AINewsnow

Which Self-Improvements Should We Trust? Reliable Self-Improvement When Agents Reuse Their Benchmarks

arXiv:2609.33180v1 Announce Type: new Abstract: As recursive self-improvement (RSI) rapidly advances, reliable evaluation becomes critical for guiding adaptive search. RSI typically relies on finite evaluation resources, such as fixed benchmarks, to determine which modifications are retained and wh…

Read the full story at arXiv stat.ML ↗

Timeline · 1 report

  1. 2026-09-29 04:00 · arXiv stat.ML
    Which Self-Improvements Should We Trust? Reliable Self-Improvement When Agents Reuse Their Benchmarks

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 →