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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 ↗5515 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
ClaimArena-LLM: Auditing Adversarial Propagation of False Causal Attributions in Multi-Agent LLM Debates ↗
Background: Multi-agent large language model (LLM) systems increasingly rely on structured interaction, debate, and final model-based evaluation, yet an unsupported causal attribution introduced by one participant may propagate through the interaction and influence subsequent decisions. This study introduces ClaimArena-LLM, a controlled framework for auditing adversarial propagation, evidentiary provenance, and final-stage evaluator vulnerability in multi-agent causal reasoning. Methods: ClaimArena-LLM uses 100 synthetic causal incident cases with predefined evidence-supported and false causal…
- OpenAlex · Published · First archived
Transformer architecture incorporating discrete wavelet transform for fully automated baseline correction of near-fault ground-motion records ↗
Baseline correction of near-fault ground motions (NFGMs) is essential for characterizing permanent displacement and pulse-like features, but remains challenging because low-frequency noise must be removed without distorting physically meaningful components in integrated velocity and displacement histories. Conventional correction methods rely heavily on manually selected parameters or time windows, whereas deep-learning methods are limited by inadequate representation of noise–pulse interactions and synthetic datasets that often neglect pulse-like characteristics. This study proposes a fully a…
- OpenAlex · Published · First archived
Physically inspired geometry-to-photometry rendering and lightweight convolutional neural network for real-time weld defect detection ↗
Welding quality inspection of battery top covers is critical to lithium-ion battery safety and reliability. The height deviation between weld defects and normal areas is extremely small; on specular, weakly textured surfaces it produces no stable brightness contrast, so two-dimensional (2D) images carry almost no usable defect signal. Three-dimensional (3D) sensing captures such deviations but incurs computational costs prohibitive for production lines. Raw depth maps, as scalar grayscale fields, cause 2D convolutional networks to overlook tiny-defect features. Addressing all three limitations…
- OpenAlex · Published · First archived
How Does AI Literacy Relate to Pre-Service STEM Teachers’ AI Readiness? Insights from Mediational Analysis ↗
Efforts to achieve widespread applications of AI in education depend, to a large extent, on teachers’ level of AI literacy (AIL), AI self-efficacy (AISE), Artificial Intelligence Technological Pedagogical Content Knowledge (AI-TPACK), and above all, the teachers’ readiness for AI-based learning. This study, therefore, examined AI literacy’s links with pre-service STEM teachers’ AI readiness (AIR), focusing majorly on the mediational roles of AISE and AI-TPACK. A quantitative research approach, specifically a cross-sectional correlational survey, was employed and was led by six null hypotheses.…
- OpenAlex · Published · First archived
Unified Graph Convolutional Neural Network with PPMI-Informed Graphs and MuRIL Embeddings for Hate Speech Detection in Indian Code-Mix Text ↗
The widespread use of social media has led to a rise in online bullying and abusive language. Huge amounts of content are created on social media daily, creating a challenge for platforms to manually detect and remove offensive posts, making automated detection systems essential. This challenge is even greater in multilingual countries such as India, where code mixing is prevalent. This paper investigates the effectiveness of graph-based representations for hate speech and offensive (HOS) language detection in social media posts written in code-mixed low-resource languages. We conducted experi…
- OpenAlex · Published · First archived
RAG-ReAct empowered large language models for localized intelligent bridge operation and maintenance: a knowledge-driven agentic approach ↗
Bridges serve as critical transportation infrastructure hubs. The recent breakthroughs in large language models (LLMs) offer transformative potential for intelligent bridge operation and maintenance (O&M), but their direct application is hindered by domain-specific hallucination, black-box reasoning opacity, and passive response limitations. This manuscript proposes a knowledge-driven, edge-side bridge O&M agent based on LLMs, addressing those critical limitations of general-purpose LLMs in specialized structural health monitoring (SHM) domains. The methodology centers on three core technical…
- OpenAlex · Published · First archived
Investigating the effect of natural aggregate on asphalt mixture performance by integrating experimental evaluation and explainable machine learning ↗
Abstract Natural aggregate (NA) is commonly incorporated into asphalt mixtures as part of the aggregate skeleton; however, excessive NA content may adversely affect mechanical strength and deformation resistance. This study investigates the influence of NA content on asphalt mixture performance using an integrated experimental, statistical, and explainable machine learning (XML) framework. Asphalt mixtures were prepared using crushed coarse aggregate with varying NA contents of 0%, 4%, 8%, 12%, 16%, 20%, and 24% by weight. The experimental program included Marshall stability and flow, indirect…
- OpenAlex · Published · First archived
Artificial Intelligence-Based Quality Control of Pharmaceutical Tablets: A Visual Dataset and Benchmark for Automated Defect Detection ↗
Background/Objectives: Automated visual inspection of pharmaceutical tablets is increasingly important for detecting manufacturing defects and maintaining consistent product quality. Progress in artificial intelligence-based defect classification is, however, constrained by the scarcity of publicly accessible image datasets covering defective tablets under diverse visual conditions. This study introduces the PharmaVision (PV) dataset, a public visual dataset and benchmark for classifying normal and defective compressed tablets. Methods: The PV dataset contains 640 smartphone-acquired images sp…
- OpenAlex · Published · First archived
Knowledge Evolution and Paradigm-Level Change in AI-in-Education Research: A Scientometric Mapping and Critical Review (2015–2025) ↗
Bibliometric commentary increasingly frames the rise of generative artificial intelligence (AI) as a paradigm shift in AI-in-education (AIED) research, yet rarely distinguishes publication growth from genuine reconfiguration of the knowledge base or of teacher–learner roles. This study maps knowledge evolution in AIED from 2015 to 2025 by integrating four analytical strands across Web of Science and Scopus: scientometric mapping (including keyword and reference bursts and co-citation analysis), structural topic modeling (STM), design-weighted coding of full texts, and triangulation across thes…
- OpenAlex · Published · First archived
WeatherAgent-SAM: Text-Grounded Agentic Tool Selection for Robust SAM Segmentation ↗
The Segment Anything Model (SAM) provides a versatile promptable segmentation interface, but adverse weather and image degradation can obscure visual cues and substantially reduce its accuracy. Existing approaches mainly follow two directions to mitigate this issue. The first adapts SAM through fine-tuning or additional trainable modules, which requires model-side optimization. The second preprocesses degraded images before SAM inference, but such methods are generally optimized for perceptual restoration rather than downstream segmentation. We introduce WeatherAgent-SAM, an agentic framework…
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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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