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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 ↗6055 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
Hybrid Physics-Informed Machine Learning via residual fitting for the prediction of creep and shrinkage in concrete ↗
Abstract Recent works about data-driven analysis of concrete creep and shrinkage have enhanced structural analysis, providing more efficient results compared to standard models. Moreover, recent studies have shown strong efficiency when physics laws are coupled with machine learning (ML), an approach called physics-informed machine learning (PIML). In this paper, the purpose is to implement Hybrid PIML via residual fitting to enhance standard ML models for predicting creep and shrinkage in concrete structures. While purely data-driven ML models offer high accuracy, they frequently lack physica…
- OpenAlex · Published · First archived
Consensus Ensembles for Zero-Shot Algorithm Classification with Large Language Models ↗
Manual classification of algorithmic problems is costly and inconsistent, although it remains essential for curriculum sequencing, recommendation systems, and quality control on competitive programming platforms. To address this limitation, this study investigates whether consensus-based, zero-shot classification using large language models can replace manual annotation. Five architecturally diverse models were evaluated: Claude Sonnet v4.6 (CS4.6), DeepSeek v3.2 (DS3.2), Gemini v3 Flash (G3F), GPT v5.4 (GPT5.4), and Grok v4.1 Fast (GRK4.1). Each model independently classified 2911 LeetCode pr…
- OpenAlex · Published · First archived
LeakAgent: a multi-agent system for leak detection using multi-modal large language model coordination in water distribution network ↗
Water distribution networks (WDNs) suffer from severe leakage, yet pinpointing the leaks remains difficult. Data-driven detection is typically split into disconnected stages of hydraulic modelling, partitioning, sensor planning and analysis, and it treats the questions of whether a leak exists and where it lies as separate problems, which limits accuracy and transfer across networks. Here we show that these tasks can be unified within a multi-agent system (MAS) coordinated in natural language by a large language model (LLM). A single hydraulic pressure-sensitivity field, computed once, informs…
- OpenAlex · Published · First archived
Perceptions of AI-assisted learning and learner development among Moroccan secondary school students ↗
The rapid proliferation of artificial intelligence (AI) tools in education has fundamentally altered traditional learning paradigms, presenting both unprecedented opportunities for personalized support and novel academic challenges. This study aims to examine secondary school students’ perceptions of the influence of AI-assisted learning on perceived learning outcomes, motivation, self-regulated learning, and digital literacy, while also exploring the perceived benefits and challenges associated with AI use in the Casablanca-Settat region of Morocco. Employing a mixed-methods design, quantitat…
- OpenAlex · Published · First archived
Thermo-vibrational fusion for early-stage ball bearing fault diagnosis: a multi-modal machine learning framework ↗
Abstract Rolling bearing failures account for 40–50% of rotating machinery breakdowns, but the complementary diagnostic potential of thermal sensing remains underutilized. This study presents a thermo-vibrational fusion framework for early-stage ball bearing fault diagnosis using the KAIST run-to-failure dataset (128 h, NSK 6205 bearing). A 40-dimensional feature vector comprising FFT-based spectral features at the bearing defect frequencies (BPFO, BPFI, BSF), sub-band energy ratios, spectral entropy, and six time-domain statistical descriptors across all four sensor channels is extracted. Thr…
- OpenAlex · Published · First archived
Cascaded machine learning-based neural Model Predictive Control for real-time thermal management of a sCO2 serial coaxial all-glass evacuated solar collector ↗
There is a growing imperative for real-time thermal energy management in solar collectors to ensure system stability and facilitate integration with advanced technologies, such as Carnot batteries. To address this need, the present study proposes a Cascading Feedforward Neural Network (CFNN)–Model Predictive Control (MPC) framework, which enables the prediction and control of complex nonlinear thermal behavior under soft constraints on useful heat gain and exergy gain. A preliminary evaluation of a series-configured, all-glass coaxial evacuated tube collector using supercritical CO 2 was perfo…
- OpenAlex · Published · First archived
Benchmark-Driven Selection of AI Can Improve Capabilities of Reasoning Language Models in Epistemology ↗
Abstract Evaluation of reasoning language models gained importance after it was observed that they can combine their existing capabilities into novel traces of intermediate steps before task completion and that the traces can sometimes help them to generalize better than past models. We show that better performance results not only from test time algorithmic improvements or model sizes but also from letting tasks from impactful benchmarks inspire curricula for learning. We call this benchmark-driven selection of AI and show its effects on DeepSeek-R1, the first open-weight large reasoning mode…
- OpenAlex · Published · First archived
Large Language Models for Code Generation from Multilingual Prompts: A Curated Benchmark and a Study on Code Quality ↗
Large Language Models (LLMs) perform differently on identical tasks when prompted in different languages—a phenomenon known as language bias. Although well-documented for general text generation, the extent to which language bias affects code generation quality and programming conventions remains largely unexplored. To address this gap, we investigate how the natural language used to describe programming tasks influences the quality of source code generated by three prominent LLMs: GPT-4o mini , DeepSeek, and Claude. Our study includes 460 coding tasks spanning Python (230 tasks) and Java (230…
- OpenAlex · Published · First archived
Toward AI-agent-driven particle transport simulations: implementation of AI-assisted workflows for PHITS ↗
Monte Carlo particle transport codes are powerful tools, but their use requires substantial knowledge of input preparation, execution, and result analysis. In this study, we present a code-side strategy for applying existing AI assistants and AI agents to PHITS. Two complementary sets of AI-ready resources were prepared from manuals, lecture materials, sample inputs, utility information, and developer-curated cautions: a bundled knowledge base for retrieval-augmented generation (RAG)-based assistants and a compact agent reference for direct use by AI agents. The knowledge base was loaded into…
- OpenAlex · Published · First archived
GrIT: Group Informed Transformer for Sequential Recommendation ↗
Sequential recommender systems aim to predict a user's future interests by extracting temporal patterns from their behavioral history. Existing approaches typically employ transformer-based architectures to process long sequences of user interactions, capturing preference shifts by modeling temporal relationships between items. However, these methods often overlook the influence of group-level features that capture the collective behavior of similar users. We hypothesize that explicitly modeling temporally evolving group dynamics alongside individual user histories can significantly enhance ne…
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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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