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A decentralized multi-agent-based optimization framework for intelligent traffic systems

Scientific Reports, Published online: 02 October 2026; doi:10.1038/s41598-026-74078-2 A decentralized multi-agent-based optimization framework for intelligent traffic systems

We haven't written up this one. Scientific Reports has the full story — the link below goes straight to it.

Read the original at nature.com →

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Jev and Laya beyond the hype: what decision models do that LLM doesn't do

No meu setup de agentes, eu fiz o que todo dev faz no começo: cada subagente acordava com o melhor modelo do SWE-bench por padrão. Typo no README, rename de variável, tudo rodando no topo do ranking.

  • Jev and Laya serve as decision-making models in the system
  • Jev chooses from a finite set of options, LLM writes when needed
  • Downshift separates decision-making from execution, improving efficiency

Who's actually blocking AI crawlers in Europe? A 185-site census (first dated point)

Dated: 2026-10-01, 17:15–17:22 UTC — single crawl pass, fixed panel, fixed method. Pennyforge research artifact. $0 data (public robots.txt + /.well-known/ai-crawler , plain HTTP with a browser…

  • 185 European websites studied in AI crawler census
  • 65.4% of sites have robots.txt file, but 64% lack AI crawler info
  • 18.9% of sites implement hard opt-out, blocking all AI crawlers

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