Unverified50% confidenceFactExact time
Traditional large language models often fail at complex tasks spanning multiple steps and multiple time points due to context loss, errors in intermediate steps, or goal drift.
1
Sources
50%
Confidence
Long-term
Relevance
9/29/2026
First Seen
Sources
Related Entities
Related Claims
Unverified低延迟与高质量推理往往难以兼得，前后台分离架构通过任务分级路由解决这一矛盾76% similarUnverifiedAt equivalent parameter scales, diffusion language models typically still lag behind mature autoregressive models in perplexity and downstream task performance.76% similarUnverifiedLarge language models are prone to hallucination drift when handling detail consistency problems that require precise long-range context tracking.75% similarUnverified传统ReAct模式在复杂场景下存在瓶颈，长链路推理中的误差累积效应会导致任务失败率上升75% similarUnverifiedMost frontier models exhibit extremely high variance when running the same task repeatedly, potentially producing drastically different results.73% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/959913API
curl https://kongchang.com/api/v1/knowledge/claims/959913MCP
get_claim(id=959913)