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The InferenceBench analysis explores the current limitations of autonomous AI agents in managing complex machine learning systems engineering tasks.

While these agents possess significant technical knowledge, they consistently fail to outperform traditional mathematical optimization algorithms like SMAC3 due to a lack of iterative discipline and a reliance on memorized configurations.

A surprising inverse scaling effect is documented, where massive models like GPT-5.5 and Claude Opus underperform smaller, more stable counterparts like Claude Sonnet 4.6 and GLM-5.

The research highlights how larger models often succumb to cognitive drift and destabilizing late-stage edits that break brittle infrastructure.

To achieve true AI R&D automation, the sources suggest that future architectures must integrate deterministic solvers and automated state-preservation protocols. Ultimately, the benchmark serves as a critical reality check, proving that raw computational scaling is insufficient for mastering open-ended engineering challenges.