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

Daily digest Artificial intelligence
  1. cs.LGMAGE: Towards Generalizable Multi-timescale EEG RepresentationsTuan-Kiet Doan, Van-Chien Nguyen, Trung-Hieu Tran, Quang-Hung Pham, Ngoc-Son Vu…

    The authors introduce an EEG model that can learn from brain signals over several time ranges and choose which range matters most for a given input. Across seven datasets, it performs better than existing EEG foundation models while using fewer …

  2. cs.LGProcess then Retrieve: Investigating the Origins of the Modality Gap in Vision-Language ModelsArpita Shanbhag, Julia Tran, Dhruv Mandala, Ayda Sultan

    The study investigates why image and language features in vision-language models can be unevenly aligned. Its analysis suggests that text representations need more early processing than image representations, offering a possible explanation for this mismatch.

  3. cs.CVMed-DualLoRA: Local Adaptation of Foundation Models for 3D Cardiac MRIJoan Perramon-Llussà, Amelia Jiménez-Sánchez, Grzegorz Skorupko, Fotis Avgousti…

    The authors develop a way for hospitals to adapt a large heart-MRI model together without sharing their private data. It keeps some model adjustments local to each hospital, which can improve fit to local data while reducing communication between sites.

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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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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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