Cool paper from PwC.
"Earlier is always better" is the default intuition for agent clarification. New paper claims that's mostly wrong.
Goal clarification loses nearly all of its value after just 10% of execution.
The team built a forced-injection framework that drops ground-truth clarifications at controlled points along a long-horizon agent's trajectory, across 4 information dimensions (goal, input, constraint, context), 3 benchmarks, and 4 frontier models. 84 task variants, 6,000+ runs.
Pass@3 falls from 0.78 back to baseline. Input clarification keeps value through roughly 50%. Past mid-trajectory, asking any clarification at all performs worse than never asking.
A complementary study of 300 unscripted sessions shows no current frontier model asks within the empirically optimal window. 52% of sessions over-ask. Others never ask at all.
Why it matters: clarification has been treated as a binary capability, does the agent ask or not. This is the first quantitative demand curve for *when* the question is worth asking.
Paper: https://t.co/U4prpHjKgP
Learn to build effective AI agents in our academy: https://t.co/LRnpZN7L4c
500 TB Tutorials+ Books+ Courses+ Trainings+ Workshops+ Data Scientists Kit 🔥
Free Giveaway💖
▪️ Data science
▪️ Web Dev
▪️ AI
▪️ Cloud
▪️ BIG DATA
▪️ Data Analytics & more.
To get it :
1. Must follow @codewithimanshu to get the Link
2. Like and Repost
3. Comment "send"
Deep dive into the world of AI and play and test your skills with guessing prompts, creating stories, drawing objects, using hand gestures and many more to control and play games!
Registration at: https://t.co/mG6pckn5bW
For more information, visit: https://t.co/4DonJdp71H