Run step
Step 22: tool results
add_relationship_edge
Input
{
"child_id": "a3a7a95b-f3fb-4cf8-a605-cbe3bee834db",
"relation": "supports",
"parent_id": "55f89371-8a43-43c1-8dd7-932c350ca7fb",
"reasoning": "A large and fast-growing AI share of AI R&D is the trend whose extrapolation underlies forecasts of autonomous successor training.",
"argument_id": "13212832-045b-46a0-a949-532148246e3f"
}{"success":true,"message":"Linked existing claim a3a7a95b-f3fb-4cf8-a605-cbe3bee834db as a subclaim of 55f89371-8a43-43c1-8dd7-932c350ca7fb (supports). Grouped it under argument \"The argument from the trend in AI R&D automation\".","child_claim_id":"a3a7a95b-f3fb-4cf8-a605-cbe3bee834db","relationship_id":"6e244f1b-b780-438a-877f-a6fb0bb31f65","created":true,"argument_id":"13212832-045b-46a0-a949-532148246e3f","grouped":true}add_relationship_edge
Input
{
"child_id": "f5a654f9-9ef3-4144-9869-6bff1c6d9631",
"relation": "supports",
"parent_id": "55f89371-8a43-43c1-8dd7-932c350ca7fb",
"reasoning": "Improvement in choosing research next steps (research taste) is the capability the forecast's author named as the bottleneck; progress on it supports the claim.",
"argument_id": "13212832-045b-46a0-a949-532148246e3f"
}{"success":true,"message":"Linked existing claim f5a654f9-9ef3-4144-9869-6bff1c6d9631 as a subclaim of 55f89371-8a43-43c1-8dd7-932c350ca7fb (supports). Grouped it under argument \"The argument from the trend in AI R&D automation\".","child_claim_id":"f5a654f9-9ef3-4144-9869-6bff1c6d9631","relationship_id":"4a088843-e16e-4b07-a010-b77752296ac6","created":true,"argument_id":"13212832-045b-46a0-a949-532148246e3f","grouped":true}add_relationship_edge
Input
{
"child_id": "d479cf4d-52b6-4260-a3dc-7a2e5a812d42",
"relation": "contradicts",
"parent_id": "55f89371-8a43-43c1-8dd7-932c350ca7fb",
"reasoning": "If humans still supply most research judgment in late 2026, end-to-end autonomous successor training within about thirteen months requires a rapid closing of that gap.",
"argument_id": "850a64f2-3d1c-4891-9f3b-d52ca77d1bc6"
}{"success":true,"message":"Linked existing claim d479cf4d-52b6-4260-a3dc-7a2e5a812d42 as a subclaim of 55f89371-8a43-43c1-8dd7-932c350ca7fb (contradicts). Grouped it under argument \"The argument from the remaining gap in research judgment\".","child_claim_id":"d479cf4d-52b6-4260-a3dc-7a2e5a812d42","relationship_id":"042ed080-5373-46e6-8408-faf1210b2aae","created":true,"argument_id":"850a64f2-3d1c-4891-9f3b-d52ca77d1bc6","grouped":true}add_decomposition_edge
Input
{
"relation": "contradicts",
"parent_id": "55f89371-8a43-43c1-8dd7-932c350ca7fb",
"reasoning": "If developers keep humans in the loop of frontier training runs, a frontier model does not autonomously train its successor even when capable of doing so.",
"seed_note": "Frontier training runs cost hundreds of millions to billions of dollars, and published frontier safety policies and alignment concerns about recursive self-improvement give developers strong reasons to keep humans approving and monitoring such runs. Competitive pressure and the low cost of oversight relative to the run cut the other way only weakly, though a race could erode it over a longer horizon.",
"child_text": "Frontier AI developers will retain human oversight of frontier model training after AI can perform it autonomously.",
"claim_type": "empirical_derived",
"importance": 0.45,
"argument_id": "0aba1636-a014-424e-abf1-8ea8d4d5b723",
"contestation": 0.5,
"seed_credence": 0.75
}{"success":true,"message":"Added subclaim to 55f89371-8a43-43c1-8dd7-932c350ca7fb: \"Frontier AI developers will retain human oversight of frontier model training after AI can perform it autonomously.\" (contradicts); seeded with prior credence 0.75 and a preliminary note; claim_type empirical_derived","child_claim_id":"4279b6b3-295f-4dee-8793-74610abbe760"}