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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 ↗5908 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
Retrieval of Total Suspended Matter in Inland Water Bodies via EnMAP and PRISMA Spaceborne Hyperspectral Imagery: A Dual Neural Network Approach Trained on Decadal In-Situ Spectroscopy ↗
Total Suspended Matter (TSM) is a primary indicator of water quality. Yet, its ecological impact is dictated by its composition— specifically the ratio between Suspended Particulate Inorganic Matter (SPIM) and Suspended Particulate Organic Matter (SPOM). In Mediterranean reservoirs, where agricultural runoff and seasonal algal blooms create complex optical mixtures, retrieving these components separately is essential for distinguishing between mineral sediment transport and organic productivity. Traditional multispectral sensors struggle to decouple the distinct scattering and absorption signa…
- OpenAlex · Published · First archived
Visibility of the Korean National Health Information Portal across search engines and generative artificial intelligence platforms: a repeated cross-sectional study ↗
Objectives: This study assessed changes in the visibility of the National Health Information Portal (NHIP), operated by the Korea Disease Control and Prevention Agency, across conventional search portals and generative artificial intelligence (AI) platforms. Methods: We analyzed 150 health-related keywords derived from NHIP traffic data, Daum search logs, and chronic disease categories across 7 platforms: Naver, Google, Bing, Daum, ChatGPT, Perplexity, and Gemini. Searches were conducted in September 2025 and February 2026; Gemini was assessed only in the second survey. NHIP exposure was defin…
- OpenAlex · Published · First archived
Projected Primal–Dual Gradient Preference Optimization Under Category-Wise Split-Conformal Safety Constraints for Large Language Model Post-Training ↗
Preference optimisation improves helpfulness, but a single aggregate safety penalty lets frequent harm categories dominate the objective while rare ones bear the residual risk. This paper models post-training as a vector-constrained programme: the pairwise preference loss is minimised under one risk tolerance per harm category. The solver is a projected primal–dual gradient recursion that prices each category by its own residual violation. For a nonconvex objective we bound three horizon-averaged residuals—primal stationarity, dual regret and the average constraint value—for the unbiased, uncl…
- OpenAlex · Published · First archived
Dynamic Machine Learning for the Oxygen Evolution Reaction: From Static Catalyst Screening to Working-State Trajectory Learning ↗
Efficient water electrolysis is limited not only by the sluggish kinetics of the oxygen evolution reaction (OER), but also by the dynamic reconstruction and degradation of catalysts under operating bias. Machine learning (ML) has accelerated OER catalyst screening by linking precursor composition, structural descriptors and adsorption energetics to endpoint activity, yet static endpoint models cannot determine when a working interface forms, how reaction pathways redistribute, or where stability costs accumulate during operation. This Review does not treat catalyst reconstruction or ML-assiste…
- OpenAlex · Published · First archived
Explainable artificial intelligence driven dynamic evaluation framework for Industry 4.0 oriented talent cultivation in higher education ↗
Abstract The rapid advancement of Industry 4.0 technologies has created an urgent demand for highly skilled graduates equipped with competencies in artificial intelligence, data analytics, Internet of Things (IoT), cloud computing, cybersecurity, digital manufacturing, and innovation management. To address this challenge, this research proposes an Explainable AI (XAI)-Driven Dynamic Evaluation Framework for assessing and optimizing Industry 4.0 talent cultivation in higher education institutions. The proposed model utilizes the College Student Placement Factors Dataset containing 10,000 studen…
- OpenAlex · Published · First archived
LLM Hallucination of Citations in Economics Persists With Web‐Enabled Models ↗
ABSTRACT Despite significant advances since 2023 in large language model (LLM) capabilities and the introduction of web‐enabled search functionality, citation hallucination remains a persistent problem in AI‐generated academic content. We test GPT‐4o, DeepSeek V3, web‐enabled GPT‐4o, and web‐enabled GPT‐5.1 across prompts derived from Journal of Economic Literature categories. Using a hierarchical citation validation framework, we find that even an advanced web‐enabled model produces non‐existent citations. While web‐enabled models show improvement over their predecessors, the error rates rema…
- OpenAlex · Published · First archived
AI Companions and Loneliness: Conceptualizing and Operationalizing Synthetic Mindfulness and Exploring Its Role in Alleviating Loneliness ↗
ABSTRACT The rise of AI companions has sparked interest in their potential to alleviate the growing social problem of loneliness, yet evidence of their effectiveness remains unsettled. Drawing on social surrogacy theory with a theories‐in‐use approach, this research introduces synthetic mindfulness, defined as consumers' judgment of an AI companion's capacity to maintain an attentive presence, engage in a non‐judgmental way, and respond with sensitive attunement to their needs. We argue that these judgments shape whether consumers experience AI companions as capable of reducing their lonelines…
- OpenAlex · Published · First archived
From Infection to Oncology: AI-Assisted Drug Repurposing Uncovers Anti-Infective and Excipient-Class Compounds as PPAR-α-Linked Anticancer Agents ↗
Drug repurposing offers an efficient strategy to identify novel anticancer agents from marketed compounds. We screened a library of ~1400 approved drugs using a PPARE–PPAR-α luciferase reporter assay to uncover modulators of lipid metabolism that induce cancer cell death without direct binding to PPARα. This primary screen identified 224 compounds inhibiting reporter activity by >50%, of which 26 demonstrated ≥50% cytotoxicity in KAIMRC1 breast cancer and HCT116 colorectal cancer cells at 10 µM after 48 h incubation. AI-based DrTarget analysis classified these hits, with Group 1 comprising fou…
- OpenAlex · Published · First archived
Advancing mathematics research with AI-driven formal proof search ↗
Large language models (LLMs) increasingly excel at mathematics tasks, but their unreliability limits their utility in mathematics research. A mitigation is to use LLMs to generate formal proofs in languages such as Lean, in which the compiler verifies every proof step. We present the first demonstration of this method’s value in solving open problems at scale. We built an artificial intelligence agent for formal proof search that autonomously resolved nine of 353 open Erdős problems, proved 44/492 On-Line Encyclopedia of Integer Sequences conjectures, and is being deployed in combinatorics, op…
- OpenAlex · Published · First archived
Physics-grounded multi-agent architecture for traceable, risk-aware human–AI decision support in manufacturing ↗
High-precision CNC machining of free-form aerospace components requires bounded compensations informed by inspection, simulation, and process knowledge. Off-the-shelf large language model (LLM) assistants can generate text, but they do not reliably execute risk-constrained multi-step numerical workflows or provide auditable provenance for high-stakes decisions. We present multi-agent knowledge analysis (MAKA), a decision-support architecture that separates intent routing, tools-only quantitative analysis, knowledge graph retrieval, and critic-based verification that enforces physical plausibil…
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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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