AINewsnow

PORA: Learning When More Reasoning Pays Off for LLM Agents

arXiv:2610.27119v1 Announce Type: new Abstract: LLM agents perform tasks over multiple steps and revise their actions based on feedback. As the total reasoning budget is often limited and must support future steps, the agent must choose between short and long reasoning before each action. Longer re…

Read the full story at arXiv cs.AI ↗

Timeline · 1 report

  1. 2026-10-21 04:00 · arXiv cs.AI
    PORA: Learning When More Reasoning Pays Off for LLM Agents

More stories

  1. DeepSeek V4.1 Flash is now available in Token Pass — Vercel Blog
  2. OpenAI acquires Orchestra Bio to expand AI in drug development — OpenAI News
  3. US bans DeepSeek AI from government systems: What the official notice says about ‘transfer of sensitive data’ to China — Mint AI
  4. Southeast Asia’s digital economy to hit $368 billion — Google AI Blog
  5. Deepseek released DeepInfer - Open Source high-performance, large-scale inference framework for DeepSeek models! — r/LocalLLaMA
  6. Gemini on Android Auto can access my photos, emails, and calendar now – why I’m OK with that (mostly) — ZDNET AI
  7. Otter AI raises $320M from General Catalyst's growth fund for its meeting tool — TechCrunch AI
  8. Run Agentic Workloads Safely at Scale with CoreWeave Sandboxes — CoreWeave Blog

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