Population
Complex reasoning and tool-using tasks stratified by estimated difficulty.
All projects
seeking partner
When does more LLM reasoning stop paying for itself?
Measure whether task-adaptive reasoning budgets can reduce the thinking tax while preserving successful completion on complex LLM and agent tasks.
HYPOTHESIS
Routing tasks to short, medium, or extended reasoning budgets will reduce tokens per successful task by at least 25% while keeping task success within 2 percentage points of an always-extended-reasoning baseline.
PRIMARY METRIC
Total model tokens per successfully completed task.
MEANINGFUL THRESHOLD
At least 25% fewer tokens per successful task with no more than 2 percentage points of task-success loss.
BUSINESS TARGET
Lower LLM inference cost and latency without a meaningful loss in task success.
DECISION RULES
Efficiency and quality thresholds both hold
Pilot adaptive reasoning on a production-like workload.
Token savings hold but success falls too far
Raise the escalation threshold for extended reasoning.
No material efficiency gain
Do not add routing complexity.
Business outcomes are research targets, not guarantees. A null or negative result may still create substantial value by preventing investment in an ineffective product, feature, or campaign.
Intervention
A task-adaptive policy that allocates reasoning budget before and during execution.
Comparator
Always use the longest available reasoning budget.
Secondary metrics
Task success · Latency · Recovery attempts · Cost variance by task class