Unverified50% confidenceTradeoffExact time
Long-context inference becoming a memory bottleneck is a key factor that must be weighed when deploying large models.
1
Sources
50%
Confidence
Long-term
Relevance
9/15/2026
First Seen
Sources
Related Entities
Related Claims
Unverified级联架构面临的核心挑战是误差传播,上游模型的偏差会被放大传递到下游75% similarUnverifiedAs model capabilities improve, too many constraints and prompts waste the context budget and cause prompt drift, and reducing constraints actually works better.75% similarUnverifiedCurrent large models still suffer from insufficient stability in long-chain, multi-step tasks, and the complexity of real trading environments places higher demands on model robustness.75% similarUnverifiedCurrent large models encounter issues such as goal drift and context forgetting when required to maintain coherent survival strategies for consecutive months.75% similarUnverifiedThe core bottleneck in current multimodal large models has shifted from the perception gap to the reference gap.74% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/923486API
curl https://kongchang.com/api/v1/knowledge/claims/923486MCP
get_claim(id=923486)