Making LLMs Reliable When It Matters Most: A Five-Layer Architecture for High-Stakes Decisionscs.AI updates on arXiv.org arXiv:2511.07669v1 Announce Type: new
Abstract: Current large language models (LLMs) excel in verifiable domains where outputs can be checked before action but prove less reliable for high-stakes strategic decisions with uncertain outcomes. This gap, driven by mutually reinforcing cognitive biases in both humans and artificial intelligence (AI) systems, threatens the defensibility of valuations and sustainability of investments in the sector.
This report describes a framework emerging from systematic qualitative assessment across 7 frontier-grade LLMs and 3 market-facing venture vignettes under time pressure. Detailed prompting specifying decision partnership and explicitly instructing avoidance of sycophancy, confabulation, solution drift, and nihilism achieved initial partnership state but failed to maintain it under operational pressure. Sustaining protective partnership state required an emergent 7-stage calibration sequence, built upon a 4-stage initialization process, within a 5-layer protection architecture enabling bias self-monitoring, human-AI adversarial challenge, partnership state verification, performance degradation detection, and stakeholder protection.
Three discoveries resulted: partnership state is achievable through ordered calibration but requires emergent maintenance protocols; reliability degrades when architectural drift and context exhaustion align; and dissolution discipline prevents costly pursuit of fundamentally wrong directions. Cross-model validation revealed systematic performance differences across LLM architectures.
This approach demonstrates that human-AI teams can achieve cognitive partnership capable of preventing avoidable regret in high-stakes decisions, addressing return-on-investment expectations that depend on AI systems supporting consequential decision-making without introducing preventable cognitive traps when verification arrives too late.
arXiv:2511.07669v1 Announce Type: new
Abstract: Current large language models (LLMs) excel in verifiable domains where outputs can be checked before action but prove less reliable for high-stakes strategic decisions with uncertain outcomes. This gap, driven by mutually reinforcing cognitive biases in both humans and artificial intelligence (AI) systems, threatens the defensibility of valuations and sustainability of investments in the sector.
This report describes a framework emerging from systematic qualitative assessment across 7 frontier-grade LLMs and 3 market-facing venture vignettes under time pressure. Detailed prompting specifying decision partnership and explicitly instructing avoidance of sycophancy, confabulation, solution drift, and nihilism achieved initial partnership state but failed to maintain it under operational pressure. Sustaining protective partnership state required an emergent 7-stage calibration sequence, built upon a 4-stage initialization process, within a 5-layer protection architecture enabling bias self-monitoring, human-AI adversarial challenge, partnership state verification, performance degradation detection, and stakeholder protection.
Three discoveries resulted: partnership state is achievable through ordered calibration but requires emergent maintenance protocols; reliability degrades when architectural drift and context exhaustion align; and dissolution discipline prevents costly pursuit of fundamentally wrong directions. Cross-model validation revealed systematic performance differences across LLM architectures.
This approach demonstrates that human-AI teams can achieve cognitive partnership capable of preventing avoidable regret in high-stakes decisions, addressing return-on-investment expectations that depend on AI systems supporting consequential decision-making without introducing preventable cognitive traps when verification arrives too late. Read More
Fighting the New York Times’ invasion of user privacyOpenAI News OpenAI is fighting the New York Times’ demand for 20 million private ChatGPT conversations and accelerating new security and privacy protections to protect your data.
OpenAI is fighting the New York Times’ demand for 20 million private ChatGPT conversations and accelerating new security and privacy protections to protect your data. Read More
Laplacian Score Sharpening for Mitigating Hallucination in Diffusion Modelscs.AI updates on arXiv.org arXiv:2511.07496v1 Announce Type: cross
Abstract: Diffusion models, though successful, are known to suffer from hallucinations that create incoherent or unrealistic samples. Recent works have attributed this to the phenomenon of mode interpolation and score smoothening, but they lack a method to prevent their generation during sampling. In this paper, we propose a post-hoc adjustment to the score function during inference that leverages the Laplacian (or sharpness) of the score to reduce mode interpolation hallucination in unconditional diffusion models across 1D, 2D, and high-dimensional image data. We derive an efficient Laplacian approximation for higher dimensions using a finite-difference variant of the Hutchinson trace estimator. We show that this correction significantly reduces the rate of hallucinated samples across toy 1D/2D distributions and a high- dimensional image dataset. Furthermore, our analysis explores the relationship between the Laplacian and uncertainty in the score.
arXiv:2511.07496v1 Announce Type: cross
Abstract: Diffusion models, though successful, are known to suffer from hallucinations that create incoherent or unrealistic samples. Recent works have attributed this to the phenomenon of mode interpolation and score smoothening, but they lack a method to prevent their generation during sampling. In this paper, we propose a post-hoc adjustment to the score function during inference that leverages the Laplacian (or sharpness) of the score to reduce mode interpolation hallucination in unconditional diffusion models across 1D, 2D, and high-dimensional image data. We derive an efficient Laplacian approximation for higher dimensions using a finite-difference variant of the Hutchinson trace estimator. We show that this correction significantly reduces the rate of hallucinated samples across toy 1D/2D distributions and a high- dimensional image dataset. Furthermore, our analysis explores the relationship between the Laplacian and uncertainty in the score. Read More
HybridGuard: Enhancing Minority-Class Intrusion Detection in Dew-Enabled Edge-of-Things Networkscs.AI updates on arXiv.org arXiv:2511.07793v1 Announce Type: cross
Abstract: Securing Dew-Enabled Edge-of-Things (EoT) networks against sophisticated intrusions is a critical challenge. This paper presents HybridGuard, a framework that integrates machine learning and deep learning to improve intrusion detection. HybridGuard addresses data imbalance through mutual information based feature selection, ensuring that the most relevant features are used to improve detection performance, especially for minority attack classes. The framework leverages Wasserstein Conditional Generative Adversarial Networks with Gradient Penalty (WCGAN-GP) to further reduce class imbalance and enhance detection precision. It adopts a two-phase architecture called DualNetShield to support advanced traffic analysis and anomaly detection, improving the granular identification of threats in complex EoT environments. HybridGuard is evaluated on the UNSW-NB15, CIC-IDS-2017, and IOTID20 datasets, where it demonstrates strong performance across diverse attack scenarios and outperforms existing solutions in adapting to evolving cybersecurity threats. This approach establishes HybridGuard as an effective tool for protecting EoT networks against modern intrusions.
arXiv:2511.07793v1 Announce Type: cross
Abstract: Securing Dew-Enabled Edge-of-Things (EoT) networks against sophisticated intrusions is a critical challenge. This paper presents HybridGuard, a framework that integrates machine learning and deep learning to improve intrusion detection. HybridGuard addresses data imbalance through mutual information based feature selection, ensuring that the most relevant features are used to improve detection performance, especially for minority attack classes. The framework leverages Wasserstein Conditional Generative Adversarial Networks with Gradient Penalty (WCGAN-GP) to further reduce class imbalance and enhance detection precision. It adopts a two-phase architecture called DualNetShield to support advanced traffic analysis and anomaly detection, improving the granular identification of threats in complex EoT environments. HybridGuard is evaluated on the UNSW-NB15, CIC-IDS-2017, and IOTID20 datasets, where it demonstrates strong performance across diverse attack scenarios and outperforms existing solutions in adapting to evolving cybersecurity threats. This approach establishes HybridGuard as an effective tool for protecting EoT networks against modern intrusions. Read More
Global Optimization on Graph-Structured Data via Gaussian Processes with Spectral Representationscs.AI updates on arXiv.org arXiv:2511.07734v1 Announce Type: cross
Abstract: Bayesian optimization (BO) is a powerful framework for optimizing expensive black-box objectives, yet extending it to graph-structured domains remains challenging due to the discrete and combinatorial nature of graphs. Existing approaches often rely on either full graph topology-impractical for large or partially observed graphs-or incremental exploration, which can lead to slow convergence. We introduce a scalable framework for global optimization over graphs that employs low-rank spectral representations to build Gaussian process (GP) surrogates from sparse structural observations. The method jointly infers graph structure and node representations through learnable embeddings, enabling efficient global search and principled uncertainty estimation even with limited data. We also provide theoretical analysis establishing conditions for accurate recovery of underlying graph structure under different sampling regimes. Experiments on synthetic and real-world datasets demonstrate that our approach achieves faster convergence and improved optimization performance compared to prior methods.
arXiv:2511.07734v1 Announce Type: cross
Abstract: Bayesian optimization (BO) is a powerful framework for optimizing expensive black-box objectives, yet extending it to graph-structured domains remains challenging due to the discrete and combinatorial nature of graphs. Existing approaches often rely on either full graph topology-impractical for large or partially observed graphs-or incremental exploration, which can lead to slow convergence. We introduce a scalable framework for global optimization over graphs that employs low-rank spectral representations to build Gaussian process (GP) surrogates from sparse structural observations. The method jointly infers graph structure and node representations through learnable embeddings, enabling efficient global search and principled uncertainty estimation even with limited data. We also provide theoretical analysis establishing conditions for accurate recovery of underlying graph structure under different sampling regimes. Experiments on synthetic and real-world datasets demonstrate that our approach achieves faster convergence and improved optimization performance compared to prior methods. Read More
How to Evaluate Retrieval Quality in RAG Pipelines (Part 3): DCG@k and NDCG@kTowards Data Science The third and final part for evaluating the retrieval quality of your RAG pipeline with graded measures
The post How to Evaluate Retrieval Quality in RAG Pipelines (Part 3): DCG@k and NDCG@k appeared first on Towards Data Science.
The third and final part for evaluating the retrieval quality of your RAG pipeline with graded measures
The post How to Evaluate Retrieval Quality in RAG Pipelines (Part 3): DCG@k and NDCG@k appeared first on Towards Data Science. Read More
The 5 FREE Must-Read Books for Every AI EngineerKDnuggets A handpicked list of free reads that teach you the science, logic, and real-world side of artificial intelligence.
A handpicked list of free reads that teach you the science, logic, and real-world side of artificial intelligence. Read More
Feature Detection, Part 2: Laplace & Gaussian OperatorsTowards Data Science Laplace meets Gaussian — the story of two operators in edge detection
The post Feature Detection, Part 2: Laplace & Gaussian Operators appeared first on Towards Data Science.
Laplace meets Gaussian — the story of two operators in edge detection
The post Feature Detection, Part 2: Laplace & Gaussian Operators appeared first on Towards Data Science. Read More
The Ultimate Guide to Power BI AggregationsTowards Data Science Aggregations are one of the most powerful features in Power BI — learn how to leverage this feature to improve the performance of your Power BI solution
The post The Ultimate Guide to Power BI Aggregations appeared first on Towards Data Science.
Aggregations are one of the most powerful features in Power BI — learn how to leverage this feature to improve the performance of your Power BI solution
The post The Ultimate Guide to Power BI Aggregations appeared first on Towards Data Science. Read More
Datasets for Training a Language ModelMachineLearningMastery.com A good language model should learn correct language usage, free of biases and errors.
A good language model should learn correct language usage, free of biases and errors. Read More
