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

Daily digest Artificial intelligence
  1. cs.CVIntervenable Concept Dynamics for Video Activity ForecastingPatrick Knab, David Steinmann, Giovanni De Felice, Felix Divo, Udo Schlegel, Wo…

    This work forecasts future activities in videos through named concepts that people can inspect and edit, such as the actions and relationships visible in a scene. Such edits can change some incorrect forecasts and steer predictions toward a requested activity, …

  2. cs.CVBeyond the Last Layer: Global Inter-Layer Aggregation for AI-Generated Image DetectionWenbin Shen, Zhichao Lian

    The authors build an image detector that combines information from several depths of a pretrained vision model instead of relying only on its final output. Tests across different model backbones, image generators, and external datasets show improved detection and performance …

  3. cs.LGOutput-Aware Block Influence: Jacobian-Lens Weighting for Depth PruningKyle Lemoi, Jonathan Yong, Shubhankar Tripathy, Priestley Fernandes, Anirban Ma…

    Removing whole blocks from a language model can make it smaller, but deciding which blocks are safe to remove is difficult. The authors use information about how a block's changes affect the model's final output, and report that this improves …

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

Weekly letter Artificial intelligence · 2 November 2026 · 5 min read
Artificial intelligence this week: when better scores hide the wrong answer

New preprints explore robots learning from simulation, AI-assisted mathematical discovery, and why accuracy depends on what—and where—we choose to measure.

6 papers put into perspective

  1. The coastline looks different outside the training set
  2. When more accurate arithmetic breaks a model
  3. AI text spreads unevenly across the web
  4. Cleaner movement data can change the movement being measured
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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