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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 ↗5498 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
Foundations for Investigations of Agentic AI Systems from the Forensic Analysis of OpenClaw ↗
Agentic AI systems are increasingly deployed as personal assistants and virtual employees, and are likely to become a common object of digital investigations and incident response. Yet little is known about how their internal state and actions can be reconstructed during forensic analysis, and systematic forensic approaches remain largely unexplored. We present the first empirical forensic study of a personal AI assistant, with OpenClaw as a case study. We examine its technical design via static code analysis and apply differential forensic analysis to identify recoverable traces across stages…
- OpenAlex · Published · First archived
Tool-grounded large language models for decision-level fusion in fault diagnosis ↗
Modern condition-monitoring systems generate heterogeneous information, such as high-rate sensor streams, derived health indicators, and discrete event histories, that must be combined into actionable diagnostic statements. Large language models (LLMs) are attractive as a decision-level fusion mechanism because they natively handle the textual modality, but their use in safety-critical fault diagnosis is impeded by hallucinated numerical content and stochastic outputs. This paper proposes a two-level information-fusion framework that uses an LLM as a constrained fusion operator rather than as…
- OpenAlex · Published · First archived
Effect of features burdened with classical error, misclassifications, Berkson error, and group-summary assignments on machine learning performance and interpretability ↗
Abstract Measurement error is widespread in modern data pipelines, but its influence on predictive modeling and variable importance assessments in machine learning is often underestimated. Our study examines how input measurement error affects both prediction accuracy and interpretability across four common additive error types: classical error, misclassification of predictive features, Berkson error, caused by algorithmic or model-based assignment errors, and group-summary (aggregation) assignment errors. Using controlled Monte Carlo simulation experiments that cover regression and classifica…
- OpenAlex · Published · First archived
Explainable machine learning for shear capacity prediction of reinforced concrete deep beams ↗
The shear capacity prediction of reinforced concrete (RC) deep beams remains a challenging issue in design of concrete structures. Although the code-based strut-and-tie method is widely accepted, it is often time-consuming for routine design applications, while existing empirical equations are unable to fully capture the highly nonlinear interactions among material properties, geometric characteristics, and reinforcement variables. To address this limitation, this study proposes an explainable machine learning framework for predicting the shear capacity of RC deep beams. A comprehensive databa…
- OpenAlex · Published · First archived
Selecting and generating web search based features using small language models for tabular data classification ↗
In machine learning, the feature space is a key framework that converts data samples into vectors for analysis and modelling. Consequently, the results obtained from machine learning algorithms depend heavily on the ability to select and generate important features. Feature generation as a central technique converts raw data into a refined feature space that facilitates model training and further enhances its structure. Despite progress in automated feature selection and feature generation, major challenges remain, especially in terms of explainability and cross-domain generalization. One alte…
- OpenAlex · Published · First archived
Multiobjective optimization of foamed concrete for sustainable construction using statistical and artificial intelligence approaches ↗
Abstract Foamed concrete is lightweight and insulating, but its mix design is a headache. Strength, durability, and carbon footprint all fight each other. This review looks at how researchers have tried to optimize these trade-offs using methods like RSM, Taguchi, ANN, and genetic algorithms. RSM is easy to interpret but struggles with nonlinear foam behavior. AI models handle nonlinearity better but need lots of data and tell you nothing about why they work. The best path forward seems to be hybrid approaches. On the environmental side, cement drives most of the carbon. Replacing it with SCMs…
- OpenAlex · Published · First archived
Self-regenerating photocatalytic water treatment systems: integrating cognitive AI control, bio-inspired self-healing, and thermoelectric energy harvesting ↗
Conventional photocatalytic water treatment systems face persistent limitations in terms of catalyst deactivation, energy consumption, and adaptability to variable influent quality. This review examines the integration of three emerging technological domains, machine learning driven process optimization, bio-inspired self-healing material strategies, and thermoelectric energy recovery, as a framework for developing more autonomous and resource-efficient photocatalytic treatment systems. A systematic literature search was conducted in Web of Science, Scopus, IEEE Xplore, and PubMed (January 200…
- OpenAlex · Published · First archived
Assessment of AI development level in construction cost management using AHP- cloud model: a case study of China’s Yangtze River Delta Region ↗
Artificial Intelligence (AI) integration is crucial for solving traditional inefficiencies and data fragmentation in construction cost management, driven by the demands of Industry 4.0. However, a significant gap exists in holistically evaluating AI development levels, lacking a framework and empirical analysis that systematically bridges macro-environmental factors and micro-organizational readiness. This study aims to assess the AI development level in construction cost management across China’s Yangtze River Delta (YRD) region. A novel PEST-O (political, economic, social and technological-o…
- OpenAlex · Published · First archived
Making Large Language Models More Flexible to Changing Contingencies through Tracking Induction ↗
Abstract Recent research has shown that large language models (LLMs) exhibit rule-based insensitivity to contingencies, a functional analog of a well-documented phenomenon in human behavior. The present study extended these findings by examining whether tracking inductions can attenuate this insensitivity. Six frontier LLMs (GPT 5.2, Gemini 3 Flash, Grok 4.1 Fast, DeepSeek V3.2, Qwen3 235B, and Kimi K2) completed a modified rock-paper-scissors paradigm with manipulated outcome probabilities in a 3 (Rule: No Rule, Correct Rule, Incorrect Rule) × 3 (Induction: No Induction, Direct Tracking, Meta…
- Europe PMC · Published · First archived
GLARE: Genomic Language model for Automated Repetitive Element classification ↗
Reliable classification of transposable elements (TEs) is important for genome annotation and evolutionary analysis, yet remains challenging: TE families differ widely across taxa, reference databases label them inconsistently, and many TEs are represented only by degraded copies. We introduce GLARE (Genomic Language model for Automated Repetitive Element classification), a TE classifier built by fine-tuning a genomic language model on harmonized PanTEon and Repbase reference data to predict TE superfamily across 32 superfamilies in 11 orders. Among the genomic language models compared, Nucleo
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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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