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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 ↗5938 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.
- GitHub · Repository created · First archived
Parcha-ai/agentrun ↗
The Agentrun Workflow DSL
- GitHub · Repository created · First archived
ilien-dev/quiron ↗
AI humanizer skill for Claude Code, Codex and Cursor. Removes AI slop and the signs of AI writing, then measures the rewrite against human baselines.
- OpenAlex · Published · First archived
Multi-Modal Deep Learning Approach for Comprehensive Pavement Condition Evaluation Using Vision-Language Models ↗
Timely and accurate assessment of pavement conditions is essential for roadway safety, infrastructure longevity, and cost-effective maintenance planning. Traditional methods rely heavily on manual inspections, which are labor-intensive, subjective, and limited in scalability. Recent advances in vision-language models (VLMs) offer a new opportunity to automate this process by integrating visual analysis with natural language generation. In this study, we propose a novel framework that leverages fine-tuned VLMs to automate pavement condition evaluation by analyzing visual inputs and generating d…
- OpenAlex · Published · First archived
Argument-Based Evaluation for AI Recommendations in E-Commerce Decision Support: A Toulmin–FCM Approach ↗
Artificial intelligence (AI) is increasingly being used to generate recommendations for e-commerce decision support. Existing XAI evaluation typically focuses on explanation attributes or user outcomes such as clarity, usefulness, trust, and acceptance but provides less insight into how an explanation justifies the recommendation itself. This study develops an argument-based evaluation framework by integrating Toulmin’s model of argumentation with fuzzy cognitive maps (FCMs). Toulmin’s model structures explanations through claim, ground, warrant, backing, qualifier, and rebuttal, while FCMs qu…
- OpenAlex · Published · First archived
White Blood Cell Classification with Data-Efficient Vision Transformer: A Multi-Dataset Evaluation on Raabin-WBC and PBC Repositories ↗
Traditional microscopic assessment of leukocyte morphology remains a labor-intensive workflow that is inherently vulnerable to inter-observer variability and subjective assessment risks. Automated classification systems are increasingly used as diagnostic aids for the identification and characterization of hematological samples. This study proposes a data-efficient transformer-based framework for white blood cell (WBC) classification under limited training data and computational constraints. The proposed system employs a DeiT-Tiny backbone with a transfer learning strategy. Contrast-Limited Ad…
- OpenAlex · Published · First archived
Whose story do we live? Experiential authorship threat in AI-mediated hospitality journeys and its consequences for meaning, memory, and engagement ↗
AI now functions as hospitality’s concierge, curating itineraries, wellness plans, and trip narratives at scale. While such systems boost efficiency and personalisation, they raise a symbolic concern central to hospitality: when algorithms pre-write the journey, do guests still feel like the authors of their own experiences? This research introduces and validates Experiential Authorship Threat (EAT), defined as the perceived loss of agency, ownership, and self-authored meaning when AI curation displaces the guest’s narrative role. Across six studies (scenario-based, embedded-field, and interfa…
- OpenAlex · Published · First archived
Validation, Calibration and Transparency, More than Accuracy: An Appraisal of Artificial Intelligence Against Antimicrobial Resistance and a Sustainable, Testable Diagnostic Architecture ↗
Artificial intelligence is widely proposed against antimicrobial resistance, but how much of that literature could support clinical deployment is unclear. We searched PubMed (2016–2026) across four domains, retrieving all 2446 records. Of 725 eligible studies, 80 were appraised at full text against the artificial-intelligence appraisal extensions (PROBAST+AI, TRIPOD+AI). Selection favoured externally validated work, so the validation figures are upper bounds. Output grew from 10 studies in 2016 to 142 in 2025; within the appraised set there was no evidence that genuinely external evaluation be…
- OpenAlex · Published · First archived
Dr. Contrarian: a persona-driven conversational AI agent for coaching argumentative reasoning in infectious disease classroom debates ↗
ABSTRACT Critical thinking and persuasive communication are essential for biology graduates navigating socioscientific dilemmas. While debate pedagogy fosters problem-solving, autonomy, competence, and relatedness aligned with adult learning and self-determination theories, inexperienced students often struggle with the cognitive load and anxiety from this format. To bridge this gap, “Dr. Contrarian,” a persona-based conversational AI agent, was developed to function as an adversarial coach during debate preparation. Survey data ( n = 21) adapted from the Technology Acceptance Model show that…
- OpenAlex · Published · First archived
Enduring Strengths of Knowledge-Based Methods in the Era of Machine Learning and Large Language Models: A Task-and-Architecture Perspective ↗
Large language models have shifted AI toward statistical learning, but knowledge-based methods remain essential for tasks governed by combinatorial structure, declarative correctness, strong domain priors, and auditable reasoning. This paper treats the issue as one of task–architecture fit rather than paradigm competition. It surveys evidence across twelve problem classes, from satisfiability, constraint solving, planning, and formal mathematics to formal argumentation, answer set programming, knowledge graphs, and interpretable rule- and concept-based models, reporting concrete benchmark resu…
- OpenAlex · Published · First archived
Artificial Intelligence Applications in Supply Chain Management: A Systematic Analysis of Technologies, Applications and Performance ↗
The growing complexity of supply chains has made it more important than ever to use smart technologis that can make operations better help with choices and make responses faster. Artificial Intelligence or AI has become a technology that is changing the way supply chains work. It uses data to look at things predict what might happen and automate tasks. This study looks at how Artificial Intelligence's used in Supply Chain Management and how it helps important parts of the supply chain. The study reviews the research that already exists on AI technologies like machine learning, predictive analy…
Live window recorded . This is the ingestion observation time, not the publication or discovery date of each source.
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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