Research separate from news
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.
A significant claim from a single source is published only after further confirmation.
A research team created an AI agent for structure determination from NMR spectra. Instead of mapping spectra directly, it uses an LLM with domain-specific tools. On the Alberts benchmark, it achieved 71% accuracy (versus 66% for chemistry graduates).
A research paper on arXiv describes STN-TGAT, a model combining Transformer and Graph Attention Network for predicting and ranking stocks in the S&P 500. The approach integrates stock price dynamics with relationships between stocks and incorporates realistic trading conditions such as transaction costs. According to the authors, it achieves…
An ArXiv preprint describes AlayaWorld, a 15B transformer model for generating 24fps videos (540p/720p). It can create interactive virtual worlds from text prompts. It achieves the best results on the iWorld-Bench benchmark. The authors plan to make it an open-source project.
A research paper on arXiv introduces MILP-Evo, a framework for automatically designing MILP solver logic. The method uses LLMs to iteratively generate candidate programs, tests them directly on MILP instances and selects components such as a cut selector and branching rule. The outputs are explicit, inspectable…
Researchers propose S2T-RLHF, a method for improving the stability of language model training with RLHF. Instead of scoring individual tokens, it applies hierarchically distributed rewards at the sentence and token levels, reducing the impact of noise during training without additional supervision.
A scientific paper compares consensus segmentation methods in medical imaging. It shows that the sophisticated STAPLE method reduces in practice to majority voting with high suboptimality and fails under data imbalance. Simple voting is surprisingly robust; the best results come from deep…
A research method converts medical ontologies into a training signal for encoder language models. The team created a dataset of 1.3 million LLM-reformulated textbooks from French ontologies and trained a 149M-parameter ModernCamemBERT with two objectives. On the Distemist-FR benchmark, they achieved an improvement of 8.0…
A research paper combines neural networks with symbolic memory in reinforcement learning. The model manages memory represented as RDF graphs in partially observable environments and achieves the best results compared with purely symbolic or neural approaches while preserving decision traceability.
A research study combines 74 000+ arsenic samples from the WQP, MRDS and gNATSGO databases and tests machine learning for predicting groundwater contamination. Graph neural networks (GNN) achieved results comparable to or better than gradient boosted trees, showing the benefit of spatially informed learning for…
A research paper analyzes the Lattice Deduction Transformer (LDT) on Sudoku. The model solves ~95 % of cells in the first pass rather than using iterative search. Backtracking and constraint propagation increased efficiency 1497× but did not change which Sudoku puzzles were solved. Digit-permutation augmentation achieved…
A research paper applies relative positional encoding (RPE) in Transformers to the Team Orienteering problem. This allows the encoder to better model spatial relationships between graph nodes. Experiments on instances with up to 100 nodes show improvements in achieved scores and optimality compared with standard…
A paper introduces NaviAIS, a standardized dataset with vessel AIS data for trajectory prediction in maritime environments, and the NaviLane framework. The dataset contains historical and future trajectories in common time windows, rasterized maps and vectorized vessel routes. NaviLane uses a hierarchical…
A scientific paper introduces TRACER, a method for predicting clinical risks from electronic health records. It combines knowledge graphs enriched with disease severity information, retrieval-augmented generation and clinical note analysis. On the MIMIC-III and MIMIC-IV datasets, it achieved an increase of 28.5…
A research study examines whether Sheaf Neural Networks actually use the geometric property of holonomy. Neural Sheaf Propagation increases SO(2) rotation from 0.010 to 0.388 radians. Replacing learned mechanisms with identities increases error, confirming their use in the networks.
The research paper AI Tour Meeting introduces a framework in which multiple LLM agents with different personas collaborate on planning a group trip through natural language discussion. The aim is a simulation tool for analyzing the behavior of multiple agents in travel planning.
Developer Dylan tested 7 popular AI models (GPT-5.6 Terra, Claude Sonnet 5, Gemini 3.5 Flash, Grok 4.5, Qwen3.7-Max, GLM-5.2, DeepSeek V4 Pro) on 48 combinations of animals and vehicles. The aim was to check whether the models are optimized for the specific case of a pelican on a bicycle. The analysis found no…
Google Quantum AI published a study in Nature on a reinforcement learning agent that optimizes thousands of a quantum computer's control parameters in real time and stabilizes it during computation. It enables simultaneous calibration and computation without interruptions, addressing a fundamental problem of analog…
A study by ETH Zurich and Imperial College London tested JudgeGPT, a GPT-4-based AI assistant, with Pakistani judges. Half of the 1 559 judges received access plus targeted training. These judges resolved 1 848 more cases annually (a 6.3 % increase), judgment quality improved, and the return on investment was $38.50 per…
AI Radar monitors Czech and international sources every day, looking for changes that truly deserve attention.
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.
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.
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Until now, the mode was available only on the highest plan; it is now available on all paid tiers without a waitlist.
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.
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.
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.
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