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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 ↗5969 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
A semantic layer architecture for removing schema identifiers from external LLM prompts, with model-dependent accuracy trade-offs ↗
Abstract The adoption of large language model (LLM)-based AI systems in credit rating agencies introduces a structural tension between operational efficiency and data security. This study examines a Semantic Layer as a technical mechanism for reducing this tension, and shows that whether it removes designated schema identifiers from the LLM-facing prompt depends on an implementation detail invisible to a schema-level analysis: whether the concept-to-SQL mapping is itself withheld from the LLM. An initial implementation (v1) includes this mapping in the prompt and removes none of the designated…
- OpenAlex · Published · First archived
Machine learning and SHAP-based explainability for accident severity prediction: evidence from a strategic road network in a developing country ↗
OBJECTIVE: This study aims to develop an Explainable Artificial Intelligence (XAI) framework to classify and predict road accident severity on a strategic road network in a developing country. The research focuses on identifying the primary risk factors contributing to fatal and serious injuries to support "Vision Zero" policies. METHODS: Three machine learning algorithms-Random Forest (RF), XGBoost, and Artificial Neural Networks (ANN)-were trained using a dataset of traffic accidents from federal highways in Pernambuco, Brazil. The study specifically addressed data imbalance by comparing mod…
- OpenAlex · Published · First archived
Causal-Chemprop: Causal Machine Learning for Molecular Property Prediction and Design ↗
Abstract A priori estimation of molecular properties has long been a topic of immense interest to the pharmaceutical sciences for molecular design. While neural network-based models have achieved high predictive accuracy, they have limited utility in molecular design, particularly in small and out-of-distribution data regimes. Current neural network-based models lack mechanisms to explicitly incorporate prior knowledge from existing data. Herein, we introduce a causal machine learning framework built on the Chemprop and DAGMA architectures for molecular property prediction called Causal-Chempr…
- OpenAlex · Published · First archived
Invisible Infrastructure at Risk: Funding Crises Threaten Global Bioscience Research ↗
Modern biology depends on invisible infrastructure that is essential yet precariously funded. Millions of biological observations are interpreted through resources that sit beneath routine scientific practice, largely unseen in everyday research. Every experiment, genome analysis, AI model, and clinical interpretation relies on these biological knowledgebases, which most researchers rarely notice until they disappear. That infrastructure is now under threat. Across the world, the biological databases that underpin modern research, built over decades of public and charitable investment, face se…
- OpenAlex · Published · First archived
From social media to artificial intelligence: tactics used to lure and exploit victims of child trafficking and for curbing child trafficking in Kenya ↗
Child trafficking is increasingly mediated through social media, artificial intelligence (AI) and other digital technologies, creating new opportunities for exploitation while strengthening digital prevention and protection. This study examines how digital platforms both facilitate and disrupt technology-enabled child trafficking in Kenya through a digital ethnography that combines social media observations, stakeholder interviews, focus group discussions and analysis of online anti-trafficking discourse. Guided by the Routine Activity and Situational Prevention (RASP) framework, the study inv…
- OpenAlex · Published · First archived
LoRA based instruction fine tuning of large language models for agronomic triple extraction and GraphRAG knowledge retrieval ↗
Due to massive growth in the unstructured agricultural data generated from reports, research articles, news, farmers’ feedback, experts’ advice, etc, there is a need to utilize this information efficiently for knowledge representation and decision-making. There are multiple techniques where information extraction and managing it in a structured manner can be less challenging. LLMs are used widely for extracting meaningful information in the way the user instructs. More precisely, LLMs, when fine-tuned on domain-specific data, e.g, agricultural data, help to retrieve information promptly. We pr…
- OpenAlex · Published · First archived
RAC-LLM: retrieval-augmented large-small model collaboration for cloud-assisted web asset risk assessment and homologous discovery ↗
Cloud-assisted cyber-asset monitoring services must continuously assess web assets that rapidly change domains, content, and page structure, making new variants difficult to evaluate and trace. Large language models can interpret heterogeneous and ambiguous security evidence, but their decisions benefit from bounded and verifiable context. Lightweight modules can efficiently retrieve evidence and construct relations across large asset collections, but they cannot resolve every semantic ambiguity. We present RAC-LLM, a retrieval-augmented large-small model collaboration method for web asset ris…
- OpenAlex · Published · First archived
Beyond Code Generation: Reassessing Software Quality in AI-Assisted Development ↗
Abstract Generative Artificial Intelligence (genAI) is fundamentally transforming software engineering practices by shifting code production from deterministic, human-authored processes to AI-assisted development workflows characterised by probabilistic behaviour, prompt sensitivity, and increased process complexity. This transformation entails significant challenges for software quality evaluation, as traditional metrics, developed under the assumptions of determinism and reproducibility, are increasingly insufficient for assessing AI-assisted development. This position paper synthesises rece…
- OpenAlex · Published · First archived
Cost-Aware and Self-Validating Multi-Agent Engineering of Computer-Vision Pipelines: An Optimization-Based Framework with Applications to Remote-Sensing Image Analysis ↗
Remote-sensing image analysis increasingly requires workflows that combine heterogeneous sensors, geospatial preprocessing, deep-learning models, uncertainty estimation, and domain-specific quality control. Conventional computer-vision pipelines are commonly designed manually, making them expensive to develop, difficult to reproduce, and inflexible when sensor quality or task requirements change. Recent agentic-AI systems have demonstrated the feasibility of automatically planning and executing computer-vision and AutoML workflows, but computational cost, geospatial validity, recovery behavior…
- OpenAlex · Published · First archived
Transformer-based deep learning framework for network intrusion detection with context-aware traffic pattern modeling ↗
This paper introduces a context aware transformer based network intrusion detection framework using publicly available benchmark traffic data. The framework introduces three domain-specific modifications to the standard transformer encoder, a partitioned multi-head attention mechanism with separate intra-group and inter-group heads that model dependencies within and across semantic feature groups, a learned positional encoding over feature indices initialized with group-identity vectors, and a gated aggregation module that selectively weights encoder outputs before classification. Evaluation E…
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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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