Run step
Step 8: tool results
write_argument
Input
{
"content": "Most published evaluations of AI research agents (MLE-bench, RE-Bench, PaperBench and similar) score performance on tasks with a predefined, automatically checkable objective, which leaves out choosing questions, judging what result would be worth publishing, and redesigning a study when it fails. The other common approach submits AI-written papers to blind peer review, and peer review is noisy: in the NeurIPS consistency experiments, independent committees disagreed on a large share of accept decisions. If these two approaches are what is mostly in use, and neither tests the open-ended parts of research reliably, then current evaluations measure open-ended research ability poorly.",
"argument_id": "78417b48-ecf9-4ae4-b901-0e87ae9735ea"
}{"success":true,"message":"Written form saved for argument \"The argument from the two dominant evaluation modes\"."}write_argument
Input
{
"content": "Some evaluations already reach past narrow scoring. Long-horizon agentic task suites measure how long a task agents can carry through. Expert-graded studies have domain researchers judge agents' work on unsolved problems. Replication benchmarks require agents to rebuild whole papers. If these capture key parts of research ability with acceptable reliability, then current evaluations measure that ability incompletely but not poorly.",
"argument_id": "fc83eb5b-1cc1-40c8-ab78-e935d7ba7656"
}{"success":true,"message":"Written form saved for argument \"The argument from expert-graded and long-horizon evaluations\"."}place_argument_passage
Input
{
"stance": "advances",
"reading": "premise_unstated",
"argument_id": "78417b48-ecf9-4ae4-b901-0e87ae9735ea",
"instance_id": "406c15f8-a49f-48f0-b1a7-fc0166066fae"
}{"success":true,"instance_id":"406c15f8-a49f-48f0-b1a7-fc0166066fae","message":"Placed the passage on argument \"The argument from the two dominant evaluation modes\" as a advances instance."}