{
  "id": 6523618,
  "title": "RiLM: Parameter-Efficient Language Modeling via Geodesic Decoding",
  "url": "https://urgent.news/2026/09/09/rilm-parameter-efficient-language-modeling-via-geodesic-decoding",
  "topic": "ai",
  "section": "AI",
  "published": "2026-09-09T15:16:31.000Z",
  "source": {
    "name": "arXiv cs.AI",
    "slug": "arxiv-cs-ai",
    "url": "https://arxiv.org/abs/2609.10305v1"
  },
  "original_language": "en",
  "account": null,
  "summary": "Language models under one million parameters matter for edge deployment, domain adaptation, and reproducible research, yet a two-layer LSTM or Transformer at embedding width d = 128 still spends roughly one third of its capacity on the output matrix W_out in R^(d x |V|). We propose Riemannian Language Models (RiLM), which remove that layer entirely: context unfolds as a trajectory on a Riemannian…",
  "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."
}