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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 ↗5944 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.
- Hacker News · Posted on Hacker News · First archived
Australia says OpenAI agent hacked into government website ↗
Hacker News discussion.
- Hacker News · Posted on Hacker News · First archived
AP: Avoid language that gives [AI] human characteristics ↗
Hacker News discussion.
- Hacker News · Posted on Hacker News · First archived
Feds Target AI Critics as "Foreign Agents" ↗
Hacker News discussion.
- Hacker News · Posted on Hacker News · First archived
AI-CAD: An OSS Multi-Agent Harness for Mech. Eng. CAD ↗
Hacker News discussion.
- OpenAlex · Published · First archived
Experimental and machine learning investigation of flexural strength and failure mechanisms in EPS-bottom ash-blast furnace slag lightweight geomaterials ↗
The flexural performance of a new lightweight geomaterial (LWG) made of bottom ash (BA), expanded polystyrene beads (EPS), blast furnace slag (BFS) and ordinary Portland cement (OPC) was studied. Five EPS–BA mix ratios (0.2–1.0), three BFS contents (4%, 8% and 12%), two cement contents (20% and 30% of the BA weight) and three curing durations (7, 14 and 28 days) were considered for the experimental program. The flexural strength tests were performed to quantify the resistance to bending and subsequently polynomial regression, random forest regression and gradient boosting regression models wer…
- OpenAlex · Published · First archived
Mixture replication in benchmark datasets inflates reported machine learning performance and degrades prediction interval coverage for ultra-high-performance concrete ↗
Abstract Machine learning models for ultra-high-performance concrete (UHPC) commonly report coefficients of determination above 0.94 on held-out records, and these values now shape expectations for data-driven mix design. We show that a substantial part of this reported predictive performance depends on how the models are evaluated. Benchmark UHPC datasets contain replicate records, meaning the same mixture tested at several curing ages or specimen sizes. Random train-test splitting distributes these replicates across both sides of the split, so models are scored partly on mixtures encountered…
- OpenAlex · Published · First archived
Machine learning framework for short- and medium-term building energy consumption forecasting ↗
Accurate forecasting of building energy consumption is crucial for increasing energy efficiency, enabling demand-side management, and improving decarbonization efforts. However, building load profiles are highly variable, impacted by operational schedules, tenant behavior, and weather conditions, limiting the accuracy of conventional forecasting systems. This study rigorously compares six forecasting approaches—Long Short-Term Memory (LSTM), Artificial Neural Networks (ANN), k-Nearest Neighbors (KNN), Extreme Gradient Boosting (XGBoost), Autoregressive Integrated Moving Average (ARIMA), and Se…
- OpenAlex · Published · First archived
A Tool-Augmented Agentic AI Pipeline for Reliable Circuit-Analysis Tutoring with Local Language Models ↗
LLM-based systems used for tutoring can produce fluent and apparently plausible explanations, but they do not guarantee Technical Correctness, which is paramount in STEM fields such as Electrical Circuit Analysis. This study investigates how three LLM-based response systems—parametric model knowledge (Pure LLM), Retrieval-Augmented Generation (RAG) and Agentic AI—affect response quality in the field of Electrical Circuit Analysis. It also introduces the HALO Agentic AI Pipeline, a local neuro-symbolic architecture that integrates LLMs, retrieval from curated pedagogical resources and determini…
- OpenAlex · Published · First archived
Responsible Use of AI-Generated Feedback and Sustainable Learning Engagement Among Chinese University Students: A Dual-Path Model of Feedback Literacy and Feedback Dependence ↗
As generative artificial intelligence (GenAI) becomes increasingly embedded in university learning, AI-generated feedback offers more accessible and efficient support. However, its educational value depends on how students evaluate and process the feedback they receive. From the perspective of responsible use of AI-generated feedback, this study develops a capability–risk dual-path model linking Student Feedback Literacy (SFL) and Dependence on AI-Generated Feedback (AIFD) to self-reported Sustainable Learning Engagement (SLE) through Critical Engagement with AI-Generated Feedback (CEAF) and U…
- OpenAlex · Published · First archived
Text-to-SQL for Low-Resource Languages: A Unified Large Language Model Framework Evaluated on Arabic, Japanese, and Vietnamese ↗
Text-to-SQL research has concentrated on English, while prior work on Arabic, Japanese, and Vietnamese has largely relied on grammar-constrained parsers or zero-shot prompting, with recent work beginning to explore trained LLM-based alternatives. We investigate whether a unified fine-tuned open-source pipeline can serve typologically distant low-resource languages and quantify the same-pipeline English-to-target gap on parallel Spider instances. We fine-tune Qwen2.5-Coder-7B-Instruct with QLoRA using shared schema grounding, value grounding, an English translation hint, and execution-based sel…
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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