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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 ↗5533 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
An Evidence-Based Knowledge Graph Framework with Large Language Models and Human-in-the-Loop for Ovarian Cancer Resistance ↗
The reciprocal relationship between large language models (LLMs) and knowledge graphs (KGs) opens new opportunities for knowledge discovery from scientific literature; however, most approaches address only one direction of this interaction. In complex biomedical domains, ensuring reliability, interpretability, and traceability remains challenging, making expert supervision essential. In this work, we propose a framework that integrates LLMs, KGs, and a Human-in-the-Loop scheme for constructing, evaluating, and reasoning over Evidence-based Knowledge Graphs (E-KGs), explicitly linking each extr…
- OpenAlex · Published · First archived
The role of artificial intelligence in the modern Russian economy innovative development ↗
Innovation is now not just one of the possible paths of development, but the main condition for the survival of any enterprise. The key factor that changes the paradigm of the innovation process is the ability to delegate routine operations. Previously, a significant portion of highly qualified employees’ time was spent on data collection and preliminary information processing, which considerably slowed down the research and development cycle. Modern technologies make it possible to completely delegate trivial tasks to intelligent systems, which are capable of processing such information much…
- OpenAlex · Published · First archived
AI-Driven Orchestration of AutoML-Based Demand Forecasting, Fleet Allocation, and Route Optimization in Last-Mile Delivery: A Single-Instance Case Comparison of Metaheuristic and Agentic AI Routing ↗
Background: Last-mile delivery couples demand forecasting, heterogeneous fleet allocation, and routing under time windows, yet these decisions are usually studied in isolation. The object of this study is the forecast-to-route decision pipeline of an urban parcel operator; we examine the hypothesis that no single routing paradigm dominates once solvers are evaluated under one shared feasibility-aware objective. Methods: We implement an orchestration architecture in which an AutoML supervisor selects transfer-learned recurrent backbones per zone, forecasts become Vehicle Routing Problem with Ti…
- OpenAlex · Published · First archived
A Stacking-Based Meta-Ensemble Hybrid Artificial Intelligence Framework for Soil Shear Strength Parameters Prediction ↗
Accurate estimation of soil shear strength parameters is essential for the safe and economical design of geotechnical structures; however, conventional laboratory testing is time-consuming, and individual machine learning models often exhibit limited generalization across heterogeneous soil conditions. This study proposes a stacking-based Meta-Ensemble Hybrid Artificial Intelligence framework for predicting cohesion (C) and angle of internal friction (ϕ) from routinely measured geotechnical properties. The proposed framework integrates ten complementary machine learning models through a meta-l…
- OpenAlex · Published · First archived
Artificial Intelligence Model’s assessment of intra-sample heterogeneity of HER2 IHC in breast cancer is related to interobserver and intraobserver agreement among pathologists ↗
Accurate assessment of HER2 status in breast cancer has been critical for guiding therapy and has become even more important with the emergence of antibody–drug conjugates, now also indicated in HER2-low tumors. However, inter- and intraobserver variability limits the reproducibility of HER2 IHC scoring among pathologists. Artificial intelligence (AI) models offer potential to standardize and improve diagnostic accuracy and bring new insights into current practices shortcomings. We conducted a study recruiting generalist and specialist pathologists from Rede D’Or centers across Brazil to asses…
- OpenAlex · Published · First archived
Spatial cognitive patterns of underground public spaces from a stimulus-organism-response perspective: evidence from LLM-empowered social media data mining ↗
Current underground public space (UPS) renewal prioritizes engineering-oriented physical upgrades, neglecting users’ multidimensional cognitive experiences. Existing studies on underground space perceptions rely on small-sample surveys and fail to capture joint co-occurrence patterns among multiple stimuli. This study integrated the Stimulus-Organism-Response framework with large language models to decode spatial cognitive patterns. It drew on 76,078 social media reviews from 64 UPSs in 19 Chinese cities (2005–2024). DeepSeek extracted 27 spatial stimuli and their sentiments, serving as public…
- OpenAlex · Published · First archived
B-INN: an invertible neural network framework for credential-bound image ownership verification ↗
The rapid expansion of image datasets for artificial intelligence has increased ownership disputes caused by unauthorized collection, redistribution, and reuse. Conventional watermarking and steganographic methods can embed identifying information into images, but successful extraction alone may not be sufficient for ownership verification when the extraction model or embedded evidence is obtained by an unauthorized party. This article proposes B-INN, a credential-bound image ownership verification framework that combines Invertible Neural Network-based data hiding with blockchain-managed Veri…
- OpenAlex · Published · First archived
Conformational heterogeneity and vibrational signatures of dAMP in aqueous solution revealed by high-accuracy machine-learning potentials ↗
Abstract Nucleotides are the fundamental building blocks of life, yet many aspects of their behavior and interactions in water remain poorly understood. Here, we investigate the structural and vibrational properties of $${2}^{{\prime} }$$ 2 ′ -deoxyadenosine $${5}^{{\prime} }$$ 5 ′ -monophosphate (dAMP) in aqueous solution using a machine-learning approach supported by IR spectroscopy experiments. We develop highly accurate neural network potentials through an active-learning protocol that systematically samples the free-energy landscape at the GGA level and efficiently transfers this informat…
- OpenAlex · Published · First archived
Lost in the comments: Social context as a single‐pass jailbreak and defense on agentic platforms ↗
Abstract As LLM‐based agents move onto multiagent platforms, they process direct prompts and surrounding social context—posts, comments, and upvotes—an attack surface that single‐turn alignment never addressed. We built a simulation of Moltbook, a social network for AI agents, and a pipeline turning 100 JailBreakBench goals into platform‐native posts with bystander comments of three valences: aggressive, ethical, and measured. Platform‐context reformatting alone raises the attack success rate (ASR) of GPT‐4o‐mini from 7% to 71%, beating a twenty‐query optimization baseline in one pass. Valence…
- OpenAlex · Published · First archived
Prediction of antimicrobial resistance of Klebsiella pneumoniae through machine learning and revelation of significant role of insertion sequence elements ↗
Klebsiella pneumoniae (K. pneumoniae) poses critical therapeutic challenges due to multidrug resistance (MDR). While antibiotic resistance genes (ARGs) are primary determinants, their expression and phenotypic impact are modulated by mobile genetic elements such as insertion sequence (IS) elements. However, the contribution of IS elements to antimicrobial resistance (AMR) prediction remains largely unexplored in K. pneumoniae. This study aimed to systematically evaluate whether integrating IS elements as genomic features improves machine learning-based AMR prediction in K. pneumoniae and to ch…
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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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