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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 ↗6057 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
Ontology-Guided Multimodal Large Language Model for Semantic Recognition of Ship Navigation Behaviors ↗
Automatic Identification System (AIS) data provide sequential motion observations for ship-behavior analysis, yet high-level navigational behavioral semantics cannot be reliably interpreted from low-level motion features alone, especially considering ship-environment spatial interactions and their temporal dependencies. To mitigate this limitation, this study proposes an ontology-guided multimodal large language model for semantic recognition of ship navigation behaviors. Built on a three-layer hierarchical behavioral model, the approach unifies motion states, spatial interactions, and tempora…
- OpenAlex · Published · First archived
The NERDD microservices platform for deploying machine learning models in drug discovery ↗
Machine learning models are transforming data-driven research across scientific disciplines, yet their deployment as accessible and reliable web services remains a significant challenge. We introduce the NERDD microservices platform, a scalable, maintainable, and secure framework designed to support the sustainable delivery of machine learning services in cheminformatics, bioinformatics, and related domains. Built in Python, NERDD provides a modular infrastructure for deploying predictive and generative models, with native support for chemistry-specific tasks such as molecular property predict…
- OpenAlex · Published · First archived
Adaptive Neural Network-Based Virtual Flux Estimation for Sensorless Deadbeat Control of Grid-Connected Modular Multilevel Converters ↗
This paper presents a grid-voltage-sensorless control scheme for a grid-connected modular multilevel converter based on an adaptive-linear-neuron quadrature signal generator for virtual-flux estimation (VF-ADALINE-QSG). The proposed estimator identifies the DC offset component and extracts the in-phase and quadrature fundamental components online without requiring offline training or specific initialization of the adaptive weights. By reconstructing VF exclusively from the identified fundamental components, the proposed method limits drift induced by DC offset and attenuates higher-order harmo…
- OpenAlex · Published · First archived
LLM‐guided semantic‐aware trajectory optimization for UAV path planning using environmental context ↗
Summary Autonomous unmanned aerial vehicle path planning in urban environments requires both geometric safety and semantic awareness. Conventional planners based on fixed costs or predefined risk models struggle to reflect mission‐dependent semantic priorities. We present a large language model (LLM)‐guided semantic‐aware planning framework in which the LLM serves as a bounded high‐level decision module. OpenStreetMap data are transformed into class‐wise semantic potential fields, and a route‐relative context and natural‐language mission description are used to infer validated semantic modulat…
- OpenAlex · Published · First archived
Artificial intelligence in the English classroom: EFL teachers’ opportunities, challenges, and readiness for AI integration ↗
The rapid integration of artificial intelligence (AI) tools into English language education has created new demands on teachers, who must navigate both the potential and limitations of these technologies. This exploratory descriptive survey examines English as a Foreign Language (EFL) teachers’ perceptions of AI tools, focusing on usage practices, perceived opportunities, challenges, and professional development needs. Drawing on the Technology Acceptance Model and an AI-TPACK perspective, a questionnaire was distributed to 28 EFL teachers working across multiple educational levels and interna…
- OpenAlex · Published · First archived
Vision–Language Model (VLM)-Based Interactive Visual Querying for Construction Ergonomic Risk Reasoning ↗
Construction workers are frequently exposed to postural ergonomic risks, yet conventional AI-based ergonomic risk assessment (ERA) systems generally provide risk classifications or scores rather than interpretable and interactive explanations. This study investigates whether domain adaptation can improve a generic vision–language model’s ability to identify and explain construction ergonomic risks from images. A dataset with 1900 construction image–text pairs was curated. The domain-adapted model was compared with the baseline model. Performance was evaluated using visual question answering (V…
- OpenAlex · Published · First archived
Automated Bergmann-Kliesch Score assessment in testicular biopsies using artificial intelligence-based whole-slide image analysis: a retrospective monocentric development and validation study ↗
Abstract Background The Bergmann-Kliesch Score is a histopathological grading system used to evaluate spermatogenesis in testicular biopsies, quantifying the fraction of seminiferous tubules containing elongated spermatids. It serves as a key parameter in the clinical process of men with non-obstructive azoospermia (NOA) undergoing testicular sperm extraction (TESE) for subsequent intracytoplasmic sperm injection (ICSI). Manual Bergmann-Kliesch Score assessment is time-consuming and subject to inter- and intraobserver variability. This study aimed to develop and validate an artificial intellig…
- OpenAlex · Published · First archived
A Mixed Formulation Physics-Informed Neural Network for a Forward and an Inverse Euler–Bernoulli Dynamic Beam Problem ↗
A mixed formulation physics-informed neural network (PINN) for solving a forward and an inverse nonlinear Euler–Bernoulli dynamic beam problem is proposed. This approach avoids the computation of fourth-order spatial derivatives in the loss function, thereby mitigating training instability and allowing seamless integration of dynamic forward and inverse problems. A sensitivity analysis is provided for the investigation of the uniqueness of the solution in the inverse problems. The developed programming code is based on open-source Python software 3.13 for deep learning. The impact of the struc…
- OpenAlex · Published · First archived
Evaluating Safety Embedding Prefiltering for Analyzing Millions of LLM Agent Social Network Messages for Security and Safety Harms ↗
Agentic social networks expose autonomous agents to large volumes of potentially malicious content, including prompt injection, social engineering, and unsafe execution requests. Large Language Model (LLM) judges can detect such content, but applying them at platform scale is expensive. We study the extent to which lightweight embedding-based prefilters can reduce this cost while retaining most of the judge’s unsafe predictions. We annotate 10,000 Moltbook posts and comments with a frontier LLM judge, assigning a binary safety verdict, severity level, malicious intent taxonomy labels, and Open…
- OpenAlex · Published · First archived
PRISM-LoRA: Principal-Direction-Guided Single-LoRA Merging Framework for Continual Learning of Large Language Models ↗
Continual learning of large language models requires incorporating new knowledge while limiting catastrophic forgetting; however, many low-rank adaptation (LoRA)-based methods retain task-specific modules or separate learning spaces that grow with the task sequence. We propose PRISM-LoRA, a replay-free framework that repeatedly trains a single LoRA module, merges its update into the backbone, and reinitializes it for the next task. Unlike LoRA-based continual-learning approaches that retain task-specific modules or allocate separate task-specific learning spaces, PRISM-LoRA uses the directiona…
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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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