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Retraction Note: Machine learning-driven evaluation of mechanical and microstructural properties of agro-waste-derived geopolymer concrete

Scientific Reports, Published online: 14 September 2026; doi:10.1038/s41598-026-71215-9 Retraction Note: Machine learning-driven evaluation of mechanical and microstructural properties of agro-waste-derived geopolymer concrete

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

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Why the New LLM Reasoning Leak Paper Matters for Your Team’s AI Workflow

A Quick Look at the Finding A group of researchers just released a paper titled Stealing Reasoning Traces from Proprietary LLM APIs (see the original site here ). In short, they show that when you call a commercial large‑language model (LLM) like Claude, GPT‑4, or Gemini, the service often returns encrypted “chain‑of‑thought” blocks .

  • Researchers can steal reasoning traces from proprietary LLM APIs.
  • Technique requires only two API calls to reverse-engineer internal reasoning.
  • Implications include privacy violations and erosion of trust in AI workflows.

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