{
  "id": 7446894,
  "title": "Dynamic Semantic Compression for Efficient Latent-Space Inference in Large Language Models",
  "url": "https://urgent.news/2026/09/14/dynamic-semantic-compression-for-efficient-latent-space-inference-in",
  "topic": "ai",
  "section": "AI",
  "published": "2026-09-14T10:24:16.000Z",
  "source": {
    "name": "arXiv cs.AI",
    "slug": "arxiv-cs-ai",
    "url": "https://arxiv.org/abs/2609.15338v1"
  },
  "original_language": "en",
  "account": null,
  "summary": "Large Language Models (LLMs) primarily perform inference at the token level, resulting in substantial memory overhead and compromised computational efficiency. In this paper, we propose a Dynamic Semantic Extraction and Inference (DSEI) framework, which achieves segment-level inference within the latent space through a two-stage training strategy. First, we construct a Dynamic Semantic…",
  "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."
}