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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 ↗6056 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
Controllable Orthogonalization for Stabilizing Neural Network Training in Deep Reinforcement Learning ↗
Deep reinforcement learning (DRL) relies on neural networks trained under temporally correlated experience, evolving replay distributions, and bootstrapped targets, conditions that can destabilize neural function approximation. This study investigates controllable orthogonalization as a network-level mechanism for improving training stability. Orthogonalization by Newton’s Iteration (ONI) is applied selectively to fully connected layers, with layer placement and Newton iteration count T controlling the location and strength of orthogonalization. We first analyze ONI placement and intensity in…
- OpenAlex · Published · First archived
Artificial intelligence driven lithium-ion battery recycling: from materials recovery to intelligent circular energy systems ↗
The rapid expansion of lithium-ion batteries (LIBs) in electric vehicles, portable electronics, and large-scale energy-storage systems has intensified concerns regarding critical material depletion, environmental impacts, and end-of-life battery management. Although conventional recycling technologies, including pyrometallurgical, hydrometallurgical, and direct recycling approaches, have enabled valuable resource recovery, their broader implementation remains challenged by heterogeneous battery chemistries and complex degradation. Artificial intelligence (AI) has emerged as a powerful tool to…
- OpenAlex · Published · First archived
Engineering Data Analysis Beyond Statistics vs. Machine Learning: A Review of Explanation, Prediction, Hybrid Modeling, and Responsible Decision Support ↗
This structured narrative review explores how engineers select and evaluate traditional statistical, machine learning, mechanistic, and hybrid methods for explanation, prediction, and responsible decision support. We identified representative literature available through 2025 via iterative searches of Web of Science, Scopus, ScienceDirect, and Google Scholar, and conducted a thematic synthesis. We qualitatively assessed the credibility and relevance of the literature sources. Application areas covered include manufacturing, semiconductor processing, mechanical and materials systems, civil infr…
- OpenAlex · Published · First archived
Human–AI-Assisted digital twins for advanced manufacturing: toward industry 5.0 ↗
Abstract Digital twins are increasingly recognized as key enablers of human-centric Industry 5.0 manufacturing. Yet existing reviews rarely examine digital twin architectures, AI methods, human-in-the-loop collaboration, and interaction technologies within a unified analytical framework. This paper systematically synthesizes eighty-two peer-reviewed studies, identified through a PRISMA-2020 systematic search of Scopus, the Web of Science Core Collection, and IEEE Xplore, into a comprehensive analysis of human–AI-assisted digital twins in advanced manufacturing. Four research questions structur…
- OpenAlex · Published · First archived
Making Enterprise AI Explainable through Calibrated Confidence and Provenance ↗
Enterprise AI systems cannot earn stakeholder trust without genuine transparency: the ability to trace reasoning, communicate uncertainty accurately, and adapt explanations to user expertise. This paper presents a framework treating provenance tracking and uncertainty communication as foundational requirements. The framework maintains complete reasoning chains with explanation adaptation across four stakeholder levels. Uncertainty decomposition separates epistemic from aleatoric components, enabling users to understand what actions might improve results. Confidence-based routing translates unc…
- OpenAlex · Published · First archived
Hierarchical Multimodal Large Language Model-Based Interpretation and Aggregation of PDP and ICE Explanations for Battery State-of-Charge Prediction in Electric Vehicles ↗
Accurate prediction of the state-of-charge (SOC) of batteries is essential for reliable energy management and safety in electric vehicles (EVs). Although machine learning (ML) models have shown strong predictive performance for SOC estimation, their practical development is hindered by limited interpretability, particularly in understanding how individual input variables affect both global model behavior and instance-level responses. Partial dependence plots (PDPs) and individual conditional expectation (ICE) plots are widely adopted visual explanation techniques. However, their interpretation…
- OpenAlex · Published · First archived
Interpretable Machine Learning for Tokenomic Collapse Risk: Evidence from 155 Real-World Tokens and a Small Prospective Holdout ↗
Can a token’s collapse be predicted from design parameters fixed at launch, before it has market history? We compile on-chain data for 155 tokens (79 survived, 76 collapsed) and benchmark seven classifiers under leave-one-out cross-validation. Random Forest attains the highest area under the curve (AUC; 0.909; nested cross-validation 0.906), though no pairwise test separates the leading models. The result is not a category prior: out-of-fold AUC stays high within categories (decentralized finance 0.943 on 64 tokens; layer-1/layer-2 0.923 on two collapsed tokens, hence unstable), and excluding…
- OpenAlex · Published · First archived
A Multi-Agent Framework with Adaptive Orchestration for Explainable Multi-Modal Deepfake Detection ↗
Modern deepfakes leave forensic traces across audio, visual, and cross-modal channels, yet contemporary detectors are monolithic transformer-based architectures whose post hoc explanations struggle to isolate modality-specific artefacts. We propose a multi-agent system in which five specialised detection agents each target distinct forensic features: facial biometrics and image quality, ECAPA-TDNN speaker embeddings, cross-modal lip-sync, XceptionNet spatial analysis, and FreqNet frequency-domain analysis. Each agent is a self-contained detector with its own perception, decision and explanatio…
- OpenAlex · Published · First archived
A Catboost-Based Machine Learning Approach for Fault Detection in VSC-Based Multi-Terminal HVDC Grid ↗
High-voltage direct current (HVDC) transmission systems face protection challenges due to the absence of natural current zero-crossings and the rapid rise of fault currents. This paper proposes a hierarchical fault detection and classification framework for multi-terminal HVDC networks that combines transient feature extraction with gradient boosting decision trees. The proposed method utilizes traveling wave transients distinguish between internal and external faults across a range of fault resistances and noise levels. The three-stage architecture employs asymmetric loss weighting to maintai…
- OpenAlex · Published · First archived
A comprehensive study on multi-dimensional predictive safety analysis of machine learning models in diabetes detection ↗
Abstract High discrimination performance is often interpreted as evidence of model reliability in clinical machine learning (ML). However, strong predictive accuracy does not guarantee safe decision behavior. In safety-critical domains such as healthcare, miscalibrated probabilities, ineffective uncertainty estimation, and overconfident errors can lead to harmful decisions despite high area-under-curve metrics. This study presents a comprehensive multi-dimensional predictive safety analysis demonstrating that models with comparable discrimination performance can exhibit substantially different…
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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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