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

Daily digest Artificial intelligence
  1. cs.CLDS@GT ARC at CheckThat! 2026: A Verdict-Anchored Pipeline for Generating Fact-Checking ArticlesHoang Thanh Thanh Truong, Shuyu Tian

    The team developed a process for turning a claim, its existing fact-check verdict, and collected sources into a complete fact-checking article. In a shared evaluation, the system placed third out of nine teams and scored above the baseline, showing how …

  2. cs.CVVAR-Q: Tuning-free KV Cache Quantization for Visual Autoregressive Image and Video GenerationBoxun Xu, Jiaji Lu, Yuxuan Yin, Zihu Wang, Ziyue Liu, Yu Wang et al.

    The authors built a system to identify the rhetorical structure of discussions during legislative hearings. Their approach parses over half of the bill discussions in their dataset, and a relatively simple classifier performed best among the tested systems, offering a …

  3. cs.LGA Sharp Dynamic Haussler LemmaShihao Shao, Dacheng Tao

    This paper proves a general rule for how many simple predictors are needed to approximate a broad class of prediction rules over a sequence of inputs. The bound is sharp, works across several common measures of prediction error, and resolves …

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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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