{
  "id": 4770768,
  "title": "TuringLLM: Efficiently Scaling Foundation Models Toward Physical AI",
  "url": "https://urgent.news/2026/08/31/turingllm-efficiently-scaling-foundation-models-toward-physical-ai",
  "topic": "ai",
  "section": "AI",
  "published": "2026-08-31T10:42:35.000Z",
  "source": {
    "name": "arXiv cs.AI",
    "slug": "arxiv-cs-ai",
    "url": "https://arxiv.org/abs/2608.30567v1"
  },
  "original_language": "en",
  "account": null,
  "summary": "We present Turing-20B-A2B, a 20B-parameter Mixture-of-Experts language model that activates approximately 2B parameters per token, designed for long-context and latency-sensitive physical AI applications. The model adopts Quantile Routing in a dynamic top-k configuration, enabling token-adaptive expert allocation while maintaining balanced expert utilization and a controlled average compute…",
  "key_points": [],
  "editors_take": null,
  "illustration": null,
  "coverage": {
    "outlets": 1,
    "also_reported_by": []
  },
  "ai_generated": true,
  "disclaimer": "Summaries, key points and the editor’s take are written by software from other outlets’ reporting and may contain errors — always check the linked original."
}