The Meta-Agent Challenge: Are Current Agents Capable of Autonomous Agent Development?

본문 미리보기

arXiv:2606.04455v1 Announce Type: new Abstract: Current AI benchmarks evaluate agents on task execution within human-designed workflows. These evaluations fundamentally fail to measure a critical next-level capability: whether models can autonomously develop agent systems. We introduce the Meta-Agent Challenge (MAC), an evaluation framework designed to test the capacity of frontier models for autonomous agent development. Specifically, a code agent (the meta-agent) is given a sandboxed environm

The Meta-Agent Challenge: Are Current Agents Capable of Autonomous Agent Development?

본문 미리보기

관련 글

Thousand Token Wood: shipping a multi-agent economy on a 3B model

Stability vs. Manipulability: Evaluating Robustness Under Post-Decision Interaction in LLM Judges

An interpretable and trustworthy AI framework for large-scale longitudinal structure-pain association studies using data from the Osteoarthritis Initiative (OAI)

SentinelBench: A Benchmark for Long-Running Monitoring Agents