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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 ↗5928 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
Automated WAF Rule Generation from Adversarial Prompt Pattern Mining for Perimeter-Level Protection of LLM Applications ↗
This work proposes an adversarial prompt pattern mining methodology for the automated generation of Web Application Firewall (WAF) detection rules for the perimeter-level inspection of requests to Large Language Model (LLM) applications. The proposed methodology extracts linguistic patterns from an adversarial prompt corpus using sequential n-gram analysis, TF–IDF weighting, normalized frequency, and Interest Factor (IF) metrics. The resulting patterns are prioritized through a configurable scoring function and automatically transformed into ModSecurity-compatible rules capable of inspecting H…
- OpenAlex · Published · First archived
Re-Engineering InSAR Kinematics via Local Markov Memory: A Scalable Machine Learning Framework for Corridor Slope Displacement Forecasting ↗
Slope instabilities pose a significant threat to transportation infrastructure, creating the need for reliable data-driven approaches for displacement forecasting. This study proposes a scalable machine learning framework that integrates Small Baseline Subset Interferometric Synthetic Aperture Radar (SBAS-InSAR) displacement time series with a structured sliding-window transformation to predict short-term vertical slope displacements under multi-cadence satellite acquisition schedules. The transformation reformats raw InSAR observations into fixed-length input windows, where the four most rece…
- OpenAlex · Published · First archived
Prediction and analysis of displacement response of submerged floating tunnel under impact loading based on machine learning and SHAP methods ↗
Submerged floating tunnels (SFTs) are prone to accidental impact hazards such as ship anchoring and vessel collisions. Existing displacement prediction methods suffer from limited working condition coverage, ambiguous nonlinear multi-factor coupling mechanisms, and the black-box limitation of traditional machine learning (ML). This work develops an interpretable high-precision prediction framework for impact-induced SFT tube displacement responses. A comprehensive dataset is constructed by combining drop-hammer impact model tests and high-fidelity ABAQUS fluid-structure interaction simulations…
- OpenAlex · Published · First archived
Cheby-FENN: a Chebyshev-enhanced feature expansion neural network for electricity theft detection ↗
Abstract Electricity theft is a major contributor to non-technical losses in power systems, causing substantial financial losses and potentially affecting grid reliability. This paper proposes a lightweight neural network framework that incorporates a third-degree Chebyshev polynomial feature-expansion layer at the input stage to enhance the representation of nonlinear electricity consumption patterns. The proposed framework is evaluated on two publicly available benchmark datasets, Open Energy Data Initiative (OEDI), and State Grid Corporation of China (SGCC), representing binary and multi-cl…
- OpenAlex · Published · First archived
The renaissance of information retrieval and the limitations of artificial intelligence ↗
This paper critically examines the limitations of generative AI in medicine and challenges the popular narrative that multimodality alone can rescue its current shortcomings. It argues for a shift toward evidence-based AI grounded in structured, high-quality clinical data rather than unsupported generation. Retrieval-Augmented Generation (RAG) is presented as a promising framework for improving grounding, attribution, and explainability, though its clinical use remains underdeveloped. The paper also highlights the risks of overreliance on commercial cloud infrastructure, particularly with resp…
- OpenAlex · Published · First archived
AI-based smart load balancing for sustainable electric vehicle charging hubs using LSTM forecasting and reinforcement learning ↗
The rapid growth of electric vehicles (EVs) on a global scale has put a significant burden on existing electricity distribution structures. Traditional charging methods at a public charger, utilizing fixed rates, cannot adjust to the continually changing supply and demand for renewable electricity or the effect of peak loads on the electric power grid system. To solve this very important limitation, this research presented an AI-Based Smart Load Balancing Electric Vehicle Charging Hub (AI-SLBECH). The proposed framework includes a simulation-validated, multi-layer intelligent ecosystem that us…
- OpenAlex · Published · First archived
Artificial Intelligence for Mineral Exploration Imagery: A Systematic Mapping Review of Data, Tasks, and Methods ↗
Artificial intelligence (AI) is increasingly applied to image-based and image-formatted geoscientific evidence across mineral exploration, from regional and surface surveys to drilling/core analysis and laboratory characterization. However, the literature remains distributed across different research communities, making it difficult to compare how visual evidence, AI tasks, methodological choices, and geological outputs relate across exploration contexts. This systematic mapping review searched the Web of Science Core Collection, Scopus, and IEEE Xplore for English-language journal articles pu…
- OpenAlex · Published · First archived
RCSB PDB AI Help Desk: retrieval-augmented generation for protein structure deposition support ↗
Abstract Motivation Structural biologists have contributed more than 260,000 experimentally determined three-dimensional structures of biological macromolecules to the Protein Data Bank. Incoming depositions are validated and biocurated by approximately 20 expert biocurators at the collaborating data centers worldwide. Biocurators at the Research Collaboratory for Structural Bioinformatics Protein Data Bank, who process more than 40% of global depositions, face growing difficulty sustaining efficient Help Desk operations, having received roughly 19,000 depositor messages across some 8,000 entr…
- OpenAlex · Published · First archived
LLMs can assist with proposal selection at large user facilities ↗
Abstract We explore how large language models (LLMs) can enhance the proposal selection process at large user facilities, offering a scalable, consistent, and cost-effective alternative to traditional human review. Proposal selection depends on assessing the relative strength among submitted proposals; however, traditional human scoring often suffers from weak inter-proposal correlations and is subject to reviewer bias and inconsistency. A pairwise preference-based approach is logically superior, providing a more rigorous and internally consistent basis for ranking, but its quadratic workload…
- OpenAlex · Published · First archived
MergeRepair: An Exploratory Study on Merging Task-Specific Adapters in Code LLMs for Automated Program Repair ↗
Abstract Large Language Models (LLMs) have shown high capabilities in several software development-related tasks such as program repair, documentation, code refactoring, debugging, and testing. However, training these models requires massive amount of data and significant computational resources. Adapters are specialized, small modules designed for parameter efficient fine-tuning of LLMs for specific tasks, domains, or applications without requiring extensive retraining of the entire model. These adapters offer a more efficient way to customize LLMs for particular needs, leveraging the pre-exi…
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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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