AINewsnow

Rethinking User Encoders: Routing Diffusion States for Scenario-Adaptive User Representation

This story is from 2026-10-20. It is preserved in the archive; the latest stories are on the live feed.

arXiv:2610.22400v1 Announce Type: new Abstract: Existing large language model (LLM)-based user encoders serialize heterogeneous behavioral logs and compress them into static representations through autoregressive (AR) prediction. However, their unidirectional constraint limits bidirectional informa…

Read the full story at arXiv cs.AI ↗

Timeline · 1 report

  1. 2026-10-20 04:00 · arXiv cs.AI
    Rethinking User Encoders: Routing Diffusion States for Scenario-Adaptive User Representation

More stories

  1. MiniMax launches M3.1 Flash, confirming Space Bunny was an early build — Vercel Blog
  2. OpenAI Staffer Charged With Bugging Office Stays Jailed for Now — Bloomberg AI
  3. Inception's Mercury 3 Hits 1,068 Tokens per Second, Beating GPT-6 Luna — AlphaSignal
  4. Introducing Clairic, a new System One class of extraction models — Cloudflare Blog — AI
  5. US bans DeepSeek AI from government use as tech tension with China continues — BBC Technology
  6. ‘Open your doors for AI’: German chancellor urges businesses to embrace AI as Nvidia announces new investments — CNBC Technology
  7. How HP Is Building an AI-Ready CDP with Databricks CustomerLake — Databricks Blog
  8. Musk pledges 1 SpaceX share for every child in America — Axios AI+

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