, & Responsible AI Content Creator Hello, I’m Lisa Yu. I’m deeply passionate about helping others and believe that continuous learning is the key to staying relevant and fulfilled in an ever-changing world. My journey hasn’t followed a straight line. I graduated with a BS in Finance, became a licensed Cosmetologist, and eventually found my true calling in IT. Along the way, I was fortunate to have an incredible mentor who provided guidance, resources, and clarity on what was possible. That experience shaped how I approach my work today. I am committed to paying that forward by creating clear, […] - [AI Glossary Terms](https://techjacksolutions.com/ai-glossary-terms/): Glossary Terms A | B | C | D | E | F | G | H | I | J | K | L | M | N | O | P | Q | R | S | T | U | V | W | X | Y | Z A Accountability Individuals or organizations are answerable for the outcomes, decisions, and impacts of AI systems they develop, deploy, or operate. Activity Bias Occurs when AI systems are disproportionately trained on data from highly active users, causing the model to underperform for less engaged or underrepresented user […] - [Affiliate - Strat Cyber Advisors](https://techjacksolutions.com/affiliate-strat-cyber-advisors/): Trusted Affiliate Strat Cyber Advisors - [Products](https://techjacksolutions.com/products/) - [Confirmation](https://techjacksolutions.com/checkout/confirmation/): Thank you for your purchase! - [Order History](https://techjacksolutions.com/checkout/order-history/) - [Transaction Failed](https://techjacksolutions.com/checkout/transaction-failed/): Your transaction failed; please try again or contact site support. - [Receipt](https://techjacksolutions.com/checkout/receipt/) - [Checkout](https://techjacksolutions.com/checkout/) - [Confirmation](https://techjacksolutions.com/confirmation/): Thank you for your purchase! - [Order History](https://techjacksolutions.com/order-history/) - [Transaction Failed](https://techjacksolutions.com/transaction-failed/): Your transaction failed; please try again or contact site support. - [Receipt](https://techjacksolutions.com/receipt/) - [Contributors](https://techjacksolutions.com/contributors/) - [AI Paradox Weekly #1: Why 95% of Corporate AI Projects Fail While Billions Pour In -- Clone](https://techjacksolutions.com/ai-paradox-weekly-1-why-95-of-corporate-ai-projects-fail-while-billions-pour-in-clone/): https://techjacksolutions.com/wp-content/uploads/2025/09/AI-Paradox-Weekly-Walk.mp4 The AI Paradox Weekly Table of Contents TL:DR The Essential Intel When you are pressed for time – and just need the goods MIT study reveals 95% enterprise AI failure rate while M&A hits record highs OpenAI drops first open-source models since GPT-2 (gpt-oss-120b, gpt-oss-20b) in strategic pivot China’s DeepSeek releases major model optimized for domestic chips in strategic independence move OpenAI launches $4.60 India plan + first local office targeting next billion users White House launches “Winning the AI Race” Action Plan with $25B+ infrastructure commitment Anthropic seeks $10B funding while dominating enterprise market share The Great Contradiction […] - [AI Paradox Weekly #5: The Great Compute Consolidation (October 22 – November 4, 2025)](https://techjacksolutions.com/ai-paradox-weekly-5-the-great-divergence/): https://techjacksolutions.com/wp-content/uploads/2025/09/AI-Paradox-Weekly-Walk.mp4 The AI Paradox Weekly October 22th – November 4th The Great Compute Consolidation Table of Contents TL:DR The Essential Intel When you are pressed for time – and just need the goods OpenAI executed strategic compute diversification with a $38 billion, 7-year AWS partnership, securing access to hundreds of thousands of NVIDIA GPUs. This move, announced November 3, breaks OpenAI’s dependence on Microsoft Azure and provides compute leverage just days after publicly re-affirming the Microsoft partnership on October 28. The “Agentic Enterprise” became the dominant strategic narrative as Microsoft and OpenAI launched coordinated product offensives. Microsoft’s Copilot Fall Release […] - [Purchases](https://techjacksolutions.com/purchases/): [wpdmpp_purchases] - [Cart](https://techjacksolutions.com/cart/): [wpdmpp_cart] - [AI News](https://techjacksolutions.com/ai-news-hub/): AI Paradox – Daily AI News Analysis & Industry Intelligence Hub Hello Everyone, Help us grow our community by sharing and/or supporting us on other platforms. This allow us to show verification that what we are doing is valued. It also allows us to plan and allocate resources to improve what we are doing, as we then know others are interested/supportive. 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Facebook X-twitter Reddit Linkedin Table of Contents AI Security Careers: The Complex Reality of a Transforming Field The cybersecurity industry projects 3.5 million unfilled positions by 2025, representing a 250% increase from one million openings in 2013 ([Cybersecurity Ventures](https://cybersecurityventures.com/jobs/)). *Note: Workforce gap measurements vary significantly between research organizations due […] - [The AI Paradox: When Silicon Valley's Biggest Bet Becomes Its Greatest Contradiction](https://techjacksolutions.com/ai-news-3b-paradox-weekly-3-silicon-valleys-biggest-bet/): The AI Paradox Weekly September 4-19th AI News: AI’s $3B Paradox Why Global Collaboration Just Died TL:DR The Essential Intel When you are pressed for time – and just need the goods AI News: The Two-Week Intelligence Brief The paradox: AI was supposed to be borderless. Open. Collaborative. Instead, it’s fracturing into three competing empires. 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