Benchmark RadarAI BENCHMARK PROFILE
AI4AI-Bench
AI4AI-Bench evaluates LLM agents on algorithmic design across 10 frozen research repositories. Agents rewrite training algorithms within 4 hours, then scored by fixed evaluators against baseline algorithms, with submissions released for repeatable measurement.
- Released
- 2026-08-20
- Readiness
- Paper only
- Primary field
- General AI
Why it matters
It isolates the ability to design training algorithms for recursive self-improvement, a capability not directly measured by existing benchmarks, and provides a concrete, normalized scoring scale.
Motivation
Recursive self-improvement (RSI) asks whether an AI system can improve the process that produces AI systems, so that the next system inherits the improvement.
Primary resources
Benchmark Radar records only publicly supported details and links back to primary sources for verification.