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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 ↗6070 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.
- Hugging Face · Model / dataset date · First archived
JoTalbot/ua-open-data — dataset update ↗
# Україна: дзеркало відкритих даних (data.gov.ua) Автоматичне дзеркало публічних наборів [data.gov.ua](https://data.gov.ua), яке підтримує пайплайн [JoTalbot/ukraine](https://github.com/JoTalbot/ukraine). ## Набори | Набір | Файлів | Джерело | |---|---|---| | Єдиний державний реєстр юридичних осіб, фізичних осіб-підприємців та громадських формувань | 6 | — | | Реєстр декларацій родинних зв’язків та доброчесності | 14 | — | | Державний судновий реєстр України | 9 | — | | Публічні закупівлі на сайті Prozorro | 1 | — | | Інформація щодо стану розгляду справ | 5 | — | | Єдиний реєстр боржників | 1
- Hugging Face · Model / dataset date · First archived
JANGQ-AI/Naive-N0.5-Flash-JANGH2 — model update ↗
Run JANG models in MLX Studio / vMLX > ⚠️ **Runtime: vMLX (Python) support for this model family is not in a released build yet.** The bundle was built > and validated on a vMLX development build that adds the `naive_n05_flash` family. Released vMLX builds do not include > this architecture and cannot load the bundle. > Known limitation of the development build: prefix / SSD cache reuse is not working for this model yet (answers are > correct; a repeated long prompt is processed again instead of being restored). # JANGQ-AI/Naive-N0.5-Flash-JANGH2 **Naive-N0.5-Flash for 128 GB Macs.** 575 GiB o
- Hugging Face · Model / dataset date · First archived
JANGQ-AI/GLM-5.3-Flash-JANGH2 — model update ↗
Run JANG models in MLX Studio / vMLX > ✅ **Runtime: supported in vMLX (Python).** Use **vMLX 1.6.68 or newer** (native JANGH loading; the vMLX 1.6.71 > release was validated with GLM JANGH on native reasoning efforts and chat tool/media continuations). Older vMLX builds refuse the > bundle at load time rather than producing wrong output. > Known limitation (vMLX 1.6.71 notes): video perception is imperfect — the model can describe overlay or > transition text that is not in the clip. # JANGQ-AI/GLM-5.3-Flash-JANGH2 **GLM-5.3-Flash for 128 GB Macs.** Same size as our previous affine release, wi
- OpenAlex · Published · First archived
Virtual staining of Endoplasmic Reticulum Fluorescence image from Brightfield images using Transformer based GAN ↗
Abstract Endoplasmic Reticulum (ER) plays a critical role in cell function and drug response. Fluorescence based cell painting enables detailed visualization of ER morphology and cell response to external perturbations. However, acquiring the fluorescence images for large scale experiments is resource intense and time consuming. In this work, a transformer based Generative Adversarial Network that integrates the convolution operation with the attention mechanism is introduced to predict the fluorescence image from the brightfield images. The generator follows an encoder decoder architecture, w…
- OpenAlex · Published · First archived
Non-Contact SpO2 Estimation Using Camera-Based Photoplethysmography: A Machine Learning Approach on Edge ↗
Vital signs monitoring, particularly of patients with infectious diseases, is crucial for clinical care. Traditional contact-based tools pose risks of cross-contamination and discomfort, especially for individuals with fragile skin. This study introduces a novel non-contact system for real-time peripheral oxygen saturation (SpO2) monitoring using an (RGB) camera. The proposed algorithm combines the ratio of ratios (RoR) method with machine learning techniques (Elastic Net, Ridge, and Linear Regression). The data of 98 patients were collected in a real-world setting: 65 were used for model deve…
- OpenAlex · Published · First archived
CAST: A Compiler-Based Framework for Systematically Testing LLM Compositional Safety ↗
Large language models (LLMs) are increasingly used in software pipelines, raising concerns about harmful behaviors in security-critical domains. Existing safety evaluations predominantly probe models with single prompts or short interactions, and therefore do not capture how safety behaves under multi-step workflows where individual requests are composed into complex behavior. This paper introduces compositional safety, the property that an LLM remains safe not only against isolated malicious prompts, but also under structured, long-horizon decompositions of harmful intents. We propose CAST, a…
- OpenAlex · Published · First archived
Machine learning estimation of prosumer net metering using optimal polynomial basis functions for dimensionality reduction ↗
Abstract Solar energy has become an essential renewable resource for residential prosumers engaging in bidirectional electricity exchange with the grid. However, accurately estimating net consumption remains a challenge due to the prevalence of limited yet redundant local Photovoltaic (PV) data. This paper designs an input dimension reduction approach using optimal polynomial basis functions to enhance machine learning estimation. Optimal higher-order powers are determined via an L 2 -norm performance function based on matrix conditioning. Additionally, Pearson correlation and the Least Absolu…
- OpenAlex · Published · First archived
A secure and explainable phishing detection framework using deep learning and LLM-based reasoning ↗
Existing phishing detection systems face a persistent trade-off: feature-based classical models offer interpretability but sacrifice accuracy on evolving attack patterns, while deep learning models achieve strong accuracy but function as opaque black boxes that provide security analysts with only a label and a confidence score, not actionable reasoning. Recent efforts to close this gap with large language models (LLMs) introduce their own risks hallucination and prompt injection that remain largely unaddressed in prior phishing-detection literature. To resolve these limitations, this paper pro…
- OpenAlex · Published · First archived
Informed forecasting: Leveraging auxiliary knowledge to boost large language model performance on time series forecasting ↗
The rapid adoption of large language models (LLMs) has sparked growing interest in extending their capabilities beyond traditional natural language tasks, including applications in time series forecasting. This work explores the enhancement of time series forecasting with LLMs by incorporating time-dependent covariates. We propose a set of representative prompting strategies that span a wide range of formats while incorporating covariates, and then implement the proposed framework to evaluate performance across three real-world time series datasets in healthcare, service operations, and transp…
- OpenAlex · Published · First archived
SWE-PDB: Teaching LLMs to Leverage Debugging Tools via Agentic Training ↗
Interaction with debugging tools enables large language models (LLMs) to reason over concrete runtime states, rather than relying solely on static analysis of source code. Specifically, with the help of a debugger, an LLM-based agent can observe actual program execution by inspecting intermediate variable states and stepping through the control flow. These runtime observations enable the model to better understand program behavior and identify the root causes of bugs. Despite these advantages, using debuggers correctly and effectively remains challenging for many models because debugger intera…
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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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