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

Daily digest Artificial intelligence
  1. cs.LGOctopus: One Brain, Many Branches for Latent-KV Multi-Agent Reinforcement LearningTianming Sha

    The authors built a system in which one language model can run several coordinated research threads while sharing its memory instead of repeatedly copying it. It achieved higher scores on two research tasks than the compared approaches while using less …

  2. cs.LGSteelAgent: LLM-Orchestrated, Physics-Informed Steel Property Prediction with Audited Generalization and Cali…Aleksandr Volkov, Roman Sultimov, Mikhail Kuzin, Yury Maximov

    The authors examined how well steel-strength predictions hold up when models face unfamiliar groups of steels rather than randomly chosen test examples. Performance and uncertainty estimates worsened sharply in these harder tests, showing why reported predictions should specify what kinds …

  3. cs.AIWhen Does Test-Time Search Help World Action Models? An Empirical Audit of Candidate Selection and Simulator …Jun Liu, Dajie Sun, Zhenglun Kong, Zhenjiang Fan, Peiyan Dong, Fan Yang et al.

    The authors tested whether spending extra computation to choose among action plans really improves robot control, saves time, and produces trustworthy evaluations. In the tested systems, selection gains were weak, actual speedups were limited, and a hidden rendering error affected …

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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