Unverified50% confidenceOpinionExact time
Understanding system runtime behavior and anticipating how changes perform under load are weak points of current large model coding capabilities.
1
Sources
50%
Confidence
Medium-term (~90 days)
Relevance
9/23/2026
First Seen
Valid until: 12/22/2026
Sources
Related Entities
Related Claims
UnverifiedModel compression techniques (distillation, pruning, quantization) incur minimal loss on high-frequency, regular tasks, but significantly degrade multi-step reasoning that requires long-range dependencies—a capability that cross-component state management in front-end interaction code heavily relies on.76% similarUnverifiedWhen a model exceeds VRAM capacity, the system is forced to repeatedly transfer data between VRAM and system memory, causing catastrophic performance degradation.76% similarUnverifiedProgramming scenarios place extremely high demands on model capability; the performance gap between weak and strong models in code generation is several orders of magnitude — a weak model may fail to solve the same coding task after dozens of attempts, while a strong model passes compilation on the first run.75% similarUnverifiedUnderstanding assembly remains a highly challenging task for LLMs, and models may confidently produce incorrect explanations, especially when hardware timing, self-modifying code, or optimization tricks are involved.74% similarUnverified研究表明LLM在处理过长、过复杂的指令集时整体遵循率会系统性下降，即指令遗忘现象73% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/944538API
curl https://kongchang.com/api/v1/knowledge/claims/944538MCP
get_claim(id=944538)