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Nvidia's research demonstrates that for long-horizon agentic tasks, the architectural framework—specifically memory management and supervisor components—is more critical than the underlying model's raw capability. This finding shifts focus from chasing larger parameters to optimizing the scaffolding, tools, and runtime environments that enable reliable multi-step reasoning. Consequently, developers should prioritize refining their harness designs over selecting marginally better base models to achieve high performance in complex workflows.

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Nvidia just showed that the harness, not the AI model, is now the real hero

TechCrunchAugust 21, 20264 min read

Nvidia research shows that AI agents can perform well, and not go off the deep end, through fine-tuning, even if the AI model isn't that great at the task.

Nvidia published some interesting new research on Friday suggesting it’s the harness, more than the underlying model, that is far more important when asking an AI to do long-horizon tasks. The tldr: simply by using a custom harness tweaked to handled memory well and including a “supervisor” boss-like component, researchers got Claude Opus 5 to achieve a 100% score on the interactive reasoning benchmark ARC-AGI-3. (That’s a benchmark that has particularly irked rival frontier lab OpenAI.) Without the harness Opus 5 scored 30%, which…

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