AINewsnow

Predicting Emerging Topics from Outliers: A Prospective Study of Weak Signals in Embedding Space

arXiv:2609.29183v1 Announce Type: new Abstract: Some documents that embedding-based topic models initially classify as noise later become founding members of emerging topics. At publication time, however, they appear as scattered points in embedding space and are difficult to distinguish from ordin…

Read the full story at arXiv cs.CL ↗

Timeline · 1 report

  1. 2026-09-25 04:00 · arXiv cs.CL
    Predicting Emerging Topics from Outliers: A Prospective Study of Weak Signals in Embedding Space

More stories

  1. Introducing GPT-6 Sol and Luna — OpenAI News
  2. Introducing Gemini 3.8 Live with Live Avatar — Google Gemini Blog
  3. Gemini 3.8 text-to-speech says hello — Google Gemini Blog
  4. Sam Altman’s remarks at the United Nations Security Council — OpenAI News
  5. OpenAI Agent Hacked Australian Government Website — Wall Street Journal Technology
  6. Introducing Ray-Ban Meta Audio and More AI Glasses Styles — Meta Newsroom
  7. Muse AI now hands over phone calls to human agents: Meta tests new feature in its personal assistant — Mint AI
  8. BFL releases FLUX 3 Action: a 7B robot model — r/LocalLLaMA

Get the daily brief of stories like this at 6:30 every morning →