Unverified50% confidenceFactExact time
Context compression causes previously injected system prompts and custom skill instructions to be diluted or even lost, leading to model behavior drift that requires reloading.
1
Sources
50%
Confidence
Long-term
Relevance
9/16/2026
First Seen
Sources
Related Entities
Related Claims
UnverifiedOperations such as switching models, switching inference intensity, toggling fast inference mode, compressing conversations, and resuming after cache expiration all cause prompt cache invalidation.79% similarUnverifiedIn long contexts, models are more easily influenced by the most recent few turns of conversation rather than the system prompt set at the beginning, a phenomenon known as instruction-following decay.76% similarUnverifiedModel 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.75% similarUnverifiedThe description of every Skill permanently occupies limited context window space, and overly long descriptions crowd out the space the agent needs to understand code and generate replies, actually degrading output quality.75% similarVerifiedOverly verbose, contradictory, or ambiguous system prompts cause the model's attention to become scattered, resulting in over-thinking or instruction-following failures; streamlining agents.md can improve the determinism of instruction following.75% similar
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