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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 ↗5979 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
ChemE-MTDS:Multi-Turn Dialogue Data Synthesis and Quality-Controlled Fine-Tuning for Chemical Engineering Large Language Models ↗
Large language models (LLMs) have demonstrated remarkable capabilities in natural language understanding and reasoning. However, their performance in multi-turn dialogues within specialized domains, such as chemical engineering, remains limited due to the lack of high-quality, context-rich instruction datasets. In this paper, we propose ChemE-MTDS, a multi-turn dialogue data synthesis and quality-controlled fine-tuning framework specifically tailored for the chemical engineering domain. The framework integrates four complementary Question–Answering (QA) synthesis strategies: Continuous Scenari…
- OpenAlex · Published · First archived
Predicting ground motion intensity measures in Japan using a hybrid artificial neural network integrated with Taguchi optimization ↗
Despite the availability of extensive strong-motion recordings in Japan, machine learning (ML)-based ground-motion models (GMMs) capable of consistently predicting a broad range of intensity measures (IMs) remain limited. This study develops a hybrid artificial neural network (ANN) integrated with Taguchi optimization (TO) to predict significant duration ( D s5-75 and D s5-95 ), Arias intensity ( AI ), cumulative absolute velocity ( CAV ), peak ground acceleration ( PGA ), peak ground velocity ( PGV ), and 5% damped pseudo-spectral acceleration ( PSA ) over periods from 0.01 to 10 s. The TO fr…
- OpenAlex · Published · First archived
Empirical Analysis of Goal Hijacking in Large Vision-Language Models via Visual Prompt Injection ↗
Abstract In this study, we explore visual prompt injection (VPI), a method that exploits the ability of large vision-language models (LVLMs) to follow instructions embedded within input images. LVLMs, such as GPT-4V, are designed to interpret and respond to visual prompts. Although this capability enables valuable applications, it introduces substantial security risks. To address this issue, we propose a new VPI method called “goal hijacking via visual prompt injection” (GHVPI). This method redirects LVLMs from their original execution task to an alternative task specified by an attacker. Our…
- OpenAlex · Published · First archived
Explainable and MOWCA-optimized machine learning for compressive strength prediction and counterfactual mixture design of GGBS concrete ↗
Abstract Accurate prediction of compressive strength in ground granulated blast furnace slag (GGBS) concrete is challenging due to complex nonlinear interactions among curing age, water content, and binder proportions. This study develops a comprehensive explainable machine learning framework integrating the Modified Water Cycle Algorithm (MOWCA), interpretable modelling, and counterfactual mixture design for GGBS concrete. Five algorithms: Support Vector Machine, Random Forest, AdaBoost, LightGBM, and CatBoost, were systematically optimized using MOWCA, a single-objective metaheuristic minimi…
- OpenAlex · Published · First archived
Does 2026 AI Exhibit Intelligence, or Can Claude Outsmart Pierre or Catherine? ↗
Using a sequence of high-school level mathematics questions that were not available on the Internet, we compare the performance of the popular AI software Claude with that of my friends and fellow human beings Pierre and Catherine. Pierre had solid scientific training as a young man, while Catherine studied literature. All three were subjected to a simulated pre-calculus oral exam with main questions and follow-up questions. Their performances are compared and the ones with the best and worst performances are identified. The outcome is that the current version of Claude, even though it is an e…
- OpenAlex · Published · First archived
Automating clinical information retrieval from Finnish electronic health records using large language models ↗
Abstract Clinicians often need to search for patient-specific information within electronic health records (EHRs) containing years of heterogeneous longitudinal documentation, a task that can be time-consuming, cognitively demanding, and prone to oversight. We evaluated 14 locally deployable open-source large language models for structured patient-specific clinical information retrieval using a dataset comprising 1664 expert-annotated question–answer pairs from EHR-derived records of 183 Finnish patients undergoing breast cancer screening, diagnosis, or treatment under fully offline conditions…
- OpenAlex · Published · First archived
Regional climate risk assessment from climate models using probabilistic machine learning ↗
Abstract Effective climate risk assessment is hindered by the resolution gap between coarse global climate models and the fine-scale information needed for regional decision making. Existing downscaling methods struggle to bridge this gap: physics-based methods are too computationally expensive for large ensemble sampling of extreme events, while traditional statistical methods fail to capture multivariate spatiotemporal dependencies crucial to compound risk estimation. Furthermore, current machine learning approaches rely on temporally aligned training pairs, which are unavailable for free-ru…
- Europe PMC · Published · First archived
Neuroprotective Effects of Nanocurcumin in Spinal Cord Injury: Modulation of Inflammatory and Inflammasome-Associated Signaling in an Integrated In Vivo , In Silico , and Machine-Learning Study. ↗
Spinal cord injury (SCI) triggers secondary neuroinflammatory and inflammasome-associated responses that contribute to tissue damage and functional impairment. This study evaluated nanocurcumin in experimental SCI using integrated experimental, computational, and exploratory machine-learning approaches. Adult male Wistar rats were allocated to Sham, SCI, or SCI + nanocurcumin groups. SCI was induced by T6 contusion, and nanocurcumin (50 mg/kg, intraperitoneally) was administered every 12 hours for 72 hours. Functional, histological, molecular, and inflammatory outcomes were assessed using BBB
- Europe PMC · Published · First archived
Genome-wide-scale prediction of compound-protein interactions using foundation and language models based on three-dimensional structures of compounds and proteins. ↗
Motivation The identification of compound-protein interactions (CPIs) is crucial in the early stages of drug discovery. However, machine-learning (ML)-based methods based on one- and two-dimensional representations cannot capture important geometric information on the binding sites of CPIs, which limits their predictive accuracy. Results We propose DESTIN (dual structure-guided estimation of substance-target interaction networks), an ML framework for CPI prediction on a genome-wide scale from the three-dimensional (3D) structures of compounds and proteins. We constructed compound feature vecto
- arXiv · Published · First archived
UOPD: Uncertainty-Aware Intervention for On-Policy Distillation of Multi-Turn Agents ↗
On-policy distillation (OPD) trains a student on its own rollouts using dense supervision from a teacher. In multi-turn environments, a mistake at a critical decision step can redirect the subsequent rollout toward poor outcomes. We use low teacher confidence on student actions to select high-uncertainty steps for correction. In a controlled ALFWorld study, a single teacher correction at a low-confidence step improves subsequent student behavior and task success, motivating selective intervention during distillation. We propose UOPD, an uncertainty-aware intervention method for on-policy disti…
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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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