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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 ↗5934 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.
- GitHub · Repository created · First archived
csnyder256/privacy-gateway ↗
Local-first, source-agnostic privacy gateway for reversible PII masking, policy-controlled redaction, and synthetic data.
- GitHub · Repository created · First archived
RandyNorthrup/muse-spark-code ↗
Meta's Muse Spark as a coding agent inside VS Code: streaming chat, tool calls with diffs, permission modes, session history, voice dictation. Unofficial.
- GitHub · Repository created · First archived
kydlikebtc/awesome-jev ↗
1207 public resources for Jev, TypeSafe AI's System One decision model, indexed by decision pattern. Source citations, dated link checks and scheduled call-site text checks; runtime and performance are not independently tested here. EN/中文, JSON schema and platform compatibility.
- GitHub · Repository created · First archived
1612535983/deepresearchagent ↗
Recoverable and auditable deep-research agent built with LangGraph. It plans research, searches the web, manages evidence and context, resumes interrupted runs, and generates cited reports—also designed as a learning reference for AI Agent enthusiasts.
- GitHub · Repository created · First archived
hlan98/JobHunterCat ↗
一只真正替你找工作的 AI 桌宠：它会找岗位、判断匹配度、帮你投递、盯 HR 回复，并根据你的求职结果不断调整策略
- arXiv · Published · First archived
West-WRF AI 2-km: High-Resolution Prediction of Integrated Vapor Transport and Precipitation ↗
We introduce a stretched-grid artificial intelligence (AI) weather forecasting model with 2-km resolution over the western United States and part of the Northeast Pacific and approximately 31-km resolution elsewhere globally. Forecasting over the western U.S. is challenging because complex topography and atmospheric rivers (ARs) strongly influence orographic precipitation. West-WRF AI 2-km builds on a global model pretrained with a 40-year European Centre for Medium-Range Weather Forecasts Reanalysis v5 (ERA5) dataset and is fine-tuned with the Center for Western Weather and Water Extremes (CW…
- arXiv · Published · First archived
FAST-ML: A Hybrid Physics-Machine Learning Framework for Tropical Cyclone Intensity Forecasting ↗
Rapid intensification (RI) remains one of the most consequential and difficult aspects of tropical cyclone (TC) forecasting. Although full-physics numerical weather prediction models can represent the processes governing RI, resolving storm-environment interactions remains computationally expensive, while purely data-driven approaches often lack physical interpretability. We present FAST-ML, a hybrid framework that bridges data-driven efficiency with physical constraints. A physically informed dual-stream neural parameterization ingests 3D ERA5 fields to diagnose ventilation controls---environ…
- OpenAlex · Published · First archived
Chemistry-transferable physics-informed neural network surrogate for lithium-ion cells with online continual learning ↗
This paper presents a physics-informed neural network (PINN) surrogate of the single particle model (SPM) for predicting lithium-ion concentrations and terminal voltage under varying operating and aging conditions. The proposed approach employs a two-stage learning framework. In the offline stage, the network learns the nominal electrochemical dynamics by enforcing the governing partial differential equations along with their associated initial and boundary conditions. During online operation, the physics-informed hidden layers are held fixed, and only the parameters of the output layer are ad…
- OpenAlex · Published · First archived
Depletion-aware intelligence for solar–wind–battery systems: Machine learning pathways for robust sizing, safe dispatch, and battery health management ↗
Battery depletion limits the reliability of solar photovoltaic (PV)–wind–battery systems when renewable variability, converter dynamics and electrified loads reduce usable battery energy or reserve. Here, depletion denotes insufficient deliverable energy or reserve to satisfy load, reliability, ride-through or recovery requirements. State-of-charge (SOC)-floor violations and reserve exhaustion are direct signatures; unmet load is a consequence, whereas deep discharge, cycling and ageing are interacting drivers. Unlike reviews centred separately on state estimation, degradation, sizing or energ…
- OpenAlex · Published · First archived
Development and Validation of a Machine Learning–Based Model for Predicting All-Cause Mortality Risk in Patients With Type 2 Diabetes Mellitus Combined With Hypertension: National Cohort Study ↗
BACKGROUND: Type 2 diabetes mellitus (T2DM) combined with hypertension significantly increases mortality risk, yet accurate risk prediction models remain limited. OBJECTIVE: We aimed to develop and validate machine learning-based models to predict all-cause mortality in patients with T2DM and hypertension. METHODS: We analyzed data from the National Health and Nutrition Examination Survey from 1999 to 2018 linked with mortality data up to December 31, 2019. Adult participants (aged ≥20 years) with concurrent T2DM and hypertension were included. Five machine learning algorithms were developed a…
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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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