AINewsnow

Context-dependent agent evaluation with orthogonal equilibrium learning

arXiv:2609.31897v1 Announce Type: new Abstract: Many applications require to evaluate agents under contextual information (e.g., a prompt, task, or user group). We study how to perform such context-dependent agent evaluation from offline feedback. Existing score-based models for this purpose (e.g.,…

Read the full story at arXiv cs.AI ↗

Timeline · 1 report

  1. 2026-09-29 04:00 · arXiv cs.AI
    Context-dependent agent evaluation with orthogonal equilibrium learning

More stories

  1. NVIDIA Open Agent Safety Platform: A Reference for Continuous In-Silicon Agent Monitoring — NVIDIA Technical Blog
  2. How we found 24 Android vulnerabilities using our open source AI security agent — GitHub Blog
  3. OpenAI Scraps Debut of Latest Astra Model Over Safety Risks — Bloomberg AI
  4. Introducing Claude Sonnet 5.5 on AWS — AWS Machine Learning Blog
  5. Heads of OpenAI and Anthropic called to face Senate inquiry after rogue agent incidents — The Guardian AI
  6. Meta launches enterprise AI business seeking to cash in on vast spending — Financial Times AI
  7. AMD agrees to acquire Fei-Fei Li's World Labs for $8.2B in an all-stock deal expected to close by year-end; Li will join AMD as EVP and chief scientist (Edward Ludlow/Bloomberg) — Techmeme
  8. Scoop: Anthropic's Dario Amodei to have White House dinner with Trump — Axios AI+

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