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Earlier sources: Artificial Intelligence
Artificial intelligence covers systems that learn from data, generate content, perceive their surroundings, or act toward a goal. Follow model research, agent tooling, evaluation, and the infrastructure used to build and run these systems.
SYMTRI organizes observations from public sources into a living map. Connections reflect shared subject matter; they do not establish causation or endorse a source.
Open in the map ↗6073 classified signals in the fourteen-day window. Recent momentum: steady. These counts include signals awaiting readable evidence.
Earlier sources
Up to ten sources with readable evidence, ordered by their feed timestamp. These archive sources can be older than the live fourteen-day window. This list is a sample, not a measure of total activity. Dates distinguish paper publication, repository creation, model/dataset activity, Hacker News posts, and KEV catalog additions. “First archived” records when SYMTRI first stored the signal.
- OpenAlex · Published · First archived
Physical-intuition-driven nonlinear ignition-threshold scaling and shell-aspect-ratio regime separation for 1D ICF via TreeSHAP explainable AI ↗
Laser-driven inertial-confinement-fusion achieves thermonuclear ignition via spherical capsule compression, yet high-fidelity multi-dimensional radiation-hydrodynamic simulations demand prohibitive computational resources. The ignition-threshold-factor (ITF) quantifies ignition margins to constrain target-design parameter spaces. Conventional log-linear ITF scaling-laws exhibit systematic prediction bias at extreme shell aspect ratios, while black-box machine-learning models achieve high accuracy but lack interpretable hydrodynamic mechanisms. Guided by physical-intuition-informed priors, we a…
- OpenAlex · Published · First archived
A Hyperchaotic Neural Network with Multi-Structure Attractors and Its Application in PRNG ↗
Memristive Hopfield neural networks (HNNs) have been widely explored for chaotic signal generation and data encryption owing to their rich dynamical behaviors. However, most existing memristive HNN models suffer from limited controllability of the number of coexisting attractors, insufficient dimensional complexity of chaotic dynamics, and weak adaptability to industrial grayscale image encryption scenarios. To address these issues, this paper proposes a novel memristive HNN model with a single magnetic flux-coupling memristor and systematically investigates its dynamical characteristics and p…
- OpenAlex · Published · First archived
HQDeTransformer: hybrid quantum denoising Transformer for time series forecasting ↗
Accurate prediction of the remaining useful life (RUL) of lithium-ion batteries is critical for enhancing the reliability and safety of battery-powered systems, especially in electric vehicles. While recent machine learning approaches such as Transformers and recurrent neural networks have shown promising results, their deployment in resource-constrained environments is limited due to high computational costs and large model sizes. In this paper, we propose a novel hybrid quantum-classical denoising Transformer (HQDeTransformer) architecture that integrates variational quantum circuits (VQCs)…
- OpenAlex · Published · First archived
PoCE: Automated Proof-of-Concept Synthesis using Large Language Models for Robust Validation ↗
Vulnerability reports play a critical role in software repair, with Proof-of-Concept (PoC) tests serving as one of their most essential components. PoC tests enable software developers to reliably reproduce reported vulnerabilities and subsequently deploy patches. However, generating effective PoCs is costly, expertise-intensive, and increasingly challenging due to the diversity of modern software ecosystems and their complex dependencies. Inadequate or incorrect PoCs can significantly delay patch deployment, thereby increasing the window of exposure to attacks. Prior work on automated PoC gen…
- OpenAlex · Published · First archived
NSync: Automated Cloud Infrastructure-as-Code Reconciliation with AI Agents ↗
Cloud infrastructure is managed through a mix of interfaces—traditionally, cloud consoles, command-line interfaces (CLI), and SDKs are the tools of choice. Recently, Infrastructure-as-Code/IaC frameworks (e.g., Terraform) have quickly gained popularity. Unlike conventional tools, IaC frameworks encode the infrastructure in a ”source-of-truth” configuration. They are capable of automatically carrying out modifications to the cloud—deploying, updating, or destroying resources—to bring the actual infrastructure into alignment with the IaC configuration. When IaC frameworks are used together with…
- OpenAlex · Published · First archived
A Semantically Oriented Method for Knowledge Representation in Artificial Intelligence Systems for Cybersecurity ↗
This paper proposes a semantically oriented knowledge-representation method for transforming primary cyber-event data into structured semantic knowledge to support explainable reasoning in cybersecurity decision-making. The core contribution is a transformation mechanism from primary semantic information (PSI) to secondary semantic information (SSI), based on a frame model of network events and attack patterns and followed by rule-based logical inference. This inference process maps semantic predicates to MITRE Adversarial Tactics, Techniques, and Common Knowledge (MITRE ATT&CK) techniques and…
- OpenAlex · Published · First archived
Machine Learning-Based Prediction of Bioprinting Quality Using Integrated Experimental and Computational Data ↗
Three-dimensional (3D) bioprinting of hydrogel inks is an enabling technology for tissue engineering and the fabrication of personalized drug-delivery systems. However, in most laboratories the extrusion parameters are still optimized by slow, resource-intensive trial-and-error, because printability depends on the combined and often nonlinear influence of ink composition, extrusion pressure, and nozzle geometry rather than on any single variable. Alginate–methylcellulose (Alg–MC) is a widely used pharmaceutical bioink whose shear-thinning behavior makes this optimization particularly demanding…
- OpenAlex · Published · First archived
CAST-DRO: Cognition-aware service triage with configurable risk adjustment for resource-constrained AI information services ↗
AI information service platforms must allocate heterogeneous requests across models, memory, clarification, and human review under joint resource constraints. We develop CAST, a cognition-aware five-action service-triage framework; CAST-ERM learns group-level empirical allocations, and CAST-DRO adds a configurable mean-plus-variability adjustment. Using sequence-grouped train/evaluation splits for 520 LifeSim-derived evaluation requests across 20 repeated splits, CAST-DRO improves over rule-based CAST in quality (0.679 to 0.743), failure rate (0.617 to 0.350), and final satisfaction (46.35 to…
- OpenAlex · Published · First archived
AI-Driven Adaptive Smart Grid for Real-Time Energy Optimization and Fault Detection ↗
Abstract: Conventional smart-grid systems often rely on fixed control strategies that have limited ability to respond dynamically to changing electricity demand, renewable-energy variability, equipment degradation, and unexpected faults. This study proposes an AI-Driven Adaptive Smart Grid (AI-ASG) for real-time energy optimization and automated fault detection. The framework integrates machine learning, real-time sensing, adaptive demand-response control, and intelligent fault classification to improve grid efficiency, reliability, and operational responsiveness. A mixed-methods quantitative…
- OpenAlex · Published · First archived
Pastoral AI ethics for chatbots in religious education ↗
Abstract Religious Education faces new ethical challenges as AI-powered chatbots become capable of offering scriptural explanations, moral guidance, and personalized learning support. This theoretical article proposes a Pastoral AI Ethics framework for the responsible use of AI chatbots in Religious Education. The framework integrates AI ethics principles, including privacy, transparency, fairness, and accountability, with pastoral values such as confidentiality, doctrinal fidelity, human dignity, and communal trust. The article also presents a conceptual design for a religious learning chatbo…
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How to assess evidence about Artificial Intelligence
Check each original source for its methods, release status, limitations, and supporting data. A repository announcement, a preprint, and a published study provide different kinds of evidence. SYMTRI’s subject classification helps discovery; it does not independently validate the source’s claims.
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