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

Daily paper alert Artificial intelligence
  1. cs.CLRethinking in Spikes: Mitigating Hallucinations in MDLMs with Step-Aware DecodingZhongxing Xu, Zhonghua Wang, Zhe Qian, Shiyan Su, Ming Hu, Xiaocheng Zou et al.

    The researchers observed that a few steps in an AI system’s text-generation process are linked to made-up details. They developed a decoding method that temporarily keeps multiple possible meanings in play at those steps, and report that it reduced these …

  2. cs.CLFrom Relevant Posts to Attributable Spans: Explicit Symptom Evidence for Depression Questionnaire InferenceSumanth Manduru, Carlotta Domeniconi

    The researchers built a system that finds specific first-person statements in social-media histories and uses them as evidence when estimating depression questionnaire answers. It improved evidence retrieval and achieved the strongest exact item-level agreement across three evaluation collections, while using …

  3. cs.CVTextual Response-Aware Guidance for Zero-Shot Adversarial Robustness of Vision-Language ModelsYubo Cui, Xianchao Guan, Zheng Zhang

    The researchers developed a way to make image-and-text AI systems more resistant to misleading inputs while preserving their ability to match images with text. Tests across different systems and tasks found strong performance on both ordinary and adversarial examples.

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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Illustrative check, verified against the proceedings. Publication record ↗

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