Unverified50% confidenceFactExact time
The effectiveness of real-time AI search may change within weeks after publishing better evidence and earning citations, while recall at the model memory level follows vendor training cycles and operates on a monthly timescale.
1
Sources
50%
Confidence
Medium-term (~90 days)
Relevance
10/11/2026
First Seen
Valid until: 1/9/2027
Sources
Related Claims
UnverifiedMoving data quality inspection to before training can significantly improve experimental efficiency, avoiding the need to trace back to data after tens of hours of training only to discover policy anomalies.74% similarUnverifiedModels trained at large scale may acquire meta-cognitive information from training data about 'being an AI' and 'potentially being under evaluation'.73% similarUnverifiedThe plugin mechanism is used to compensate for the inherent limitation that a large model's knowledge base is pre-trained, static, and has a cutoff date—the model cannot answer questions about events after the training cutoff and must call search-type plugins for real-time retrieval.71% similarUnverifiedOpenAI data shows that agent usage frequency is positively correlated with research output, and the more frequently a team uses agents, the shorter the experimental iteration cycle.71% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/997396API
curl https://kongchang.com/api/v1/knowledge/claims/997396MCP
get_claim(id=997396)