Nvidia researchers improve AI agent reliability with a judging model

Nvidia researchers have found a fairly human fix for unreliable AI agents: think before you type. Their new method, called Mid-Harness, has an agent generate several possible actions, then lets a separate judge model choose which one actually runs. On one benchmark, that extra moment of deliberation lifted the first-try success rate from 50.00% to 68.03%. For software that operates inside a command line, where one bad command can sink an entire task, that matters. How Mid-Harness works Mid-Harness has a generator model sample multiple candidate actions at each step, and a second model, the verifier, scores them. Only the top choice is forwarded for execution. The target is what researchers call terminal agents. These are AI systems that work in command-line interfaces or call external tools, typing real commands into real environments. Those environments are stochastic, meaning outcomes can vary unpredictably from one run to the next. An agent might usually know the right command and still fumble it at the worst possible moment. The paper frames this as a gap between generation and reliable execution. A model can be capable of producing a useful command without consistently produci...