Unverified50% confidenceFactExact time
Large language models have significant inference latency and cannot compete with top human players on the dimension of actions per minute (APM).
1
Sources
50%
Confidence
Long-term
Relevance
9/20/2026
First Seen
Sources
Related Entities
Related Claims
UnverifiedLarge models have high inference costs, large response latency, and steep deployment barriers, making them unsuitable for all scenarios.72% similarUnverifiedEven top-tier labs find it difficult to accurately predict the performance of a model at a given scale in advance, and typically need to approximate the answer through extensive scaling law experiments71% similarUnverifiedWhen model scale crosses certain thresholds, emergent capabilities not previously present arise (emergent abilities), such as multi-step logical reasoning and complex mathematical computation.69% similarUnverifiedThe core motivation behind the deceleration argument is that the rate of model capability growth is outpacing humans' ability to understand and constrain those capabilities.67% similarUnverifiedThe sample efficiency of evolutionary strategies in high-dimensional parameter spaces is generally lower than that of gradient-based methods, and convergence speed and final accuracy may be limited.67% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/936311API
curl https://kongchang.com/api/v1/knowledge/claims/936311MCP
get_claim(id=936311)