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.
The research paper presents a multi-agent framework for automatic code repair. PhoenixRepair explores multiple potential edit locations and performs iterative reflection to generate better patches. On SWE-bench-Verified, it achieved a 7.8% improvement over SWE-agent with DeepSeek-V3.1 and 76.0% Pass@1 with…
The preprint shows that when fine-tuning LLMs using evolution strategies, population size behaves more as a function of reward design than as a fixed constraint. With z-score normalization disabled, N=2 achieves better results on GSM8K and TREC, where the normalized variant fails. The article mathematically models…
A scientific paper describes the PCS framework for better control of language model behavior. It preserves the model's original capabilities and provides controllable, safety-oriented steering through concept-guided selection of steering vectors.
A study examined whether semantic primes from Natural Semantic Metalanguage explain emotional representations in LLMs better than appraisal-based approaches. Across four models (Llama-1B, Gemma-2B, Gemma-9B, OLMo-7B), it found that NSM primes enable control of emotions that is three times as strong and twice as precise.
A research paper on arXiv extends spectral losses for surrogate modeling of chaotic systems to irregular meshes. It introduces GLEAM (Graph Laplacian Energy Alignment for Meshes) and Chebyshev approximations. Results show improvements in long-term predictability and preservation of statistical invariants during…
A research paper introduces SynPre-FL, a framework combining synthetic healthcare data generation (a latent autoencoder with a diffusion model) with federated learning. The method addresses client heterogeneity and class imbalance, protects privacy against attacks (membership inference, reconstruction), and improves…
The study extends one-dimensional neural operators to two dimensions. The authors test Fourier neural operators (FNO) and U-shaped neural operators (UNO) to approximate scalar flux in one-group neutron transport with isotropic scattering. They investigate three approaches, including a variation using…
Research examines explainability as a tool for understanding continual learning in adaptive forecasting. It tests neural architectures (PatchMixer, PatchTST, DLinear) with attention-based mechanisms on piezometric data. The analysis reveals how attribution patterns evolve over time and how they can…
Researchers introduced a framework for training generative models that predict animal behavior from the animal's own perspective. The models capture an individual's observations and movements, supporting social behavior in groups. They demonstrated it on fruit flies (Drosophila) and released a library for working with…
Research on arXiv describes how orthogonalizing the mLSTM memory matrix at read time (five iterations of the Newton-Schulz algorithm) improves associative recall but does not act as a memory improvement. The effect comes from reconditioning the learning problem during training; removable once convergence is reached, effects on…
An academic paper (arXiv:2607.19384) proposes the SUM method for combining federated and continual learning. It addresses catastrophic forgetting caused by client heterogeneity and sequential tasks through a purely server-side geometric operation on adaptation vectors, without imposing any additional load on clients. It achieves up to…
The study examines how transformers (2.8M–316M parameters) perform Bayesian model selection. In a controlled setting with fixed-point-free involutions, they achieve an entropy match of 0.01 bits with the Bayesian optimum. Model selection works with both integer tokens and opaque symbols, but fails with…
The research paper examines four deep reinforcement learning algorithms (DQN, REINFORCE, PPO, MuZero) trained on the traditional Nepali game Baghchal with asymmetric roles. MuZero achieved the best results: an 86% win rate when playing as the tigers and 62% for the goats. PPO was competitive with lower computational costs.
A paper introduces CruiseBench, a new benchmark derived from the N-CMAPSS dataset for evaluating models that predict the remaining useful life (RUL) of aircraft engines. It introduces CPM-N-CMAPSS (a mask for identifying flight phases) and tests LSTM, GRU, TCN and TSMixer models. TSMixer achieved the lowest RMSE of 3.4±1.71.
The research compares four approaches to intermediate state compression in a two-stage LLM system: no compression, narrative summary, JSON schema and embedding-based pruning. JSON schema achieved the highest accuracy (0.96), narrative the lowest (0.48). Structured representations preserve constraints better than compact…
A research study addresses the generalization of neural decoding across individuals. The method aligns neural responses from multiple subjects into a shared latent space and trains a decoder to predict semantic embeddings. On electrocorticographic data from natural language comprehension, it achieves consistent…
An arXiv study audits ML models for predicting cryptocurrency extrema for trading on Binance Spot. The main result: the strongest model lost 6.72 % over 19 cycles, while validation models lost 1.79 % and 2.80 % compared with buy-and-hold. One model lost 44.30 % versus −41.20 % for the passive strategy. Conclusion: no…
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…
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.
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Model selectionModel comparison by type of work, capabilities, price and speed.
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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.
