Step 7: assistant

Calls write_argument

{
  "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"
}

Calls write_argument

{
  "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"
}

Calls place_argument_passage

{
  "stance": "advances",
  "reading": "premise_unstated",
  "argument_id": "78417b48-ecf9-4ae4-b901-0e87ae9735ea",
  "instance_id": "406c15f8-a49f-48f0-b1a7-fc0166066fae"
}