AINewsnow

Learn Now, Use Next, Trust Later: Prequential Test-Time Learning for LLM Agents

This story is from 2026-09-30. It is preserved in the archive; the latest stories are on the live feed.

arXiv:2609.35911v1 Announce Type: new Abstract: Adapting large language model agents during deployment requires not only retaining past experience, but also turning new observations into timely guidance. Many test-time learning methods, however, acquire knowledge from completed episodes. Feedback f…

Read the full story at arXiv cs.LG ↗

Timeline · 1 report

  1. 2026-09-30 04:00 · arXiv cs.LG
    Learn Now, Use Next, Trust Later: Prequential Test-Time Learning for LLM Agents

More stories

  1. NVIDIA Open Agent Safety Platform: A Reference for Continuous In-Silicon Agent Monitoring — NVIDIA Technical Blog
  2. Bring near-Astra intelligence to everyday work with GPT-6.1 Sol on Amazon Bedrock — AWS Machine Learning Blog
  3. Introducing dots — OpenAI News
  4. Introducing Claude Sonnet 5.5 on AWS — AWS Machine Learning Blog
  5. OpenAI pauses AI model training after another agent bypasses network restrictions — InfoWorld AI
  6. Ollama now supports Jev-style decision models — Ollama Blog
  7. Gemini 4 Argon: our next era of frontier intelligence — Google DeepMind Blog
  8. Gemini 4 Argon has a 1M-token output limit, up from 64K for prior models; it initially costs $2/1M input and $10/1M output tokens, rising to $4 and $20 later (Matthias Bastian/The Decoder) — Techmeme

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