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

Daily paper alert Artificial intelligence
  1. stat.MLFinite-Cadence Refresh Equilibria for Streaming Prior-Anchored Ridge: A Closed-Form Forgetting Window and Spe…Mrigank Shekhar Singh

    The paper studies how often a regression model should refresh its reference point when data keep changing. It derives rules for balancing adaptation to new data against noise, then tests an automated version on simulated settings and two real data …

  2. cs.AIZugwise: Domain-Aware Game Reconstruction for Digitizing Handwritten Chess ScoresheetsGerhard Trippen

    The authors built a tool that reconstructs chess games from handwritten scoresheets by checking whether recorded moves make sense in the evolving game, rather than relying only on handwriting recognition and move legality. In replay tests, it reduced many errors …

  3. cs.LGEEGDash Corpus: A Training-Ready Public Corpus for EEG Foundation ModelsKuber Shahi, Bruno Aristimunha, Arnaud Delorme

    The researchers gathered and standardized a large collection of publicly redistributable EEG recordings so others can train and check brain-signal AI models. Training a foundation model on this collection produced frozen representations with performance close to the released model on …

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

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 paper alert 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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