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.
825
published research events
Latest work
A significant claim from a single source is published only after further confirmation.
Research shows that the foundation model Uni-Mol2, adapted to predict odor descriptors, successfully transfers to other tasks: odor recognition in individual datasets, binary classification, distinguishing enantiomers, and odor discrimination in mixtures. The model matches or surpasses the previous SOTA on GS-LF…
The research team proposed PIEFL, a procedure that adds an auxiliary network after training the primary network, with the auxiliary network learning to correct predictions based on error fields. The method achieves higher accuracy in solving partial differential equations without changing the architecture of the primary network and while maintaining the same computational budget.
An empirical study comparing four approaches to identifying metaphors in Chinese sentences. BERT fine-tuning performed best on the original dataset (91.76 Macro-F1). Expert-informed skill prompting achieved the lowest variability (4.08 points) and the most stable performance across datasets (82.92 F1).
The study tested whether decodable empathy directions in LLMs control model behavior. Across three models (Qwen, Llama, Gemma), it found: the affective facet changes (+0.29 in Qwen, ~26 % of the natural difference), the cognitive facet does not change, and human perception remains unchanged. Conclusion: decodability is not a reliable lever.
The research paper introduces a 0.6B-parameter embedding model for legal retrieval. It achieves 75.11% on MLEB and 64.38% on MTEB(Law, v1). Trained on 3.4M query-passage pairs (150k manually annotated) using a two-stage process: knowledge distillation + domain-specific fine-tuning. Supports quantization for…
Google Research released GlucoFM, a foundation model for analyzing data from continuous glucose monitors (CGM). The model separates long-term glucose trends from short-term deviations using a dual-stream architecture. It achieves a PR-AUC 5.8 percentage points higher than GluFormer, trained on 109,066 hours of unlabeled data…
The research paper presents Neurosymbolic Alignment, a method that improves the safety of a 7B clinical LLM by connecting it to a physiological world model. On Clinical Safety Benchmark, it achieves 90.8% accuracy (compared to 69.5% without the method), outperforms GPT-4 with 10× fewer parameters, and reduces medical errors from 14.1%…
A research paper combines topological deep learning with equivariant neural networks to predict molecular Hamiltonians. The new Equivariant Cellular Sheaf Networks model formalizes the Hamiltonian as a Laplace operator with O(3)-steerable kernels, generalizes existing approaches, and achieves lower…
The research team at Apple proposed IDEA Prune, a method that combines model expansion, pruning and recovery in a single cycle with iterative pruning. Compressing 2.8B models to 1.3B achieves better token efficiency than training models of the target size from scratch.
The team published MAP, a framework for predicting transcriptomic responses to unknown drugs. It combines knowledge graphs (187 089 drugs, 694 246 relationships) with a single-cell foundation model. They achieved an improvement of up to +12.3 % in predicting differentially expressed genes compared with baseline methods.
Google Research publishes AgentHands, an LLM-powered XR prototype that generates synchronized hand gestures for conversational agents. The project, prepared for CHI 2026, combines spatial understanding with natural gestures to give instructions for physical tasks.
The research framework MSM-Mem adds structured memory with semantic, episodic, and visual components to medical AI agents. It enables gradual improvement in decision-making processes. Testing on MoE-LLaVA shows consistent improvements in performance.
The study presents MCite-RL, a framework for multimodal RAG focused on precise visual citations. It combines iterative retrieval, agentic decision-making and reinforcement learning to optimize both answer accuracy and source traceability. Tested on the Wiki-VISA, FinRAGBench-V and MMLongBench-Doc benchmarks.
The LëtzCross benchmark for cross-lingual retrieval in Luxembourgish with queries in 4 languages. It combines text-focused and visually-grounded QA pairs. Page-image retrievers (ColPali) outperform OCR-based approaches. Multilingual fine-tuning with Luxembourgish yields the best results.
The research presents the ASP method for multimodal agents with a fixed token budget. It tests the method on 7 open-weight models (3B–31B) on the synthetic benchmark SEW-Bench. ASP achieves 75–94 % retrieval accuracy compared with 3–19 % for the baseline without a query; the query-conditional approach emerges as decisive…
A research team has released the Wazobia Eval benchmark with 550+ manually annotated examples for evaluating language models on their understanding of Nigerian. The dataset includes a 16-category emotion taxonomy, sarcasm detection and evaluation of cultural reasoning. The dataset is publicly available.
The research paper presents the ADAPT framework for efficiently creating families of LLMs: it combines size-based interpolation with cross-variant optimization, enabling the creation of L × K models (L interpolated sizes, K post-trained variants) in a single distillation run instead of independently training each…
A Stanford study from August 2026 shows that employment among workers aged 22–25 in occupations most exposed to AI is 19 percent lower than among their peers in less exposed fields (last year it was 13 percent). Older workers have not yet been significantly affected.
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.
