A preprint is a signal, not a finished product or an independently confirmed result. We therefore track research papers separately and show the actual state of supporting evidence for each one.
846
published research events
Latest work
A significant claim from a single source is published only after further confirmation.
An arXiv preprint study of federated learning for predicting breast cancer progression. It combines clinical data and MRI images, testing a multimodal model with attention to transparency, safety and fairness. It compares federated and centralized approaches with the aim of keeping sensitive data at the patients' location.
A study compares two approaches to generating bearing vibration signals with predetermined fault probabilities (0.25; 0.50; 0.75). PR-GAN modifies signals through training-driven generation with a mean error of 0.046–0.059. A training-free Wachter-style counterfactual achieves higher accuracy (error…
A scientific study demonstrated that, when comparing sparse autoencoders, autointerpretability scores are influenced more by methodological choices in the evaluation pipeline than by actual architectural differences. Testing four metrics on two LM models shows that methodological variability exceeds architectural variability…
The Black-Mamba research model forecasts under distribution shift. It uses biologically inspired accumulation of surprise to decide when to update dynamic memory. Adaptation is selective and event-driven rather than continuous. It achieves competitive performance with fewer memory updates during…
A research study across three datasets (SCMS, DataCo, Olist) compares ML models for delay prediction with simple value-based sorting. ML won only on DataCo (R²=0.27, +10.1 pp), losing on SCMS and Olist (R²≈-0.02). The author recommends deploying ML only after auditing learnability and calibration.
A scientific survey of the trustworthiness of agentic AI systems in engineering. It defines five dimensions of trustworthiness (safety, robustness, transparency, auditability, privacy) and their application in four domains: energy, autonomous vehicles, HPC and communication networks. The aim is to create…
The Phionyx architecture enables deterministic LLM execution with governance mechanisms. It has three layers: a deterministic core, a safety layer and a memory system. Experiments show a 31% reduction in computational overhead, a 24% improvement in data retention and zero deviations across 100 runs.
A research paper introduces TARA, a method for repairing incorrect prompts in text-to-image generators. The method diagnoses and resolves errors in object counting, attribute confusion and illegible text. It improves accuracy by 5.6 points over VisualPrompter on the DSG benchmark and runs 20 % faster.
A new target network update mechanism in Deep Q-Networks mixes parameters from the recent history of the online network, weighted by Q-value sensitivity. Tests in Atari environments show competitive results, outperforming DQN, Averaged DQN and PQN with gradient clipping.
A new research dataset (AQA-Data) and benchmark (AQA-Bench) for forecasting air pollution. It covers 6 pollutants over 3 years across 7 countries, 4 continents and 14 000+ stations. It tested 11 time-series foundation models, which outperformed classic baselines; the top model uses a cross-modal architecture with…
A research paper introduces CPSAINT, a framework for analyzing failures of autonomous agents across seven layers (physical state, sensors, data, compute, actuators, environment, time), and FRIESA-K, a function for quantifying residual risks. The methodology maps failures to measurable risk using an absorbing…
A research paper validates FedCVR (federated learning with server-side adaptive optimization and differential privacy) on five real cardiovascular datasets. The model achieved an F1-score of 79.2 % and an AUC of 0.96 under an operational privacy budget (epsilon ≈ 4.2), statistically outperforming standard…
A new approach to latent world models used for continuous control. Koopman Dreamer uses spectrally constrained deterministic dynamics with Koopman-inspired rotation-scaling blocks to represent damping and periodicities. Experimental validation on the DeepMind Control Suite and UAV-LiDAR…
Research on arXiv finds that common metrics for evaluating machine unlearning (removing knowledge from models) can be misleading. The study tests 45 configurations across five architectures and shows that certificate-style criteria fail for models that should pass, and vice versa. It proposes more selective…
A research paper derives four optimized inference artifacts for transformer attention using Mathematics of Arrays: single-query decode with minimal DRAM communication, a GPU kernel with OpenACC, KV-cache with O(d_k+d_v) append, and GQA/MQA mechanisms with h_q/h_kv reduction.
Tabula, a new foundation model for single-cell genomics analysis, uses federated learning without sharing raw data. It was tested on biological systems (hematopoiesis, neurogenesis, cardiogenesis) and in a human fibroblast study. It nominates rejuvenation factors that outperform conventional approaches.
Researchers created a model that analyzes EEG recordings as a sequence of dynamic functional connectivity graphs. An architecture with multiple experts and a gating mechanism adaptively combines their outputs. Tests on the TUAB dataset showed competitive performance in detecting abnormal EEG.
A research paper studies methods for estimating the total variation distance between autoregressive distributions (e.g. between different LLM engines). It proposes three approaches based on the type of data access: sampling, logits and noisy logits. It empirically validates them on SGLang vs vLLM. Code is available online.
AI Radar monitors Czech and international sources every day, looking for changes that truly deserve attention.
MonitorsOfficial AI company blogs, specialist media, and research sources.
Selects and combinesFilters out information noise and combines articles about the same change into a single event.
Summarizes and explainsExplains significant events in English: what happened, why it matters and where the information comes from.
The result is a quick overview of what has actually changed in the AI world, rather than another stream of articles.
Use the CS/EN switch to read the same Radar in Czech or English. English content is published after its translation has been checked, so new and older items may appear later.
Everything you need to navigate the AI world
Today’s briefingThe “What is worth attention” selection sits beside Live · AI Flash, followed by research and links to other Radar sections. On mobile, these blocks appear one below another.
AI FlashAn ongoing feed of brief updates with an evidence status. Links lead to a Radar detail page when one is ready, otherwise to the original source. You can also find reset and outage histories here.
Practical applicationsWhat new tools and features can do, what you can try and what their actual impact could be.
Model selectionModel comparison by type of work, capabilities, price and speed.
Research and archiveA separate research overview, topic search and older events by date.
One event, everything that matters
Each row represents one event — not one article. At a glance, you can see its significance, credibility and main point.
Illustrative example, not a current news item.
Importance: ▮▮▮ majorOpenAIModels✓ 6
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▮▮▮ major · ▮▮ important · ▮ we're tracking = how significant the change is✓ 6 = six independent publishers, not the number of articles or feeds✓ official = a clear release, law or incident is substantiated by the relevant authority1 source = no independent confirmation yetbold = who is behind the changegray text = a brief summary of what happened
The detail page contains a fuller summary, its significance and original sources. Practical impact appears in the detail and the For individuals and For businesses views. An AI Flash item reaches the main selection only after it has been expanded and meets the publication rules.
The same news, two practical uses
We first summarize each event in the same way for everyone. Based on those same facts, we then explain what the change means for your own use and what it could mean for how a company operates.
For individualsWhat you can use or try, how the change can help you at work and what to watch out for.
For businessesWhat impact the change could have on processes, costs, risks and other business decisions.
Today’s briefing is the same for everyone. Pages
For individuals and For businesses
can be found in the main navigation — they select only events relevant to the given use case.
