Unverified50% confidenceFactExact time
The paper points out that existing user simulators lack outcome calibration, meaning they cannot cause agents to fail in the right places in the right ways.
1
Sources
50%
Confidence
Long-term
Relevance
10/5/2026
First Seen
Sources
Related Entities
Related Claims
UnverifiedModels lacking self-verification tend to fall into a cycle of confident errors, making it difficult to reliably complete multi-step end-to-end tasks even when initial generation accuracy is relatively high.72% similarUnverifiedThe author reported that in the experimental results some icons disappeared partway through, and the model did not fully follow the prompt.72% similarUnverifiedExperimental results showed that these AI agents were unable to complete research work at the same level as NeurIPS papers.72% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/975495API
curl https://kongchang.com/api/v1/knowledge/claims/975495MCP
get_claim(id=975495)