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Real, recent examples from the field “Artificial intelligence”.

Daily digest Artificial intelligence
  1. cs.LGGenerative Reconstruction of Full-Field Sensor States for Safety-Critical Digital TwinsUmme Mahbuba Nabila, Linyu Lin, Majdi I. Radaideh

    The authors compare methods for filling in missing temperature readings in a test facility where sensors can fail or drift. Generative models, especially those that account for sensor locations and changes over time, reconstruct temperatures accurately even when many readings …

  2. cs.LGRethinking Data Selection as Distribution Matching on Semantic ManifoldsPeng Sun, Yi Yang, Mingshuo Chen, Chunxiao Li, Yao Zhu, Huawen Shen et al.

    The authors develop a way to select smaller, cleaner training datasets while keeping them representative of the full dataset. Across many language and vision-language tasks, their method often performs better than existing selection methods and sometimes even beats training on …

  3. cs.LGEmbedding Consistent Recurrent Neural Networks in Finite Element Simulations for Path-Dependent Damage Predic…Muhammed Adil Yatkin, Mihkel Kõrgesaar, Hasan Kurban

    The authors embed recurrent neural networks that predict material damage directly into a finite-element simulation of a steel plate. They show that one network gives consistent predictions as the simulation’s step size changes, an important requirement for reliable use in …

Every weekday, the new papers in your categories, each with a short summary. See a whole digest →

Weekly letter Artificial intelligence · 9 November 2026 · 5 min read
Artificial intelligence this week: When better scores miss the point

This week’s preprints test whether AI improvements survive contact with new hospitals, financial markets, physical laws and the people expected to use them.

6 papers put into perspective

  1. Tutors change their questions, but still dominate the conversation
  2. A convincing heat map is not an error alarm
  3. Physical laws give neural networks a close rival
  4. Reading financial news is not the same as beating the market
Read the whole letter →
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Illustrative previews based on a published paper. Explore a live summary or set up your own digest to try the tools.

01 / Understand

Attention Is All You Need

Vaswani et al. · 2017 · arXiv:1706.03762

The question
Can attention alone support a strong sequence-to-sequence model?
The main idea
The Transformer replaces recurrence and convolution with attention. The paper evaluates it on translation tasks.
A point to check
Results on these benchmarks do not establish performance on every language or task.

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02 / Check references

A preprint has a published version

Your reference

Vaswani et al., 2017
arXiv:1706.03762

Publication to review

Advances in Neural Information Processing Systems 30 (2017)

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