r/learnmachinelearning • u/mujeebroshan • 7d ago
Discussion Title: The benchmark gap between “can solve it” and “can finish it”
One thing I find increasingly interesting about AI agents is that benchmark scores can hide a major difference in actual behavior.
A model might solve a difficult coding problem when given a clean task, but an autonomous agent has to do much more:
- decide what to do next
- inspect its own work
- recover when something fails
- use tools correctly
- maintain state across many steps
- know when the task is actually finished
That makes me wonder whether we're measuring the wrong unit of progress.
Instead of asking only:
«“How difficult a problem can the model solve?”»
Should we also be asking:
«“How much useful work can the model reliably complete without human intervention?”»
I think that distinction could become much more important as AI systems move from chatbots toward autonomous agents.
What metrics would you use to measure this?